Sensors 2013 , 13, 2295-2348 doi:10.3390s130202295 [601359]

Sensors 2013 , 13, 2295-2348; doi:10.3390/s130202295

sensors
ISSN 1424-8220
www.mdpi.com/journal/sensors
Review
Diverse Applications of Electronic-Nose Technologies in
Agriculture and Forestry
Alphus D. Wilson
USDA Forest Service, Southern Research Sta tion, Center for Bottomland Hardwoods Research,
Southern Hardwoods Laboratory, P.O. Box 227, Stoneville, MS 38776, USA;
E-Mail: [anonimizat]; Tel. : +1-662-686-3180; Fax: +1-662-686-3195
Received: 1 December 2012; in revised form: 30 January 2013 / Accepted: 30 January 2013 /
Published: 8 February 2013

Abstract: Electronic-nose (e-nose) instruments, derived from numerous types of
aroma-sensor technologies, have been developed for a diversity of app lications in the broad
fields of agriculture and forestry. Recent adva nces in e-nose technol ogies within the plant
sciences, including improvements in gas-sensor designs, innovations in data analysis and
pattern-recognition algorithms, a nd progress in material science and systems integration
methods, have led to significant benefits to both industries. Electr onic noses have been
used in a variety of commercia l agricultural-related industrie s, including the agricultural
sectors of agronomy, biochemical processing, bo tany, cell culture, plant cultivar selections,
environmental monitoring, horticulture, pesticide detection, plant physiology and
pathology. Applications in forestry include uses in chemotaxonomy, log tracking, wood
and paper processing, forest management, fore st health protection, and waste management.
These aroma-detection applications have impr oved plant-based product attributes, quality,
uniformity, and consistency in ways that have increased the efficiency and effectiveness of
production and manufacturing processes. This paper provides a comprehensive review and
summary of a broad range of electronic-nose technologies and applications, developed
specifically for the agriculture and forestry indu stries over the past thirty years, which have
offered solutions that have greatly improve d worldwide agricultural and agroforestry
production systems.
Keywords: artificial olfaction; electronic aroma detection; volatile organic compounds
OPEN ACCESS

Sensors 2013 , 13 2296

1. Introduction
A wide variety of sensor technol ogies are utilized in modern ag riculture and forestry to obtain
accurate information on crop, soil, weather, and e nvironmental conditions. Sens ing tools are used in
these industries for a multitude of ap plications in the manufacturing of agricultural and forest products,
particularly for quality control and monitoring industrial processe s. Agricultural and forestry
management methods strongly rely on a spectrum of sensor technologies rangi ng from aerial remote
sensing, portable field weather stations, greenhouse environmental sensors, electrochemical sensors,
electronic noses, biosensors, and so phisticated wireless sensor netw orks [1]. Electronic-nose devices
are being used with increasing frequency because th ey allow the acquisition of real-time information
about the chemical and physical nature and qualit y of plants, plant and animal products, and gas
effluents released from agricultural and fore stry products throughout th e entire food and fiber
production cycle. The continuous-monitoring capabi lity of e-nose devices provides a means of
assuring that production methods and outcomes meet quality specifications (standards) and demands
required by regulatory agencies and the consumer for ultimate salability in commercial markets.
The invention of diverse electronic nose (e-nose) sensor types and instruments, based on different
electronic aroma detection (EAD) principles and m echanisms, has led to the development of e-nose
applications for diverse disciplines within the plant sciences [2]. Gas sensing- applications utilizing
e-nose devices in agriculture and fo restry are naturally divided into two major groups, including those
developed for commercial and industrial applica tions of products derived from: (1) small nonwoody
(herbaceous) plants, used as ag ronomic crop (food) plants, and animals within the agricultural
industry, and from (2) larger woody plants used as or namentals, landscape structure, fiber, or wood
production within the forestry industry. Thus, th e agriculture and forestry industries handle the
majority of plant and plant-derive d products that originate from w ild and domesticated plant species
throughout the world. The industrial sectors comprisi ng each of these two pl ant product-associated
industries are vast due to the larg e number of plant species and produ ct types that are exploited by
world commerce. Animal-derived products in agricu ltural are primarily derived from the commercial
meat-producing industries includ ing livestock, fish, poultry, and various milk-derived products.
Plants, as a taxonomic group, collectively synthesi ze a very large range of organic (carbon-based)
compounds that are categorized into many different chemical classes. These diverse organic chemicals
are produced as a result of biochemi cal or metabolic processes that ta ke place within specialized cells
of many different types of differentiated plant tissues in the root , stems, and leaves. Leaf tissues are
particularly rich in diverse orga nic compounds as a consequence of being the chief organ that captures
solar energy in the form of radiation and stores th at energy as chemical energy, required for all cellular
processes and biosynthetic pathways that produce the myriad of or ganic compounds present within
plants. Some chemical monomeric compounds are linked together to form various types of structural
or functional biopolymers such as carbohydrates, lipid s, proteins, and nucleic acids. These polymeric
compounds generally have low vola tility as a result of their high molecular weight. Other smaller
intermediates of biochemical proc esses are modified to form a va riety of primary and secondary
metabolites performing many cellular or biochemical functions. Relatively sm all molecular weight
organic compounds, generally <350 Daltons [3], may contain vari ous polar and nonpolar functional
groups that contribute to volatility . Compounds having high vapor pressure (low boiling point), called

Sensors 2013 , 13 2297

volatile organic compounds (VOCs), are particularly conduc ive to e-nose detect ion because they are
easily vaporized (made airborne as gases), greatly increasing their accessibili ty for detection within
sampled air.
The detection of plant- or animal-derived VOCs using electronic-nose devices usually is performed
on simple to complex mixtures of volatilized organic compounds derived from living tissues or from
nonliving processed products derived from plant or animal cells. The most common purpose of such
analyses with e-nose instruments is to identify th e source (plant, animal, or derived product) that
produced the unique mixture of orga nic compounds present in the samp le analyte, not the individual
compounds present in the sample mixture. A se cond common purpose for performing e-nose analyses
is to assess one or more chemical , biological or physical characteristi cs about the sour ce that released
the sample analytes. Characterizing the source of a sample may be done for the specific purposes of
determining product consistency, quality, purity, age, or state of merchantability. For example, e-noses
are used to evaluate fruit freshness, ripeness, a nd shelf-life. For commercial wines, the bouche from
different bottles of a wine batch or vintage may be analyzed for uniformity, fruitiness, aroma, age, and
other characteristics that determin e quality, merchantability, and appropr iate price in the market place.
The agriculture and forestry indus tries have become highly depende nt upon electronic-nose devices
because of the capability of these instruments to r ecognize the presence of spec ific gas mixtures that
are produced or released during or as a consequence (byproduct) of various manufacturing processes.
The aroma characteristics of ag ricultural products, pa rticularly in the f ood industry, contribute
immensely to product value and a ppeal to consumers and thus of ten determine the salability of
manufactured goods. For these reasons, quality c ontrol (QC) of the aroma characteristics of
manufactured products is of pa ramount importance because product consistency is essential for
maintaining consumer brand recognition and sa tisfaction [4]. Other common QC manufacturing
applications of e-noses are in product grading, uniformity, mech anical processing controls, and
monitoring environmental effluents rel eased from manufact uring processes.
The purpose of this review is to provide a t horough overview of the diversity of uses for
electronic-nose technologies within the wide spectrum of applications in the ag ricultural and forestry
sectors and to provide numerous examples demonstr ating the many ways in wh ich e-nose devices have
improved the quality and efficiency of food and fi ber production processes within these industries.
2. The Nature of Electronic-Nose Devi ces and Target Chemicals Detected
Electronic-nose devices are different from most other instruments used in chemical analyses in that
they are mainly designed to recognize gas mixtures as a whole without iden tifying individual chemical
species within the mixture. For th is reason, e-noses generally are not primarily utilized to determine
the entire composition of complex gas mixtures, but ra ther are most useful fo r determining the sources
(from which gas mixtures were derived), the identity of specific gases present, and associated
physicochemical characteristics. The sources of gas analytes may be e ither natural or synthetic organic
sources that produce VOCs or inorganic gas sources releasing various type s of volatile inorganic
compounds (VICs) as gases. In fact, e-noses are commonly used to detect both natural and manmade
organic and inorganic pollutants in the environment [5]. All of these categories of volatile gases are
produced in association with many different agri cultural and forest-produ ct industrial sectors.

S

a
S
a
th
o
inSensors 201
The type s
and nature o
Some majo r
Table 1 .
Volatile c h
Bioc h
Food p
Fl
F
Mic
Pest
Plant h
Secondar y
Vege
W
W
A large
association w
hese man u
offensive, r e
nto air soi l3, 13
s and mixt u
of the prod u
r categories
. Major typ e
hemical typ e
hemical
products
loral
ruit
crobial
ticides
hormones
y metabolite s
etative
Waste
Wood
diversity o f
with plant h
ufacturing w
equiring th e
l, and wat eures of VO C
ucts being m
and comm o
es of VOCs
es Exam p
py
meth y
2-ph
ac
gl
e
s c
hexe
dime t
α
f agricultu r
harvesting, p
waste bypr
e use of e-n o
er resource sC gases det e
manufactur e
on sources o
in gas mix t
ple compou n
yruvic acid
citrinin
yl propionat e
henylethanol
cetic acid
lyphosate
ethylene
caffeine
enyl acetate
thyl disulfid e
α-pinene
ral and for e
product m a
oducts are
ose type se n
s from ind uectable by e –
ed in indivi d
of VOCs d e
tures detec t
nd C
e
e
est-product s
anufacturin g
either ha z
nsors to co n
ustrial pro c
-nose instr u
dual agricu l
etected by e
ted with e- n
Chemical st r
s industry w
g, and asso c
zardous to
ntinually m o
cesses [5]. S
uments dep e
ltural and f o
-noses are p
noses in agr i
ructure

waste byp r
ciated indus t
human h e
onitor efflu e
Some offe n
end on the s
orest-prod u
presented i n
icultu re and
Commo n
Cellula r
Myc
conta
Flowe r
Wine
Ferment a
Her
Fruit –
hor
Plant
Leaf
Paper b
Woo d
roducts are
trial proces
ealth or ar
ent levels b e
nsive agric u229
sources, us e
uct industri e
n Table 1.
d forestry.
n source/us e
r metabolite
cotoxin
aminan t
r fragrance
e volatile
ation produc t
rbicide
-ripening
rmone
alkaloid
fvolatile
byproduc t
d volatile
produced i
ses. Many o
e olfactori l
eing releas e
ultural was t98
es,
es.
e
t
in
of
ly
ed
te

Sensors 2013 , 13 2299

effluents monitored by e-nose devices are listed, al ong with human olfactory detection and recognition
thresholds, in Table 2. Many of these compounds also are produced as a result of microbial or
chemical degradation of raw or pr ocessed agricultural or forest-produc ts, before or after harvesting,
during the manufacturing process, or in storage before or after processing.
Table 2. Offensive agricultural byproducts with threshol d levels for human dete ction and recognition.
Chemical odorant Formula Characteristic odor Detection † Recognition †
Acetaldehyde CH 3CHO Pungent, fruity 2.1 × 10−1
Allyl mercaptan CH 2CHCH 2SH Strong garlic, coffee 1.6 × 10−2
Ammonia NH 3 Sharp, pungent 4.7 × 101
Amyl mercaptan CH 3(CH 2)4SH Putrid
Benzyl mercaptan C 6H5CH 2SH Strong
Butylamine C 2H5(CH 2)2NH 2 Ammonia-like, sour 2.4 × 10−1
Cadaverine H 2N(CH 2)5NH 2 Putrid, decaying flesh
Chlorophenol ClC 6H5O Phenolic, medical
Crotyl mercaptan CH 3CH=CHCH 2SH Skunk-like 7.7 × 10−3
Dibutylamine (C 4H9)2NH Fishy
Disopropylamine (C 3H7)2NH Fishy 8.5 × 10−2
Dimethyamine (CH 3)2NH Putrid, fishy 4.7 × 10−2
Dimethylsulfide (CH 3)2S Decayed vegetables 1.0 × 10−3
Diphenylsulfide (C 6H5)2S Unpleasant 2.1 × 10−3
Ethylamine C 2H5NH 2 Ammonia-like 8.3 × 10−1
Ethyl mercaptan C 2H5SH Decayed cabbage 2.6 × 10−3 1.0 × 10−3
Hydrogen sulfide H 2S Rotten eggs 4.7 × 10−3
Indole C 2H6NH Nauseating, fecal
Methylamine CH 3NH 2 Putrid, fishy 2.1 × 10−2
Methyl mercaptan CH 3SH Decayed cabbage 2.1 × 10−3
Propyl mercaptan CH 3(CH 2)2SH Unpleasant 2.4 × 10−2
Putrescine NH 2(CH 2)4NH 2 Putrid, nauseating
Pyridine C 6H5N Disagreeable, irritating
Skatole C 9H9N Nauseating, fecal 2.2 × 10−1 4.7 × 10−1
Sulfur dioxide SO 2 Pungent, irritating
Tert-butyl mercaptan (CH 3)3CSH Unpleasant, skunk
Thiocresol CH 3C6H4SH Rancid, skunk 1.4 × 10−2
Thiophenol C 6H5SH Putrid, garlic-like 1.4 × 10−2 2.8 × 10−1
Triethylamine C 2H5OH Ammonia-like, fishy
† Human thresholds for detection and recognition of odorant gases ar e measured in parts per million
(ppm) in dry air at standard temperature and pressure (STP).
Generally, the concentration levels required for human olfactory detection are significantly lower
than the concentrations required for recognition. De tection of these compounds released from tainted
products usually indicates that these commercial products have undergone mi crobial degradation to
produce staling metabolic products and therefore must be culled because they no longer have
merchantable value. Thus, e-nose sensors in this cas e serve to maintain quality control of agricultural
products for human safety and to preserve or a void contamination of other perishable goods or
products that may be in close proxim ity or contact with spoiled products.

Sensors 2013 , 13 2300

There are two major sources of VOCs, emitted into the atmosphere as a result of agricultural and
forestry-product industrial processes, that are detectable with e-nose devices. Biologically-generated
VOCs account for the majority of carbon released in the form of VOCs by plants and animals in
agricultural crop fields, grazing lands, natural forests and plantations or tree farms. The major sources
of biologically-generated VOCs include methane fr om livestock, wetlands, and agricultural fields
(about 340 teragrams of carbon per year); and also isoprene (C 5H8) and isoprenoid or terpenoid
(C5H8)n-compounds released from plants (mostly from l eaves), accounting for an estimated total of
1,150 teragrams of carbon per year in the form of VOCs [6]. Anthropogenic sources, derived from
harvesting and manufacturing activities from various industries, account for the remainder of VOCs
emissions, totaling about 140 teragrams of carbon releas ed per year in the form of VOCs such as
hydrocarbon solvents, fuels, cleaning products, refrig erants, pesticides, and ga seous or volatile liquid
industrial byproducts (wastes) [6].
Volatile inorganic compounds (VICs) also are a significant pollution-emission problem arising from
industrial activities relate d to agriculture and forestry producti on systems such as the industrial
production of pesticides, fertilizers, and other chemicals needed in ag roforestry production. Similarly,
VICs may be detected by a range of different e-nos e devices that are commonl y used in the detection,
monitoring, and control of environm ental pollution because VICs are common chemical pollutants [5].
Some of the more common VIC pollutants released as gas effluents from agroforestry production
systems include CO, CO 2, NH 3, NO 2, NO x, H2S, SO 2, as well as heavy metals (e.g., arsenic, cadmium,
lead, mercury, and zinc) released into agricultural systems via fertilizers, organic wastes such as
manures, and in industrial waste byproducts.
2.1. Electronic Nose Types and Characteristics
The diversity of EAD technologies u tilized in electronic-nose device s include a variety of different
sensor types that operate based on different gas-sensing principles, ranging from bulk acoustic wave
(BAW), calorimetric or ca talytic bead (CB), carbon black composite (CBC), cata lytic field-effect (CFET),
conducting polymers (CP), complementary metal oxide semiconductor (CMOS), electrochemical
(EC), fluorescence (FL), metal oxide semiconductor (MOS), Metal oxide semiconductor field effect
transistor (MOSFET), micro-electromechanical sy stems (MEMS), quartz crystal microbalance (QCM),
optical fiber live cell (OF-LC), and surface acoustic wave (SAW) gas sensors. Some advantages and
disadvantages of these various e-nos e sensor types have been summari zed previously [4], although the
utility of individual sensors largely depends on the particular application, e nvironmental conditions,
and types of gas analytes to be detected.
A complete electronic-nose system typically consists of several integrated and/or interfaced
components including a multisensor array (composed of several to many gas sensors with broad
sensitivity and cross-reactivity or partially-overlapping selectivity), a data-processing and analysis unit
such as an artificial neural network (ANN), softwa re having digital pattern -recognition algorithms, and
often aroma reference-library databa ses containing stored f iles with digital fingerprints of specific
aroma reference (signature) patter ns [2,4]. Broad spectrum cross-re active sensor arrays usually are
composed of incrementally-different sensors chosen to respond to a wide ra nge of chemical classes
and capable of discriminating diverse mixtures of possible analytes that may be detected. Narrow-

Sensors 2013 , 13 2301

spectrum sensor arrays are designed for applicatio n-specific e-noses to detect a limited range of
analytes from specific chemical classes known to be the only analytes of interest for detection. The
electronic outputs, derived from all responses of the individual se nsors in the sensor array, are
converted into digital values by a transducer and assembled together to produce a dis tinct electronic
aroma signature pattern (EASP) that is determined by the collective sensor-a rray responses to the
entire mixture of VOC or VIC gas analytes present in the sample being analyzed. Identification and
classification of the analyte mixture is acco mplished through recognition of this unique aroma
signature (electronic fingerprint) from comparisons with the referenc e databases in a library of known
EASPs—much like similar libraries used in gas chromatography-mass spectroscopy (GC-MS)
analyses. The reference library of aroma signature patterns for known samples is constructed prior to
analysis of unknowns and is used to form the rec ognition files used by pattern-recognition algorithms
to arrive at a percentage match value with k nown patterns in the library. Sensory output patterns
derived from analytes that do not match any patterns of known gas mixtures to a significant level
(>90%) are determined to be unidentified or unknow n. Therefore, false-positive determinations are
usually rare when analyte samples are from a known sample type (source), fully represented (variation
accounted for) in the reference libra ry, and confidence-level controls are set appropria tely to make
effective discriminations.
2.2. Considerations of E-Nose Designs for Specific Applications
The suitability of an electronic nose for a specif ic application is highly dependent on the required
operating conditions (environment) of the sensors in the array and the composition of the target analyte
gases being detected. A proper selec tion of an appropriate e-nose syst em for a particular application
must involve an evaluation of systems on a case-by-ca se basis. Some key considerations involved in
e-nose selection for a particular application must necessarily include assessments of the selectivity
and sensitivity range of individual sensor arrays for particular target analyte gases (likely present in
samples to be analyzed), the number of unnecessary (r edundancy) sensors with similar sensitivities, as
well as sensor accuracy, reproducibi lity (preciseness), response speed, recovery rate, robustness, and
overall performance.
The effective design of electronic-nose devices for ag ricultural and forestry applications depends on
several factors including the specif ic gas-sensing application(s) to be employed, the range of target
analyte chemicals to be detected, the required op erating conditions (environm ent) of the instrument,
the selectivity and sensitivity ranges for detectio n required, and various operational requirements such
as run speed and cycling time between samples, se nsor array recovery time , data analysis and
result-interpretation requirements [4]. In the r ecent history of e-nose sensor design, it has become
apparent to some design engineers that there are many advantages to designing electroni c-nose devices
based upon the specific applic ation(s) for which the instrument will be applied, instead of basing the
design on a more gene ralized goal of producing a versatile inst rument with a broad -range of gas-sensing
capabilities and applications. Logica lly, it would appear to e-nose ma nufacturers that a more general
e-nose device would have wider appl ications and could be sold to cl ients in many different industries.
In reality, the needs and sp ecification requirements of individual i ndustries are so vastly different and
specific that a generalist-type instrument is of ten unusable due to the inflexibility of operating

Sensors 2013 , 13 2302

parameters, detection limits, and sensing capabilities and requirements ( i.e., specific types and range of
analytes that must be detectable with the instrume nt). From these experiences, it has become apparent
that application-specific e-noses serve individual customers or i ndustries to greater levels of
satisfaction because such instruments do a better job of detecting the specific analytes required and can
be designed to produce results (instr ument outputs) in customized format s that are most useful for data
analysis and use by specific narrow industries. T hus, narrow-spectrum sensor arrays designed for
application-specific e-noses often are considerably cheaper because the number of sensors required in
the array for effective discriminations is significantly reduced.
Sensor array selectivity for specific target VOCs is a major factor for consideration in designing
e-nose devices or in selecting specif ic sensor types to include in the array for a particular gas-sensing
task. For example, MOS sensors are particularly us eful for monitoring VOCs due to such advantages
as low cost, rapid sensor response and recove ry times, and ease of e- nose manufacture [7–11].
However, certain MOS sensors are not widely used for interior environmental-monitoring applications,
such as monitoring indoor air quality in buildi ngs, because they are often limited by the lack of
selectivity towards VOCs from similar chemical cl asses. This difficulty in distinguishing between
related VOC species results from similar elementa l composition (primarily carbon and hydrogen) in
molecular structure. Thus, pollutants consisting of such VOCs as benzene, formaldehyde, toluene and
xylene that cause indoor enviro nmental illnesses (building-relate d sicknesses) often cannot be
distinguished without improving sensor selectivity to discriminate between structurally-similar
VOCs [12]. Wen and Tian-mo [13] proposed the use of a mixed-oxi de MOS sensor consisting of
SnO 2-TiO2 doped with silver (Ag) ions to improve se lectivity for VOC detection. They found this
mixed-oxide sensor exhibited differential selectiv ity to different VOCs whic h varied at different
operating temperatures. Furthermore, quantum chem istry calculations showed that differences in
orbital energy of structurally-di fferent VOC molecules may be a qua litative factor that affects the
selectivity of mixed- oxide MOS sensors.
Sensor selection for individual e-nose systems is of paramount importance in order to achieve
effective and efficient aroma iden tifications or classifications. A fundamental design concept for an
array of sensors used in electronic noses is th at each sensor should maximize overall instrument
sensitivity and provide different sele ctivity profiles over the range of ta rget-gas analytes to be detected
or classified for a particular ap plication [14]. Id eally, a sensor array should consist of individual
sensors that produce a different re sponse to a given odor analyte so that a unique aroma pattern is
created. If there is difficulty in obtaining unique aroma patterns for different gas analytes, sensor
selection must be modified or the number of sens ors adjusted when classifi cation, performance, cost,
or technological limitations are issues of concern.
The first step in sensor selection and adjustments w ithin the sensor array is to analyze the sensor’s
output and performance to a range of target gas analyt es to be detected and determine whether there is
any redundancy (cross-sensitivity) or irrelevancy (lack of sensitivity) of indivi dual sensors that reduces
the effectiveness of analyte discriminations [14]. In appropriate sensor selecti on or a poor sensor array
configuration can result in the deterioration of e-nose performance. One major advantage of e-nose
devices is the large number of sensor types that are available for inclus ion in a sensor array of different
e-nose types and for different gas-se nsing applications. Large libraries of sensor types are available for
selection in many cases to facilitate the custom design of an e-nose for de tecting specific target

Sensors 2013 , 13 2303

analytes [4]. The development of mobile portable e-nose devices usua lly involves a reduction in sensor
number (relative to larger bench- top laboratory instrument versions ) and more precise selection of
specific sensor types in the array to optimize perfo rmance for specific applications and minimize size
and costs.
Electronic nose sensor designs frequently are in spired by biological olfactory systems that are
analyzed and modeled, serving as a basis for designi ng e-noses by mimicking the functionality of these
natural systems to produce so-called biologically-i nspired (biomimetic) e-nose devices. In reality,
e-nose instruments neither truly mimic the mechan ical structure nor func tionality of biological
olfactory systems due to their complexity and huge sensor diversity, e.g., more than 300 human
olfactory binding proteins (OBP) ha ve been identified in the human olfactory system. Nevertheless,
Che Harun et al. [15] have developed an improved concept for an electronic nose that combines three
large chemosensor arrays (300 resistive elements pe r array) with two micro- packages, each containing
a column inspired by the study of the human olfact ory mucosa and nasal cavity, that significantly
enhances the ability of the e-nos e to discriminate complex odors. Further studies of biological
olfactory receptors (ORs), consisting of a large fam ily of G-protein coupled receptor proteins (GPCRs)
responsible for sensing the ambient chemical envi ronment [16,17], will no doubt result in future e-nose
sensor designs that take into account the 3-dimensi onal structural confirmation of odorant molecules to
produce e-nose devices with greater discrimination capabilities than is currently achieved based only
on the electronic effects of odorants as they adsorb to the surface of contemporary e-nose sensors.
The relationship between the properties of odor ant molecules (structu ral conformation and
composition) and the resulting odors or aromas rec ognized by biological olf actory systems provides a
means of measuring or quantifying odors and pl acing them into categories based on measured
likenesses or differences in olfactory characteristi cs. Likewise, attempts to quantify aroma properties
of different classes of VOCs usi ng sensory outputs from electronic noses have provided ways of
categorizing aromas using various electronic metrics. This process generally is accomplished using
data-manipulation algorithms, su ch as artificial neural network (ANN) systems, that look for
differences between aromas based on selected measurable parameters.
Odorant molecular recognition in biological systems involves bi nding of odorant molecules to
olfactory-receptor sites with either attractive or repulsive (electrostatic ) chemical interactions that can
be associated with the presence of odotopes (expos ed charges of specific shapes, types and numbers
resulting from fragments of molecular shape [18] ) present on odorant molecules. These electrostatic
interactions can occur between fixed ch arges, dipoles, induced dipoles or atoms able to form weak electron
bonds (e.g., hydroge n bonds); and include repulsive interactions (electrostatic or quantum-mechanical
electron-shell exchange repulsion) as well as attract ive forces between odorants and receptors. Every
possible change in molecular structure of odorants alte rs the set of exposed surf ace features (odotopes)
capable of forming such attractive or repulsive interactions, and thus is affected by molecular shape
and charge distribution.
Odotope theory suggests that the smell of a molecule is due to the pattern of excitation that results
from the interaction of exposed atoms or functiona l groups in odorant molecule s to specific types and
numbers of excitable sensory receptors to which they bind [19]. This theory acc ounts for the sensing of
a considerable number of possible smells based on the many permutations of interactions between
odorant odotopes and different types of sensory-receptor binding sites. Even if one assumes that sensor

Sensors 2013 , 13 2304

receptors are only on or off (binary), this scheme pot entially accounts for considerable combinations of
possible sensory input to discri minate odor types depending on the number of atoms, odotopes and
receptor types involved in these interactions. Comb ining multiple odotopes of odorant molecules with
possible variable intensity of excitation for each receptor would enable such as a system to detect and discriminate a vast number of possible odorants. If th e large number of odorant receptor types (binding
sites) represent sensory analogs of odotope categories, then the possibi lities for sensor y discrimination
of different VOCs becomes astronomical [18].
Good empirical evidence to support the odotope theo ry is the ability of humans to detect the
presence of functional groups with excellent reliability. Examples incl ude the case of thiols (–SH) that
impart the familiar sulphur sm ell to compounds, nitriles (–C ≡N) that yield a meta llic character to
any smell, isonitriles (–N ≡C) with an unpleasant, flat meta llic smell, oximes (–C=NOH) with a
green-camphoraceous odor, nitro groups (–NO
2) with a sweet-ethereal character, and low molecular
weight aldehydes (–C=O(H)) with a rotten-fruit smell [18,20]. Huma ns can, in some cases, even
recognize the presence of specific bond types between atoms in an odorant. The acetylenic triple bond
between carbon atoms (–C ≡C–) in alkyne hydrocarbons imparts a mu stard-like smell to molecules [18].
However, exceptions do exist such as compounds having very similar chemical st ructure but dramatically
different odors, and compounds with completely different structures ha ving similar odors [21].
Apparently, other unknown factors are involved in odorant characterizatio n and recognition by the
human brain based on sensory input derived from odor ant-sensor (olfacto ry receptor protei n) interactions.
Odorant molecules generally must be volatile, h ydrophobic, and have a molecu lar weight less than
300 Daltons to be detectable by ol factory systems. The size requireme nt appears to be a biological
constraint related to sensory-receptor size-response limitations. Vapor pressure (volatility) falls rapidly with molecular size, but does not explain why larger molecules have no smell given that some of the
strongest odorants (e.g., some steroids) are large mo lecules. A further indication that the size limit of
odorants is related to the chemoreception mechanism is that specific anosmia (the inability to smell a
particular substance) becomes mo re frequent as odorant molecular size increases [18]. Thus, human
subjects become increasingly anosmic to large nu mbers of VOCs as molecular weight increases.
The relationship between aroma quality and odorant molecular properties is harder to quantify in
biological systems than with electronic gas sensors due to variability in sensitivities of individuals to
specific classes of odorants and individual differen ces in subjective judgments of how odorants are
described or classified [22]. Nevertheless, the measurement of odors from agricultural production
areas, industrial faciliti es, or from municipal solid waste (MSW) landfills is usually a legal
requirement for Environmental Protection Agency (EPA) compliance monito ring, planning, site
expansion and review of operatio nal practices. Thus, specific methods and practices have been
developed for subjective quantification of odors from MSW landfills by regulators, operators and the
community for purposes of monito ring, planning and test ing [23]. By comparis on, individual sensors
in the sensor array of e-nose devices can be designed and selected for sensitivity to specific classes of VOCs or VICs based on the chemical nature of odoran ts such as the types and numbers of chemical
functional groups or elements present in odorant mol ecules. The presence of specific functional groups
in analyte gases and the carbon-chain length (mol ecular weight or size) of aliphatic VOCs from
different chemical classes is correlated with odor de tection threshold (ODT), but not in rigid-molecule
(e.g., cyclic planar and aromatic compounds) [24] . Electronic-nose odor-monitoring systems offer

Sensors 2013 , 13 2305

several advantages over human detection. E-nose de vices are more sensitive to gas analytes (have
much lower ODTs), offer greater potential discrimina tion of individual gases present (especially when
several different analyte-specific e-noses are used simultaneously), and are not subject to operator
fatigue as are human monitors.
An important final consideration fo r designs of e-nose systems for part icular agricultural, industrial,
and forestry applications is the incidence and fr equency of false classifications that occur in
association with different gas analyte types and what error rates are acceptable in e-nose
discriminations. Random noise in e-nose outputs from th e sensor array is one po tential source of false
classifications. Goodner et al . [25] found noise-based false clas sifications could be minimized by
increasing samples sizes, using a minimum number of variables (features) when developing
classification models to avoid over-fitting data, making su re the ratio of data points to variables is at
least six to prevent over-fitting classification errors, and using di fferent data points (for model
validation) other than those used in generating the model. Various algorithms also have been employed
to select variables and build pred ictive data-regression models to improve odorant discriminations and
model-validation methods [5,26–28].
False-positive determinations of the presence of specific gas analytes can be as serious as
false-negative determinations. The failure to detect toxic gases that may be present in the environment
can lead to human fatalities and de aths of farm animals. False-posit ive indications can result in the
implementation of unnecessary pollu tion control measures or expens ive adjustments in industrial
processing controls leading to si gnificant economic losses. Thus, sel ection of the proper sensor array
(matched to the specific gas analytes to be detect ed) and periodic calibrati on of e-nose monitors is
necessary to maintain effective and accurate monitoring of output data from e-nose devices.
3. Roles of Electronic-Noses in Mo dern Agricultural Development
Electronic-nose devices are utilized in a wide range of agricultural industries to perform a multitude
of functions ranging from quality-control monitoring of agricultural and forestry products, monitoring
industrial-process controls, food pr oduction and storage systems, indoor air-quality cont rol, detection
of environmental hazards, gaseous and liquid efflue nts and other factory waste releases. The most
common applications of electroni c noses in agriculture are to monitor food quality and production
processes, detect crop diseases, a nd identify insect infestations [1]. Some less common uses for e-nose
devices include the detection of explosive gases [29], determining the niche-roles of organisms in
forested agro-ecosystems [30], monitoring plant physio logical processes [31,32], and identifying plants
or for plant classifications via chemotaxonomy based on plant volatiles, includi ng essential oils [30].
Plants utilized in ag riculture and forestry release VOCs as a byproduct of normal physiological
processes. The specific VOCs produced and the quan tities released are indicative of both crop and
field conditions. Many factors including humidity, available moisture, light, temperature, soil
condition, fertilization, insects, and plant diseases may affect the release of VOCs from agricultural
plants. Thus, monitoring VOCs released from plan ts provide indications of plant health, growing
conditions, presence of environmen tal stresses, and the presence of adverse factors that may affect
plant growth, producti on and crop yields.

Sensors 2013 , 13 2306

Product and sample analyses with e-nose devices are accomplished by the detection of headspace
volatiles or gaseous VOCs in sampled air, released from organic and inor ganic chemical sources
associated with the various types of agro-produc tion systems. The followi ng sections provide more
specific details of e-nose uses involving specific a pplications in individual agricultural sectors.
3.1. Electronic-Nose Applications with in Specific Agricultural Sectors
Electronic-nose devices offer numer ous potential applications in agriculture including such diverse
uses as the detection of pesticide residue levels on crops or in the environment, industrial applications
including detection of ga s-leaks and toxic gas emissions, and for homeland security as an early
warning system for bioterrorism. Some of the most common applications of e- noses from a wide range
of agricultural sectors are listed in Table 3.
Agronomic uses of e-nose devices have included cr op-protection applications in the field to detect
hazardous chemicals and microbes (e.g., chemical or bi ological agents of bioterrorism) as well as
pesticides on plant foliage [2,33], making selections of plant cultivars of individual crop types for
cultivation [34,35], and to monitor plant cell culture s for growth and behavi or [36]. Related e-nose
applications are found in horticultu re involving similar tasks of asep tic plant tissue culturing in the
laboratory and cultivation of plant stocks in the greenhouse environment for commercial production of
ornamental (e.g., flowers, landscape shrubs) and food (crop) plants.
Electronic-noses have been utilized for several botanical applica tions involving th e detection and
monitoring of volatile biogenic gas emissions and flor al odors to determine season variations in plant
emissions [37,38], for identification of plant host-defense mechanisms , and for plant identifications
based on nonfloral volatiles [30]. D udareva and Pichersky [39] reviewed the potential of metabolic
engineering to modulate the volatile profiles of plants to enhance dire ct and indirect plant chemical
defenses and to improve scent and aroma quality of flowers and fruits. Advances in metabolic
engineering techniques ha ve provided a better unde rstanding of the biochemi cal pathways involved in
the biosynthesis of volatile seconda ry metabolite compounds, facilitating the identification of the plant
genes and enzymes involved as well as the chemical structures of a large number of new plant
volatiles [40–43]. Plants produce a la rge diversity of low molecular weight VOCs known as secondary
or specialized metabolites. At least 1% of these plant secondary metabolites (PSMs) are lipophilic
molecules (consisting primarily of terpenoids, phenyl propanoids/benzenoids, fatty acid and amino acid
derivatives) with low boiling points and high vapor pr essures at ambient temperatures. Plant secondary
metabolites are released from all pa rts of the plant (e.g., roots, stems, leaves, flowers and fruits) into
the atmosphere. The primary functions of PSMs are to defend plants against insect herbivores and
microbial pathogens, attract pollinator s, facilitate seed dispersers, pr omote the growth of beneficial
animals and microorganisms, and serve as chemical signals involved in plant- plant and plant-herbivore
interactions. Thus, PSMs are important volatiles that contribute to plant defenses as well as survival
and reproductive success in natural ecosystems. Production of PSMs by crop plants also has a
significant impact on agronomic and commercial plan t characteristics, crop yield and food quality.
Consequently, the modification of PSM-volatile production via genetic en gineering has the potential to
make crop plants less attractive to herbivore enemies a nd improve the traits of cu ltivated plant species.

Sensors 2013 , 13 2307

Table 3. Major categories of electroni c-nose applications within va rious agricultural sectors.
Agricultural sector Specific application areas References
Agronomy/Horticulture Crop protection [2,33]
Cultivar selection & discrimination [34,35,44]
Pesticide detection [33,45–48]
Plant cell culture [36]
Biotechnology processes Monitoring [49,50]
Botany Floral odors [37]
Plant identification [30] Plant volatiles detection [30,38,39]
Taxonomic determinations [30]
Cell culture Plant growth [36]
Chemistry Chemical detection & identification [51]
Classification [52,53]
Ecology Niche roles in ecosystem [30]
Plant and animal species identification [30]
Entomology Detect insects or induced plant volatiles [54–56]
Insect identification and plant damage [57–61]
Environmental hazards Ecosystem management [30]
Explosive vapors [62–64]
Health hazards monitoring [5,65–70] Toxic gas detection [71–77]
Water contamination detection [78–81]
Food production Chemical contaminants [82]
Microbial pathogens or toxins [83,84]
Forestry/Silviculture Classify/identify wood types [30,85]
Forest health protection [2,86] Forest management [30]
Industrial Processes Process monitoring control [87,88]
Formulation development [89] Quality control [90]
Microbiology Discrimination of strains [91–95]
Identification of microbes [96] Microbial growth phases [97]
Pathogen detection [98]
Toxin production [99]
Monitoring Enzyme and protein activity [100]
Humidity [101,102]
Immunoglobulin levels [103] Oxygen levels [104]
Plant volatiles [39]
Physiological conditions Disease effe cts on plant physiology [31,32]
Fruits [105]
Plant Pathology Crop protection against bioterrorism [2]
Disease detection and monitoring [2,106–112] Host identification [30,85]
Host physiology (pathogenesis effects) [31,32,105]
Host resistance [113] Pathogen identification [2,106]
Post-harvest decay or rot detection [114–118]
Wood decay fungi [2,86,96,119,120] Wood decay types [2,86]
Waste management Monitoring malodorous emissions [23,121–125]
Wood science Wood identifications [30,85,126]

Sensors 2013 , 13 2308

The utilization of metabolic engi neering technologies to modify PSM-volatile spectrums of plant
presents an enormous potential for plant improveme nt because of the great contribution of volatile
secondary metabolites to plant reproduction, defens e and food quality [39]. Electronic-noses offer
significant assistance to this effort by providing the capabilities to m onitor and identify the sources of
PSM-volatile mixtures released fr om specific plant species [30].
3.2. Electronic-Nose and Electronic-Tongue Applications in the Food Industry
The largest proportion of e-nose app lications within agriculture over the past twenty-five years has
been in the food-production industr y. There has been considerable in terest in the use of electronic
devices for the sensing of food aromas for several major applications in th e food industry. Electronic
noses are needed as objective, automated sampling systems to monitor food quality and characterize
the aromas of multiple food products simulaneously to determine whether the production system is
running to specifications—without re quiring human sensory panelists, lengthy analytical methods or
data interpretations [127]. In an automated food pr oduction system, electronic noses serve to rapidly
obtain quality-classification inform ation on food products to maintain product quality, uniformity, and
consistency based on aroma characteristics. Specifi c VOCs released from food constituents are
responsible for the characteristic aroma of food products. Other uses of e-noses in the food industry
include: quality assuran ce of raw and manufactured products, monitoring of c ooking processes,
fermentation processes, mixing, fl avoring, blending and product-packag ing interactions, determining
food freshness and aging in storage, evaluating the maturation and ripe ning of wine, cheese, and meat
products. The e-nose assessment of food freshness a nd spoilage during pro cessing, packaging, and
storage are particularly important for assuring that the fi nal products presented for human consumption
are of sufficient quality to be salable in commercial markets.
E-noses are used in the flavor and food industries for many of the same tasks employed in the
cosmetics and perfume industries. The differential volatilities of chemical species that compose the
complex aromas released from commercial food pr oducts are given major consideration in product
development. The food and beverage industries, like the perfume or scent industries, seek to manage
and manipulate product aromas for commercial or market-share advantages. Thus, the continuous
search for attractive or pleasing ar omas and flavors to enhance food products is a major preoccupation
in the food and beverage industries. The characteristics a nd qualities of complex aromas, composed of
a widely diverse mixture of volatile chemical c onstituents including VOCs that collectively produce
the unique olfaction sensation that defines a specific produc t, are key attributes receiving th e greatest
attention in product-development research [4].
Potentiometric electronic-tongue (e-tongue) instru ments for evaluating and quantifying the quality
of taste characteristics of food pr oducts are functionally analogous inst ruments to electronic-noses that
focus on the olfactory or aroma characteristics of f oods. Some diverse applica tions of electronic-nose
and e-tongue technologies in the food industry are liste d in Table 4. E-tongues have been applied to the
food and beverage industries in many of the same functions as e-noses, such as for food-taste
monitoring, classification, grading, qua lity assessments, and predictions of human taste-test results for
commercial food and beverage products. Hruskar et al . [128] utilized a pot entiometric e-tongue,
consisting of seven sensors and an Ag/AgCl reference electrode, to effectively monitor taste changes in

Sensors 2013 , 13 2309

probiotic fermented milk in storage, to classify probiotic fermented milk according to flavor, and to
predict sensory characteristics and their relationship to the quality of the fermented milk as measured
by human consumers.
Table 4. Diverse applications of elec tronic-nose and e-tongue techno logies in the food industry.
Food industry sector Specific application areas References
Aroma analysis Acidity [129]
Antioxidants [130–133]
Astringency or bitterness [134–138] Beer [139–143] Bioethanol [144] Chemical content analysis [145–149] Coffee [78,150–153] Flavor analysis (taste) [152,154–162] Fragrance or odor analysis [127,159,163–165] Fruit ripening or maturity [35,116,166–172] Fruit and floral volatiles [37,173,174] Fungal volatiles [175,176] General food analysis [177–183] Juice levels in beverages [184,185] Lipid, oils, or fat content [186] Meat [187,188]
Milk [189,190]
Plant or vegetable oils [191,192] Soft drinks (beverages) [185,193] Soybean [194] Spice mixture composition [195] Storage-condition effects [196,197] Taste analysis and consumer-choice tests [159,160,198–201] Tea [145] Wine [202–206]
Aroma classifications/
discrimination Alcohol and liqueur [207,208]
Apricots [209,210]
Baking breads [211] Bitterness of foods & beve rages [134,138, 139,212–215]
Carrots [216] Cheeses [217,218] Chickpeas [219] Citrus juices [220,221] Coffees [222–224] Edible oils [225–228] Floral [37] Food products [229] Grains [230]

Sensors 2013 , 13 2310

Table 4. Cont .
Food industry sector Specific application areas References
Herbs [34]
Honeys [231,232]
Liquids [139] Milk [161,233] Mineral water [234,235] Peaches [35,82] Pears [236] Rice [237] Seeds [238] Soybeans [44] Teas [145,239,240] Tomatoes [173] Volatile organic compounds (VOCs) [13,50–53,241] Wines [242–246]
Detection &
identification Artificial and natural sweeteners [247]
Food processing Control of processing parameters [87,88]
Aging of food products [248–252]
Geographical origin Cheeses [217]
Honeys [253] Olive oils [254]
Wines [255]
Teas [256]
Quality control Adulteration with cheaper components [192,257–260]
Contamination with microbes/pathogens [95,141,230,261–263] Coffee [224] Fish [264–268] Foods [269,270] Food storage methods [271] Fruits [105,272]
Quality control Fruit maturity [116,171]
Fruit decays or rot detection [114–116,273] Meats [274,275] Milk [276] Oxidation [191,277] Off-flavor and off-odor detection [278,279] Product grading and defect detection [16,279] Quality assessments and sorting [114,115,196,280,281] Shelf life before spoilage [128,282–293] Storage age or food freshness [13,147,174,290,294–306] Toxins present in spoiled foods [99,302,307–311] Vegetable flavor [154,312] Wine [118]

Sensors 2013 , 13 2311

They employed various pattern-recognition techniques, including multivariate data processing
based on principal component analysis (PCA) for mon itoring changes in the four types of fermented
milk (plain, strawberry, apple-pear, and forest-fruit) during storage, and partial least squares regression
(PLS) with artificial neural netw orks (ANNs), to estimate and pred ict human sensory panel evaluation
results. Correct classification of the four fermented milk types ranged from 87–95% correct
identification with a high le vel of correlation for ANN (r2 = 0.998) and PLS (r2 = 0.992). Sensor analysis
and food classification using potentio metric e-tongues have been applie d to many other similar functions
to qualify taste characteristics in the food and beverage industries [49,89,143,147,148,181,183].
The cognitive mechanisms that control human sens ory perceptual interac tions between olfaction
and taste have been thoroughly st udied. Olfaction has a strong infl uence on taste and trigeminal
perceptions and modulates pe rceptual taste/taste and taste/trigeminal sensory interactions, suggesting a
multiplicity of overlapping olfactory /trigeminal/taste perceptual inte ractions to foods with complex
flavors [4] . Generally, odor-taste interactions are regarded by the scientific community to be the result
of associations experienced and committed to memory following episodes of exposure to foods
without any involvement of expl icit attention or learning [313–315]. Perceptual interactions between
olfaction and taste have been extensively explored in aqueous systems. Initial studies reporting perceptual interactions between olf action and taste showed that tastes perceived to be attributed to
ethyl butyrate and citral odorants by test subjects disappeared wh en the retronasal olfactory was
prohibited by closure of the nasal passages [316,317] . These complex sensory interactions between
olfaction and taste have been explored in elect ronic-sensor research by combining the use of
electronic-noses and electronic-ton gue technologies to assess the ar omas and flavors of specific
foods [27,49,138,181,201]. Additional reviews of e-nose an d e-tongue applications in the food industry
have been published previously [4,318–320].
4. Electronic-Nose Applications in Forestry
Tree sap-flow sensors, consisting of cylindrical thermocouples and heater probes for estimating
plant transpiration [321], are importa nt instruments for assessing the phys iological state of forest trees
to determine the presence of drought stresses and to measure wood-moisture content. This information
is essential for making forest management decisi ons such as estimating the proper time for tree
harvests. The primary intent of physiological measurem ents is to monitor physical parameters that are
indicators of the health of individual trees. Sim ilarly, electronic-nose devi ces have been used to
determine the presence of damaging ins ects in wood (e.g., termites) [61], to identify tree diseases [106],
and detect other microbial pests th at have significant impacts on the present status of forest-stand
health and future tree merchantabilit y following tree harvests. Visual asse ssments to confirm plant-health
status, determined with e-nose inst ruments, also are possible via image analysis of plant symptoms
using smart optical sensors [322].
Wilson et al . [2] first applied e-nose technologies to plant pathology for the diagnosis of tree
diseases, particularly those cau sed by phytopathogenic microbes, such as vascular wilts [107] and
bacterial wetwood, and for the detect ion and identification of wood decay fungi, causal agents of wood
rots in living trees. Subsequent studies have demonstrated th e capabilities of several e-nose
instruments to detect specific types of wood decays, i.e., those caused by particular wood decay fungi,

Sensors 2013 , 13 2312

in different host wood species [86]. The early detect ion of incipient wood decays in trees with e-noses
is particularly important in forested urban enviro nments where tree failures, e.g., breakages of major
limbs or the main truck, can cause significant damage to property or result in human fatalities [120,323].
The proper identification of wood types and charac teristics has many impor tant applications in
forestry, forest management and production, and fore st science. Wood type and composition affects
the microenvironmental characteristics of forested ecosystems, the types of flora, fauna, and microbes
present, the relative uti lization of the wood as a food and hab itat base, and the quality of forest
products manufactured from various wood types present in a forest stand.
Three species of conifers predominate in the forest stands of eastern Canada, including black spruce
(Picea mariana ), balsam fir ( Abies balsamea ) and jack pine ( Pinus banksiana ). The quality of pulp and
paper produced from wood chips of these three speci es is determined by th e proportion of wood types
present in the wood chip mixture for each batch. C onsequently, a determination of the composition of
wood types present in the mixture is a prerequisite to obtaining an accurate assessment of expected
product (paper) quality. Garneau et al. [324] utilized a Cyranose 320 e-nose, containing a sensor array
with 32 thin-film carbon black composite (CBC) sensors, to disc riminate between the odor signatures
(fingerprints) of wood chip mixtures (in each sa mple batch) based on wood-type composition derived
from either sapwood or heartwood. Unknown samples were identified at high levels of confidence
using CPA and comparisons against aroma refe rence databases created from known wood-chip
mixtures of different wood-type proportions.
Identifications of wood types based on unique mi xtures of wood volatiles also are useful for
determining niche-functions of microbes and micro-i nvertebrates in forested ecosystems and in studies
of chemotaxonomy [30,85,126]. Such information facilitates understanding of the operations and
interactions between organisms in ecosystem microclimates, facilita ting multi-use forest management
decisions. Headspace volatiles from woody plant parts provide valuable chemotaxonomic data to
indicate relatedness between plant sp ecies within and between plant fa milies that often support genetic
(DNA sequence-homology) data.
The specificity of e-nose identific ations of wood samples is so pr ecise that e-nose aroma signatures
may even be used to identify individual logs th at are inventoried from a tree harvest [325,326]. Log
tracking with e-nose devices has been developed to help counter high-value log theft that has become
increasingly common on public lands in the United States, and to facilitate inventory-accounting of
harvested logs from the forest stand to the lumber mill. During log-sniffing procedures, e-noses also may be used to improve the efficiency of logging cuts in log-harvesting ope rations by detecting bole
sections with decay or defects and guiding laser scanners of logging harv ester machines [327].
Similarly, e-noses may be used in the logging yard and in the lumber-cutting line of commercial saw
mills to detect wood decays and defects in logs to increase the efficiency of saw cuts by minimizing
lumber-defect losses (cull volume).
There are several important functions that e-nose instruments play within the manufacturing sector
of the forest products industry. E-no se applications in forest-products manufacturing in clude industrial
processing controls, particularly for monitoring of chemical and biochemical processes to adjust
machinery controls [87–89], quality control [90], a nd waste management [5]. Federal regulations
require personnel at industrial pro cessing plants to monitor, dete ct, and control hazardous waste
emissions, including gas releases of malodorous effluents and air pollu tants from industr ial facilities,

Sensors 2013 , 13 2313

lumber and paper mills that operate within the fo rest products industry. Electronic noses serve a very
significant function in keeping fo rest-products manufacturing plants safe for the environment and
surrounding communities.
5. E-Nose Instrument Types Used in Agriculture and Forestry Applications
A wide range of e-nose instrument types are utili zed in the agricu ltural and forestry industries to
perform many diverse functions and applications to facilitate the multitude of steps and processes
involved in the production of plant- based products (Table 5). The major ity of these applications have
involved the use of MOS and CP-type sensor s, but other e-nose se nsor types (CBC, CO 2, ECS,
MOSFRT, QMB, SAW, and SnO 2 sensors) have been used to dete ct certain specialized types of
gas analytes.
Table 5. Electronic-noses used for specific ag ricultural and forestry applications.
Applications Electronic-nose Sensors/types † Chemicals detected or uses References
Crop production Moses II 8 MOS, 8QMB Pesticide residues [328]
Aromascan A32S 32 CP Pe sticide residues [33,46]
Environment BH-114 14 CP As, Cd, Pb, Zn (in water) [329]
Kamina 38 MOS NH 3, chloroform [330]
ProSAT 8 CP Diesel oils [331] Cyranose 320 32 CBC H
2S, SO 2, VOCs [332]
FreshSense 4 ECS CO, H 2S, NH 3, SO 2 [266]
Food EOS 835 6 MOS Mycotoxin contaminants, fruit variety
classifications [209,333]
EOS 507 6 MOS Oxidative status and classify olive oils [191]
PEN 2 10 MOS Mycotoxin contaminants,
fish shelf-life and freshness [305,334,335]
Food FOX 4000 18 MOS Alcoholic-beverage off-flavor
detection and discrimination [205]
Experimental 8 QMB Water loss in postharvest fruits [118]
E-nose 8 MOS Classify fruit odors by source [336]
Manufacturing
control Figaro TGS 2600 4 MOS Continuous monitoring-control of
industrial processes [50]
Multi-analyzer 10 MOSFET,
19 MOS,
18 SnO 2, CO 2 Batch microbial fermentation
processes [337,338]
Plant pathology Aromascan A32S 32 CP Disease detection, pathogen ID,
wood decay fungi ID [2,86,106,107]
LibraNose 2.1 8 QMB Wood decay and fungi ID [86]
PEN 3 10 MOS Wood decay and fungi ID [86] Cyranose 320 32 CBC Post-harvest disease detection [117] Wood decay (bas al stem rot) [339]
Plant taxonomy Aromascan A32S 32 CP Plant identifications, chemo-taxonomy
(classifications) [30,85,126]

Sensors 2013 , 13 2314

Table 5. Cont .
Applications Electronic-nose Sensors/types † Chemicals detected or uses References
Quality control/
quality assurance A-nose 8 MOS Detection and classi fication of coffee
sample/batch defects [224]
Z-nose 7100 1 SAW Detecting adulteration in virgin
coconut oil [259]
Waste EOS 3, 9 6 MOS Composting gas effluents, alcohols,
sulfur compounds [340]
PEN 2 10 MOS Waste-treatment monitoring [341]
Aromascan A32S 32 CP Monitoring odor abatement using a
biofiltering system [125]
Wood Aromascan A32S 32 CP Wood identifications, bacterial
wetwood detection [2,30,85,126]
† Number of sensors and sensor type abbreviations: Carbon black composite (CBC), Carbon dioxide sensor
(CO 2), Conducting polymer (CP), elect rochemical (EC), Metal oxide semiconductor (MOS), Metal oxide
semiconductor field effect transistor (MOSFET), Quar tz crystal microbalance (QMB), surface acoustic wave
(SAW), and Tin dioxide (SnO 2), a type of MOS sensor.
The major application sectors to which e-nose gas detections have been applied within the
agricultural and forestry industrie s are in such key areas as crop and food production, chemotaxonomy,
environmental protection and monitoring, manufactur ing process controls, pl ant pathology, quality
control and quality assurance (QA/QC), waste management, and wood identifications.
Testing the aroma qualities and ch aracteristics of manufactured plant products resulting from
specialized manufacturing processe s is among the most important utilities afforded by the use of
e-nose devices in agriculture and forestry. E-nos es are capable of discriminating very subtle
differences in the aroma characteri stics of manufactured food and fiber products which affect aromatic
favorability qualities (discerned by consumers) that often determine their choices of preferred product
brands. For example, many different coffee brands ar e available in commercial food markets of most
developed countries. The aroma constituents of co ffee are very complex involving hundreds of VOCs
with a wide range of functional groups [342]. Studies of the most significant constituent compounds
accounting for the characteristic coffee aroma have indicated th at about 29 VOCs were most
responsible for the roast and gr ound coffee aroma of which only 13 had a particularly important
contribution to coffee aroma [152,343]. Thus, no si ngle compound was found that could be considered
most responsible for the typical fl avor of roasted and ground coffee.
Routine analyses frequently are pe rformed on coffee aromatic extracts to evaluate the effectiveness
of the extraction methods used in rendering a quality coffee aroma. A good extraction method is
expected to provide an extract w ith sensory characteristics very close to the aroma of ground coffee
beans prior to extraction. Sarrazin et al . [344] evaluated five differe nt extraction methods on three
coffee brands: supercritical-fluid extraction with carbon dioxide, simu ltaneous distilla tion extraction,
oil recovery under pressure, and v acuum steam-stripping with water (or with organic solvent), to
compare the resulting coffee aromas derived from these extraction me thods. Arabica Colombia coffee
also was used for comparison at three different roas ting levels: green coffee, light-roasted and medium
roasted. By sensory testing, they found that the vacuum steam-stripping method with water provided
the most representative aroma extract for all three coffees.

Sensors 2013 , 13 2315

The specific compounds responsible for the char acteristic aromas of many other food products
similarly have been determined to identify the target chemicals that should be included in
aroma-recognition libraries for e-no se or e-tongue tests to eval uate food processing methods and
product brands. Precise chemical analyses of th e aromatic compounds most representative and
responsible for the charact eristic aromas associated with common fruits have been determined for
citrus [345], pineapple [346], watermelon [ 347], and wine (fermented grapes) [348].
Lorenz et al . [89] utilized an electroni c tongue to determin e the taste-masking effectiveness of
pharmaceutical formulations compared to place bos. Just like plant-based food products, oral
pharmaceutical products that reside in the mouth long enough to be tasted must be palatable. Palatable
attributes include appearance, taste, smell, and texture. Palatability affects compliance (patient use of a
prescribed drug) and dictates whether a therapeutic outcome is attained. Palatability of the drug
product must be given careful consideration to ach ieve optimal effectiveness because the drug cannot
work if the patient does not take the medication. Pa latability also affects commercial success of a drug
product because drug formulations with higher palatability have a greater chance of being prescribed
by physicians when there is a choice between several products with similar efficacy and safety
profiles. The electronic tongue used in this study was an Alpha MOS Astree II with 7 sensors
consisting of MOS Field Effect Tran sistors (MOSFET), similar to ion-se lective FET, but coated with a
proprietary membrane. Specific ch emical compounds were embedded in the co-polymer coating to
impart cross-selectivity/cross-sens itivity. The sensors were made w ith a polymer matrix, plasticizer
and various sensitive materials (e.g., alcoholic or hydrophobic ionophores). Th e data were collected
using a Ag/AgCl reference electrode.
6. E-Nose Uses in Combination with other Sensing Technologies
The potential to utilize electronic- nose devices in concert with ot her electronic sensing instruments
and new analytical detection methods for additive or synergistic benefits are considerable. The
following discussion provides some recent examples of feasible applicati ons, showing how other
detection methods might be used in cooperation with e-noses to yield better, more detailed information
so critical to effective decision-making required in all phases and types of agricultural and forestry
production systems.
6.1. DNA Microarrays
E-nose devices have been used extensively to detect pathogens present in fish
products [4,264–268]. However, othe r detection technologies such a DNA microarrays are becoming
increasingly useful in helping to simultaneously identify the specific microbes or combination of
microbes responsible for fish diseases. Chang et al . [349] recently combined the use of 16S rDNA
PCR and DNA hybridization technol ogy to construct a microarray for the simultaneous detection and
discrimination of eight fish pathogens ( Aeromonas hydrophila , Edwardsiella tarda , Flavobacterium
columnare , Lactococcus garvieae , Photobacterium damselae , Pseudomonas anguilliseptica ,
Streptococcus iniae and Vibrio anguillarum ) most commonly encountered in fish aquaculture. The
microarray consisted of short olig onucleotide probes (30 mer), co mplementary to the polymorphic
regions of 16S rRNA genes of the target pathogens. Target DNA that annealed to the microarray probes

Sensors 2013 , 13 2316

were reacted with streptavidin-c onjugated alkaline phosphatase and nitro blue tetrazolium/5-bromo-4-
chloro-3'-indolylphosphate, p-toluidine salt (NBT/BCIP), resulting in blue spots (color reaction) that
was easily visualized by the naked eye. Testing pe rformed on 168 bacterial st rains showed that each
probe in the microarray consistently identified its corresponding target strain with 100% specificity.
The microarray detection limit wa s estimated to be about 1 pg fo r genomic DNA and 103 CFU/mL for
pure pathogen cultures. These results demonstrat ed the feasibility of using DNA microarrays to
facilitate the simultaneous diagnostic te sting for multiple fish pathogens. Zhang et al . [350]
summarized the current status of microarray technology for the detec tion and analysis of chemical
contaminants in foods.
6.2. Biosensors
The common use of e-noses to detect microbi al toxins produced by human pathogens in
foods [307–311] may be improved by the additional de tection of the specific microbial strains of
human pathogens (such as Escherichia coli ) known to cause the most da mage to humans that consume
contaminated foods. Liu et al. [351] multiplexed an electrochemical DNA biosensor for the detection
of a highly specific sing le-nucleotide polymorphism (SNP) within the β-glucuronidase gene (uidA),
characteristic of the most toxic strain of E. coli . A 16-electrode array was applied with an
oligonucleotide-incorporated nonfouling surface (ONS) on each electrode for the resistance of unspecific
absorption. The fully matched target DNA templated the ligation between the capture probe, assembled
on gold electrodes and the tandem signal probe w ith a biotin moiety, wh ich was transduced to
peroxidase-based catalyzed amperometric signals. Th ey demonstrated the poten tial practical use of the
ONS-based electrochemical DNA bi osensor using a SNP on the β-glucuronidase gene (uidA) of E. coli
(T93G) to screen food lots and detect the presence of the most harmful (O157:H7) E. coli strain in order
to help prevent possible life-threatening E. coli outbreaks due to consumption of contaminated food lots.
Label-free optical detection systems for industrial small-molecule chemical screening a pplications
have gained popularity during the past decade within many industr ies. Microplate-based biosensor
systems hold the promise to match the throughput requirements for indus trial uses without
compromising data quality, thus repr esenting a sought-after complement to traditional fluidic systems.
Geschwindner et al . [352] reviewed the applic ation of the two most prominent optical biosensor
technologies, namely surface plasmon resonance ( SPR) and optical waveguide grating (OWG), in
small-molecule screening. These methods offer good complimentary support for e-nose sensors to
monitor industrial chemicals in manufacturing processes.
Microsensing systems using biotic sensor component s, such as optical fibe r biosensors, are in high
demand because of their lower cost and usefulness as tools for measurement and analysis in the fields
of biorobotics, healthcare, pharmaceuticals, environm ental monitoring, and military defense as well as
in various agricultural applica tions, such as disease diagnosis, food testing, and environmental
detection of biological agents (h omeland security). Thus, optical biosensors compliment the
same detection objectives of e-nose instruments. Zhang et al . [353] recently proposed a new fiber
surface-modification methodology using gold nanoparticles to increase the sensi tivity of fiber-optic
plasmon resonance biosensors.

Sensors 2013 , 13 2317

6.3. Chemical Aptasensors
The compatible marriage between conducting polymer (CP) technologies of electronic noses and
modified electrodes using nanoparticles, derived fr om electrochemical (electrode) technologies, has
resulted in the development of chemical aptasens ors (electrochemical bios ensors) consisting of CP
nanocomposite materials produced by the electropolym erization of CPs onto specialized nanoparticle
electrodes. Nanocomposites containi ng inorganic nanoparticle and CPs a llow current flow with unique
electrical and optical propertie s, compared to CPs or metal nanoparticles alone [354,355]. The
electrocatalytic properties of na noparticles are enhanced by the favorable environment supplied by the
CP-polymeric matrix [356]. Conducting polymers exhibit unique properties such as catalysis,
conductivity, biocompatibility, and the ability to act as an electrical plug connecting the bio-
recognition element to the su rface of the electrode [357–359].
One major class of environmental contaminants called Endocrine Disrupting Chemicals (EDCs),
named for the disruptions these ch emicals cause to normal functions of the endocrine system, has
become an important research topic in the field of environmental science because EDCs cause adverse
effects on humans and their progen y, as well as on many other organisms in natural environments.
EDCs are ubiquitous becaus e of their abundant use in many industria l and agricultural applications [360].
Most EDCs are synthetic organic chemicals introduced into the e nvironment by anthropogenic sources,
but they can also be naturally generated by the estrogenic hormones 17 β-estradiol and estrone in
humans exposured to EDCs especially via drinki ng water. Consequently, the detection of these
chemicals in humans and the environment is nece ssary to protect public and environmental health.
Olowu et al. [361] developed a simple and highly sensitiv e electrochemical DNA aptasensor with high
affinity for endocrine-disrupting 17 β-estradiol. Poly(3,4-ethylenedi oxylthiophene) (PEDOT), doped
with gold nanoparticles (AuNPs), was electrochemically synthesized and employed for the
immobilization of biotinylated aptamer to detect the 17 β-estradiol target. The aptasensor distinguished
17β-estradiol from structurally-similar endocrine di srupting chemicals, demons trating specificity to
17β-estradiol. The detectable co ncentration range of the 17 β-estradiol was 0.1 nM–100 nM, with a
detection limit of 0.02 nM.
6.4. Electronic Tongues
Electronic noses have been used in combination with electroni c-tongues for many applications
primarily in the food industr y [27,49,138,181,201]. However, potentiom etric e-tongues have been
employed in a wide range of other applications in agriculture and forestry. So me examples of e-tongue
applications include the detection and analysis of alkaline ions [362–364], anions [365], ascorbic
acid [366], environmental polluta nts monitoring [367], heavy metal ions [368], nitrates [369],
oxidizable compounds [370], paper mill effluents [371], pestic ides [372], and pheno lic compounds [373] in
liquids or industrial-proc essing solutions. Gutierrez et al. [374] used an e-tongue to monitor fertigation
(i.e., application of fertilizers in ir rigation water) nutrients applie d for greenhouse cultivation (plant
propagation). In agricultural food anal yses, e-tongue sensors often util ize a lipid membrane as a taste
element to measure electrical char ge potential across the membrane when analytes (taste) molecules

Sensors 2013 , 13 2318

come in contact with it. The dete ction limit of the e-tongu e sensor may be optimized by adjusting the
concentration of the lipid in the membrane [186].
Vlasov et al. [375] provided an early review of the deve lopmental history of potentiometric sensors
as an analytical tool, over the past century, de scribing advances from si ngle-ion sensors to new
multisensor arrays for liquid (solution) analysis th at utilize advanced mathematical procedures for
signal processing based on pattern recognition (PARC) and multivaria te analysis including ANNs and
PCA. More recent reviews provide further details of e-tongue developments [376–378].
6.5. Electroconductive Hydrogels
Electroconductive hydrogels are co mposite biomaterials made of polymeric blends combining
conductive electroactive pol ymers (CEPs) with highly hydrated hyd rogels. They bring together the
redox-switching and electrical propert ies of CEPs with the small-mol ecule transport and compatibility
of cross-linked hydrogels [379]. CEPs often are incorporated into biosensors to de tect chemical species
such as proteinaceous antigens, metabolites, enzyme substrates , and ssDNA fragments [90]. The
capability of detecting proteins, enzymes, and DNA fr agments is most useful for sensing the presence
of toxins and microbial cont aminants in foods, beverage s, and drinking water. Park et al. [380] recently
developed a suspension protein microarray usi ng shape-coded polyethylene glycol (PEG) hydrogel
microparticles for potential applications in multiplex and high-thr oughput immunoassays. Two
different mixtures of hydrogel micr oparticles with different shapes, immobilizing IgG (c ircle) and IgM
(square), were prepared allowing s imultaneous detection of two different target proteins without cross-
talk using the same fluorescence indicator because each immunoassa y was easily identified by the
shapes of hydrogel microparticles.
Many other examples show the potential for using e-nose instruments in combination with other
electronic-sensing devices to help confirm gas-detection determina tions for specific application
areas [90]. Spinelli et al. [381] evaluated the use of a near infr ared (NIR) instrument in combination
with an electronic nose system for the early detec tion of fire blight (diseas e) in pears. The e-nose
system detected the disease prior to symptom deve lopment by the distinctive olfactory signature of
volatiles released as early as si x days after infection. Sankaran et al. [382] reviewed other advanced
techniques and instruments for detecting plant dis eases which might be used in combination with
electronic noses for disease diagnoses.
7. Conclusions
Electronic-nose devices have been u tilized in a wide diversity of a pplications in the agriculture and
forestry industries to improve the effectiveness, e fficiency and safety of pr ocesses involved in the
production of quality food and fiber plant-based products while at the same time helping to avoid the
adverse effects of chemical byproducts on human hea lth and the release of t oxic chemical gases and
effluents into the environment. The challenges for th e future are to further develop e-nose technologies
to expand on potential applications in these natu ral plant-production sector s by exploring several new
key areas of scientific R&D including the developmen t of smaller, portable devi ces more applicable to
field use, simpler application-specific instruments at lower costs, new sensor types and algorithms for
more effective gas-detection and discriminati ons, and the discovery of new, problem-solving

Sensors 2013 , 13 2319

applications requiring gas-sensing ta sks within plant-product industries. There is also a large potential
for the integration of e-nose uses with other electronic-sensing instru ments for cooperative and
synergistic applications, providing more useful information for decision-making by re source, industrial
and plant-production managers. This work will re quire the development of new specific e-nose
technologies with expanded sensor capabilities and thorough efficacy testing in real, end-user settings.
Recent advancements in e-nose designs and methods could lead to improve d gas-analyte detection.
For example, Brudzewski et al. [383] reported on an improved e-nos e that combines two identical or
very similar sensor arrays. Analyt e aromas were analyzed independen tly by the sensor arrays and the
difference between sensor output signals from the arrays was subject to 2-dimensional convolution,
greatly enhancing the sensitivity of the e-nose. Choi et al. [384] developed new data-refinement and
channel-selection methods for vapor classifica tion to reduce background noise in the data and
distinguish the portion of the data most useful for discriminations with a porta ble e-nose system. Data
refinement improved data clustering of different aroma classes and classification perf ormance. They
also designed a new sensor array that consiste d only of the useful (most aroma-discriminative)
channels. They analyzed data channels from i ndividual sensors by evalua ting discriminative power
using the mask feature in data refinement. By this process, th e new sensor array had improved
classification rates and efficiency in data computation and storage.
Finding new ways to improve e-nose performance th rough the use of better or more target-specific
sensors and sensor arrays, patt ern-recognition algorithms, data analysis methods, and sensor
architecture and micromorphology s hould significantly widen the range of gas-sensing capabilities and
applications of e-noses in agricu ltural and forestry plant-product industries. Several studies have
shown how nanostructures may be applied to e-nos e sensors to improve in strument performance.
Twomey et al. [385] devised techniques using a combinatio n of microfabrication techniques, e-beam
evaporation and pulsed-laser deposition, to apply coatings on an electronic-tongue device that
contained all of the electrodes inte grated on a silicon die to improve robustness and reproducibility of
the device. Sun et al. [386] recently reviewed some of the wa ys that sensitivity, selectivity, response
speed, and performance of MOS sensors could be improved such as th rough changes in the
morphology and structure of sensing ma terials, including modifications in particle size, shape, porosity
and metal-doping. When the particle size of metal-oxide sensor coatings is clos e to or less than double
the thickness of the space-charge layer, the sensitiv ity of the sensor will in crease remarkably (known
as the “small-size effect”), yet the small size of me tal oxide nanoparticles will be compactly sintered
together during the film-coating process, a signifi cant disadvantage for analyte gas diffusion. Metal
doping is particularly useful in enhancing cataly tic activity and modulating the intrinsic electrical
resistance of the metal-oxide sensor coating. Zhang et al . [76] found that unmodified multi-walled
carbon nanotubes (MWNTs) and those modified by atmo spheric pressure dielectric barrier discharge
(DBD) air plasma improved gas sensor sens itivity, response time, and selectivity for H 2S, but not for
SO 2 detection. Chen et al. [387] reviewed the recent developm ent of e-nose systems based on metal
oxide nanowires with great potential for the improvem ent of sensor selectivity. They also discussed the
use of 1-D metal oxide nanostructures with unique geometric and physical properties for chemical-
sensing applications. Chemical sensors composed of a wide range of pristine 1-D metal oxide
nanostructures, such as In 2O3, SnO 2, ZnO, TiO 2, and CuO, have exhibited good sensitivity for the
detection of important industrial gases.

Sensors 2013 , 13 2320

Electronic noses with diverse sensor arrays are res ponsive to a wide variety of possible gas analytes
and have a number of advantages over traditional analytical instruments. Electronic nose sensors do
not require chemical reagents, have good sensitivity and specificity, pr ovide rapid repeatable (precise)
results, and allow non-destructive sampling of gas odorants or analyt es [388]. Furthermore, e-noses
generally are far less expensive than analytical systems, easier and ch eaper to operate, and have greater
potential for portability and field use compared with complex an alytical laboratory instruments [90].
Thus, electronic noses have far gr eater potential to be customized for unskilled laborers and for
innumerable practical and mechanized applications in the agricultural and fo rest-products industries.
However, some disadvantages of e-nose sensing include problems with re producibility, recovery,
negative effects of humidity and temperature on sens or responses, and inability to identify individual
chemical species within gas samples. Thus, electr onic noses probably will never completely replace
complex analytical instruments, but offer quick r eal-time detection and disc rimination solutions for
applications requiring accurate, ra pid and repeated determinations [90]. Such applications are
increasingly common and required for highly-mechaniz ed industrial manufacturing processes. The real
time, rapid-analysis capabilities of new portable e-noses ar e not only required but expected operating
capabilities to accommodate the fast -paced activities and mechanized processes of modern industries.
New sensing technologies emergi ng from R&D are beginning to yi eld new ways of improving on
e-noses and EAD capabilities through interfaces and comb inations with classical analytical systems for
rapid identification of individual chemical species within aroma mixtures. E-nose instruments are
being developed that combine EAD se nsors in tandem with analytical de tectors such as with fast gas
chromatography (FGC) [389]. More co mplicated technologies such as optical gas sensor systems may
improve on traditional e-nose sensor arrays by providi ng analytical data of mi xture constituents [390].
Similar capabilities for identifying multiple com ponents in liquid mixtures are now possible using
electronic tongues.
Very recent literature on e-nose appl ications in agriculture and forest ry provide some indications of
future trends in R&D and industrial uses within these areas. The strongest trend appears to be the
expanded utilization of e-nose device s as a monitoring tool in the f ood industry, assuring the safety and
quality of consumable plant produ cts, continuing with the development of new methods to detect
chemical contaminants [350,391], adulterations with baser elements [190,259,260], fo od-borne microbes
and pathogens [263,351,392–395], and toxins [84,311,396] in crops and food products. Similarly, new
food-analysis e-nose methods are being developed to detect changes in VOCs released from foods and
beverages in storage to assess shelf-life [346,397, 398] and quality [1 85,206,399–403], a nd for chemical
analyses [404,405], classifi cations [227,232,346,406,407], and disc riminations [162,218,228,408] of
food types, varieties and brands. Electronic-nose app lications to detect plan t pests in preharvest
and postharvest crops and tree sp ecies continue to expand to include new insect [54–61] and
disease [111,112,339,409–413] pests, primarily microbial plant pathogens, beyon d those originally
reported by Wilson et al . [2,106,107]. In the macroenvironments adjacent to indust rial plants and
indoor working spaces within asso ciated food- and fiber-production fa cilities, e-noses increasingly are
being utilized to monitor air quality to detect hazardous chemicals [68–70,76,77,80,414–419],
explosives and flammable gases [29,64], pollutant s [420–422] and other VOCs that threaten human
health. Likewise, malodorous gase s produced from point sources, such as agricultural feedlots and
paper-production facilities (pulp mills), increasingl y are being monitored by e-nose devices to assure

Sensors 2013 , 13 2321

that release of gaseous odors and effluents are maintained below offensive and hazardous threshold
levels [423–427]. Pesticide residues on food crops, part icularly on fresh fruits and vegetables, likely
will be monitored electronically with e-noses in the future by Food and Drug Administration (FDA)
officials for certification (clearing foods for safe consumption) prio r to marketing in groceries and
fresh-food stores [33]. Electronic- nose detection of human pathoge ns on fresh food surfaces also
should be possible with the development of portabl e e-noses having rapid se nsor-array detection,
analysis and recovery times. E-nos e applications involving the identification of agricultural
plants [37,126] and animal [306,428] species will become useful for many types of checks for quality
and identity controls, verification assurance, hea lth tests, and government-r egulation enforcement.
Tests of soil health and microbiological activity will provide means of assuring that crop plants are
grown in healthful growth environments and in soils free of harmful chemicals or microbes [429].
Finally, electronic-noses are having greater utility in indoor agricultural pr oduction within
greenhouses, such as for environmental controls of air quality (pollutants) [104], relative
humidity [102], fertigation metering [374], and irrigation wate r quality [80] to assu re that ornamental
and food crops remain free of bio tic and abiotic diseases [110,430].
The potential for future developments and new a pplications of electroni c-nose devices for the
agriculture and forestry industries are enormous as new technological discoveries in electronic-sensor
design allow for the development of new ga s-sensing capabilities for electronic noses. The current
trend of developing electronic noses for specific narrower applications will likely continue because such instruments are cheaper and provide greater ut ility, efficiency, and eff ectiveness in gas-sensing
operations in specialized industrial applications . The efficiency of specialized e-noses is derived from
the ability to minimize the number of sensors needed for discriminations by targeting the detection of
specific gases which reduces instrument costs, a llowing for greater portability through miniaturization.
New potential discoveries in sensor materials and technologies will help to expand e-nose capabilities
as new products, machines, and industrial processes are developed. These discov eries will lead to the
recognition of new ways to exploit the electronic nose to solve many gas-detection problems arising in
the agricultural and forestry industries.
Acknowledgments
The author would like to thank Gonzalo Pajares Martinsanz (University Complutense of Madrid,
Spain), Andrea Peruzzi (Universit y of Pisa, Italy), and Pablo Go nzalez-de-Santos (Centre for
Automation and Robotics, Arganda de l Rey, Madrid, Spain) for the invi tation and opport unity to write
this international revi ew article on the applic ations of electronic-nos e and electronic-tongue
instruments in the fields of agriculture and forest ry. The author also apprec iates the assistance of
Charisse Oberle who compiled, collated and formatted the references, and provided useful edits for the
manuscript.
References
1. Li, S.; Simonian, A.; Chin, B.A. Sensor s for agriculture and the food industry. Electrochem. Soc.
Interfac. 2010 , 19, 41–46.

Sensors 2013 , 13 2322

2. Wilson, A.D.; Lester, D.G.; Oberle, C.S. Deve lopment of conductive polymer analysis for the
rapid detection and identifica tion of phytopathogenic microbes. Phytopathology 2004 , 94,
419–431.
3. Cagni, D.; Ghizzoni, C. The role of non-volatile compounds in flavor science: Applications of
HPLC-mass spectrometry technique. In Flavour and Fragrance Chemistry ; Lanzotti, V.,
Taglialatela-Scafati, O., Eds.; Kluwer Academic Publishers: Dordrecht, The Netherlands, 2000;
pp. 221–226.
4. Wilson, A.D.; Baietto, M. Applications and advances in electronic-nose technologies. Sensors
2009 , 9, 5099–5148.
5. Wilson, A.D. Review of electronic-nose tec hnologies and algorithms to detect hazardous
chemicals in the environment. Proc. Technol. 2012 , 1, 453–463.
6. Goldstein, A.H.; Galbally, I.E. Known and unexplored organic constituents in the earth’s
atmosphere. Environ. Sci. Technol. 2007 , 41, 1514–1521.
7. Zhu, B.L.; Xie, C.S.; Wu, J.; Zeng, D.W.; Wang, A.H.; Zhao, X.Z. Influence of Sb, In and Bi
dopants on the response of ZnO thick films to VOCs. Mater. Chem. Phys. 2006 , 96, 459–465.
8. Ge, C.Q.; Xie, C.S.; Cai, S.Z. Preparation and gas-sensing properties of Ce-doped ZnO thin film
sensors by dip-coating. Mater. Sci. Eng. B 2007 , 137, 53–58.
9. Itoh, T.; Wang, J.; Matsubara, I.; Shin, W.; Izu, N. ; Nishibori, M.; Murayama, N. VOCs sensing
properties of layered organic-i norganic hybrid thin films: MoO 3 with various interlayer organic
components. Mater. Lett. 2008 , 62, 3021–3023.
10. Fernandez, C.D.; Manera, M.G.; Pellegrini, G.; Be rsani, M.; Mattei, G.; Rella, R.; Vasanelli, L.;
Mazzoldi, P. Surface plasmon resonance optical gas sensing of nanostructured ZnO films.
Sens. Actuator B Chem. 2008 , 130, 531–537.
11. Kim, Y.S. Thermal treatment effects on the material and gas-sensing properties of room-temperature
tungsten oxide nanorod sensors. Sens. Actuator B Chem. 2009 , 137, 297–304.
12. Xu, J.; Jia, X.; Lou, X.; Xi, G.; Han, J.; Gao, Q. Selective detection of HCHO gas using mixed
oxides of ZnO/ZnSnO 3. Sens. Actuator B Chem. 2007 , 120, 694–699.
13. Zeng, W.; Liu, T.-M. Gas-sensin g properties of SnO2-TiO2-base d sensor for volatile organic
compound gas and its sensing mechanism. Phys. B Condens. Matt. 2010 , 405, 1345–1348.
14. Phaisangittisagul, E.; Nagle, H.T.; Areekul, V. Intelligent method for sensor subset selection for
machine olfaction. Sens. Actuator B Chem. 2010 , 145, 507–515.
15. Che Harun, F.K.; Covington, J.A.; Gardner, J. W. Portable e-mucosa system: Mimicking the
biological olfactory. Proc. Chem. 2009 , 1, 991–994.
16. Breer, H. Sense of smell: Signa l recognition and transductions in olfactory receptor neurons. In
Handbook of Biosenso rs and Electronic No ses: Medicine, Food and Environment ; Kress-Rogers, E.,
Ed.; CRC Press: Boca Raton, FL, USA, 1997; pp. 521–532.
17. Peterlin, Z.; Li, Y.; Sun, G.; Shah, R.; Firestein, S.; Ryan, K. The importance of odorant
conformation to the binding and activation of a representative olfactory receptor. Chem. Biol.
2008 , 15, 1317–1327.
18. Turin, L.; Yoshii, F. Structure-odor relations: A modern perspective. In Handbook of Olfaction
and Gustation , 2nd ed.; Richard, L.D., Ed.; Informa Healthcare: New Yor k, NY, USA, 2003;
p. 35.

Sensors 2013 , 13 2323

19. Mori, K.; Shepherd, G.M. Emerging principles of molecular signal proce ssing by mitral/tufted
cells in the olfactory bulb. Semin. Cell Biol. 1994 , 5, 65–74.
20. Myth Debunking 1: What Are Aldehydes, Ho w Do Aldehydes Smell and Chanel No.5.
Available online: http://perfumeshrine.bl ogspot.com/2008/12/myth-debunking-1-what-are-
aldehydes-how.html/ (accessed on 1 November 2012).
21. Rossiter, K.J. Structure-odor relationships. Chem. Rev. 1996 , 96, 3201–3240.
22. Wise, P.M.; Olsson, M.J.; Cain, W.S. Quantification of odor quality. Chem. Sens. 2000 , 25, 429–433.
23. McGinley, C.M.; McGinley, M.A. Odor quantifi cation methods and practices at MSW landfills.
In Proceedings of Air and Waste Management A ssociation 91st Annual Meeting and Exhibition ,
Stillwater, MN, USA, 14 June 1998; p. 12.
24. Zarzo, M. Effect of functiona l group and carbon chain length on the odor detection threshold of
aliphatic compounds. Sensors 2012 , 12, 4105–4112.
25. Goodner, K.L.; Dreher, J.G.; Rouseff, R.L. The dangers of creating false classifications due to noise
in electronic nose and similar multivariate analyses. Sens. Actuator B Chem. 2001 , 80, 261–266.
26. Lu, Y.; Bian, L.; Yang, P. Quantitative artifi cial neural network for electronic noses. Anal. Chim.
Acta 2000 , 417, 101–110.
27. Buratti, S.; Ballabio, D.; Benedetti, S.; Cosio, M.S. Prediction of Italian red wine sensorial
descriptors from electronic nose, electronic tongue and spectrophotometric measurements by means
of genetic algorithm regression models. Food Chem. 2007 , 100, 211–218.
28. Wang, X.; Ye, M.; Duanmu, C.J. Classification of data from electronic nose using relevance
vector machines. Sens. Actuator B Chem. 2009 , 140, 143–148.
29. Brudzewskia, K.; Osowskia, S.; Pawlowskia, W. Metal oxide sensor arra ys for detection of
explosives at sub-parts-per million concentrati on levels by the differential electronic nose.
Sens. Actuator B Chem. 2012 , 161, 528–533.
30. Wilson, A.D.; Lester, D.G.; Oberle, C.S. App lication of conductive polymer analysis for wood
and woody plant identifications. For. Ecol. Manage. 2005 , 209, 207–224.
31. Anderson, L.J.; Harley, P.C.; Monson, R.K. Reduc tion of isoprene emissions from live oak with
oak wilt. Tree Physiol. 2000 , 20, 1199–1203.
32. Faldt, J.; Solheim, H.; Langstrom, B.; Borg-K arlson, A.K. Influence of fungal infection and
wounding on contents and enantiomeric comp ositions of monoterpenes in phloem of Pinus
sylvestris . J. Chem. Ecol . 2006 , 32, 1779–1795.
33. Wilson, A.D. Development of an electronic-nose technology for the rapid detection of
agricultural pesticide residues. Phytopathology 2012 , 102, 136.
34. Bernáth, J.; Novák, I.; Szabó, K.; Seregél y, Z. Evaluation of selected oregano ( Origanum vulgare
L. subsp. hirtum Ietswaart) lines with traditiona l methods and sensory analysis. J. Herbs Spices
Med. Plants 2005 , 11, 19–26.
35. Benedetti, S.; Buratti, S.; Spinardi, A. ; Mannino, S.; Mignani, I. Electronic nose as a
nondestructive tool to characterize peach cultivar s and to monitor thei r ripening stage during
shelflife. Postharvest Biol. Technol . 2008 , 47, 181–188.
36. Komaraiah, P. ; Navratil, M.; Carlsson, M.; Jeffers, P.; Brodelius, M.; Brodelius, P.E.; Kieran,
P.M.; Mandenius, C.F. Growth be havior in plant cell cultures ba sed on emissions detected by a
multisensor array. Biotechnol. Prog. 2004 , 20, 1245–1250.

Sensors 2013 , 13 2324

37. Fujioka, K.; Shirasu, M.; Manome, Y.; Ito, N. ; Kakishima, S.; Minami, T.; Tominaga, T.;
Shimozono, F.; Iwamoto, T.; Ikeda, K.; et al. Objective display and discrimination of floral odors
from Amorphophallus titanium , bloomed on different dates and at different locations, using an
electronic nose. Sensors 2012 , 12, 2152–2161.
38. Villanueva-Fierro, I.; Popp, C.J.; Marin, R.S. Bi ogenic emissions and ambient concentration of
hydrocarbons, carbonyl compounds a nd organic acids from ponderosa pine and cottonwood trees
at rural and forested site s in central New Mexico. Atmos. Environ. 2004 , 38, 249–260.
39. Dudareva, N.; Pichersky, E. Metabo lic engineering of plant volatiles. Curr. Opin. Biotechnol.
2008 , 19, 1–9.
40. Degenhardt, J.; Gershenzon, J.; Baldwin, I.T.; Ke ssler, A. Attracting friends to feast on foes:
Engineering terpene emission to make crop plants more attractive to herbivore enemies.
Curr. Opin. Biotechnol. 2003 , 14, 169–176.
41. Aharoni, A.; Jongsma, M.A.; Bouwmeester, H.J. Volatile science? Meta bolic engineering of
terpenoids in plants. Trends Plant Sci. 2005 , 10, 594–602.
42. Dudareva, N.; Pichersky, E. Metabolie engi neering of floral scent of ornamentals. J. Crop
Improv. 2006 , 18, 325–346.
43. Lücker, J.; Verhoeven, H.A.; van der Plas, L.H.W. ; Bouwmeester, H.J. Mo lecular engineering of
floral scent. In Biology of Floral Scent ; Dudareva, N., Pichersky, E., Eds.; CRC Press: Boca
Raton, FL, USA, 2006; pp. 321–337.
44. Gregory, C.; Silva, J.B.; Wiziacki, N.K.L.; Paterno, L.G.; Paniazzi, M.C.C.; Fonseca, F.J.
Application of electronic tongue in identification of soybean. In Proceedings of the 13th
International Symposium, Olfaction and Electronic Nose ; Brescia, Italy, 15 April 2009;
pp. 533–534.
45. Déjous, C.; Rebiére, D.; Pistré, J.; Tiret, C. ; Planade, R. A surface acoustic wave gas sensor:
Detection of organophosphorus compounds. Sens. Actuator B Chem. 1995 , 24–25 , 58–61.
46. Wilson, A.D.; Oberle, C.S. Identification an d discrimination of pe sticide residues using
electronic aroma detection. Phytopathology 2004 , 94, 110.
47. Obare, S.O.; de, C.; Guo, W.; Haywood, T.L.; Samuels, T.A.; Adams, C.P.; Masika, N.O.;
Murray, D.H.; Anderson, G.A.; Campbell, K.; et al . Fluorescent chemosensors for toxic
organophosphorus pesticides: A review. Sensors 2010 , 10, 7018–7043.
48. Mishra, R.K.; Deshpande, K.; Bhand, S. A high-throughput enzyme a ssay for organophosphate
residues in milk. Sensors 2010 , 10, 11274–11286.
49. Rudnitskaya, A.; Legin, A. Sensor systems, el ectronic tongues and electronic noses, for the
monitoring of biotechno logical processes. J. Ind. Microbiol. Biotechnol. 2008 , 35, 443–451.
50. Trincavelli, M.; Coradeschi, S.; Loutfi, A. Odour classification system for continuous monitoring
applications. Sens. Actuator B Chem. 2009 , 139, 265–273.
51. Gao, T.; Tillman, E.S.; Lewis, N.S. Detection a nd classification of volatile organic amines and
carboxylic acids using arrays of carbon black-dendrimer co mposite vapor detectors. Chem.
Mater. 2005 , 17, 2904–2911.
52. Tillman, E.S.; Koscho, M.E.; Grubbs, R.H.; Lewis, N.S. Enhanced sensitivity to and
classification of volatile carboxylic acids using arrays of linear poly(ethyl enimine)-carbon black
composite vapor detectors. Anal. Chem. 2003 , 75, 1748–1753.

Sensors 2013 , 13 2325

53. Tillman, E.S.; Lewis, N.S. Mechanism of enha nced sensitivity of linear poly(ethylenimine)-
carbon black composite detector s to carboxylic acid vapors. Sens. Actuator B Chem. 2003 , 96,
329–342.
54. Henderson, W.G.; Khalilian, A.; Han, Y.J.; Green e, J.K.; Degenhardt, D.C. Detecting stink
bugs/damage in cotton utilizing a portable electronic nose. Comput. Electron. Agric. 2010 , 70,
157–162.
55. Miresmailli, S.; Gries, R.; Gries, G.; Zamar, R.Z.; Isman, M.B. Herbivor e-induced plant volatiles
allow detection of Trichoplusia ni (Lepidoptera: Noctuidae) in festation on greenhouse tomato
plants. Pest Manage. Sci. 2010 , 66, 916–924.
56. Degenhardt, D.C.; Greene, J.K.; Khalilian, A. Temporal dynamics and electronic nose detection
of stink bug-induced volatile emissions from cotton bolls. Psyche 2012 , 2012 , 1–9.
57. Rains, G.C.; Tomberlin, J.K.; D’Alessandro, M. ; Lewis, W.J. Limits of volatile chemical
detection of a parasitoid wasp, Microplitis croceipes and an electronic nose: A comparative
study. Trans. ASAE 2004 , 47, 2145–2152.
58. Zhang, H.; Wang, J. Detection of age and insect damage incurred by wheat, with an electronic
nose. J. Stored Prod. Res. 2007 , 43, 489–495.
59. Rains, C.G.; Tomberlin, J.K.; Kulasiri, D. Using insect sniffing devices for detection.
Trends Biotechnol. 2008 , 26, 288–294.
60. Lan, Y.B.; Zheng, X.Z.; Westbrook, J.K.; Lopez, J. ; Lacey, R.; Hoffmann, W.C. Identification of
stink bugs using an electronic nose. J. Bionic Eng. 2008 , 5, 172–180.
61. Wilson, A.D.; Oberle, C.S. Development of an el ectronic-nose technology for the rapid detection
and discrimination of subterranean termites within w ood in service. Phytopathology 2011 , 101,
S192.
62. Lechuga, L.M.; Calle, A.; Golmayo, D.; Brione s, F. Hydrogen sensor based on a Pt/GaAs
Schottky diode. Sens. Actuator B Chem. 1991 , 4, 515–518.
63. Albert, K.J.; Myrick, M.L.; Br own, S.B.; Milanovich, F.P.; Walt, D.R. High-speed fluorescence
detection of explosives-like vapors. Anal. Chem. 2000 , 72, 1947–1955.
64. Trocino, S.; Donato, A.; Latino, M.; Donato, N. ; Leonardi, S.G.; Neri, G. Pt-TiO2/MWCNTs
hybrid composites for monitoring lo w hydrogen concentrations in air. Sensors 2012 , 12,
12361–12373.
65. Strom, G.; West, J.; Wessen, B.; Palmgren, U. Qu antitative analysis of microbial volatiles in
damp Swedish houses. In Health Implications of F ungi in Indoor Environments ; Samson, R.A.,
Flannigan, B., Flannigan, M.E., Ve rhoefl, A.P., Adan, O.C.G., Hoekstra, E.S., Eds.; Elsevier:
Amsterdam, The Netherlands, 1994; pp. 291–305.
66. Hwang, J.; Shin, C.; Yoe, H. Study on an agricu ltural environment monitoring server system
using wireless sensor networks. Sensors 2010 , 10, 11189–11211.
67. Mothé, G.; Castro, M.; Sthel, M.; Lima, G.; Brasil, L.; Campos, L.; Rocha, A.; Vargas, H.
Detection of greenhouse gas pr ecursors from diesel engine s using electrochemical and
photoacoustic sensors. Sensors 2010 , 10, 9726–9741.
68. Gawas, U.B.; Verenkar, V.M.S.; Patil, D.R. Nanostructured ferrite based electronic nose
sensitive to ammonia at room temperature. Sci. Technol. 2011 , 134, 45–55.

Sensors 2013 , 13 2326

69. Sato, T.; Breedom, M.; Miura, N. Improvement of toluene selectivity via the application of an
ethanol oxidizing catalytic cell upstream of a YS Z-based sensor for air monitoring application.
Sensors 2012 , 12, 4706–4714.
70. Trevathan, J.; Johnstone, R.; Chiffings, T.; Atkinson, I.; Bergmann, N.; Read, W.; Theiss, S.;
Myers, T.; Stevens, T. SEMAT—The next gene ration of inexpensive marine environmental
monitoring and measurement systems. Sensors 2012 , 12, 9711–9748.
71. Feng, L.; Musto, C.J.; Kemling, J.W.; Lim, S.H.; Su slick, K.S. A colorimetric sensor array for
identification of toxic gases be low permissible exposure limits. Chem. Commun. 2010 , 46,
2037–2039.
72. Dighavkar, C.; Patil, A.; Patil, S.; Borse, R. Al-doped TiO 2 thick film resistors as H 2S gas sensor.
Sci. Technol. 2010 , 9, 39–47.
73. Fine, G.F.; Cavanagh, L.M.; Afonja, A.; Binions, R. Metal oxide semi-conductor gas sensors in
environmental monitoring. Sensors 2010 , 10, 5469–5502.
74. Xie, C.; Xiao, L.; Hu, M.; Bai, Z.; Xia, X.; Zeng, D. Fabrication and formaldehyde gas-sensing
property of ZnO-MnO2 copl anar gas sensor arrays. Sens. Actuator B Chem. 2010 , 145, 457–463.
75. Ling, Y.P.; Heng, L.Y. A potentiometric formal dehyde biosensor based on immobilization of
alcohol oxidase on acryloxysuccinimi de-modified acrylic microspheres. Sensors 2010 , 10,
9963–9981.
76. Zhang, X.; Yang, B.; Wang, X.; Luo, C. Effect of plasma treatment on multi-walled carbon
nanotubes for the detection of H 2S and SO 2. Sensors 2012 , 12, 9375–9385.
77. Durrani, S.M.A.; Al-Kuhaili, M.F.; Bakhtiari, I. A.; Haider, M.B. Investigation of the carbon
monoxide gas sensing characte ristics of tin oxide mixed cerium oxide thin films. Sensors 2012 ,
12, 2598–2609.
78. Singh, S.; Hines, E.L.; Garner, J.W. Fuzzy neural computing of coffee and tainted-water data
from an electronic nose. Sens. Actuator B Chem. 1996 , 6, 185–190.
79. Chang, C.C.; Saad, B.; Surif, M.; Ahmad, M.N.; Shakaffm, A.Y.M. Disposable etongue for the
assessment of water quality in fish tanks. Sensors 2008 , 8, 3665–3677.
80. O’Connor, E.; Smeaton, A.F.; O’Connor, N.E.; Regan, F. A neural network approach to smarter
sensor networks for water quality monitoring. Sensors 2012 , 12, 4605–4632.
81. Labrador, R.; Soto, J.; Martinez-Manez, R.; Gil, L. An electronic tongue for qualitative analyses
of anions in natural water. J. Appl. Electrochem. 2009 , 39, 2505–2511.
82. Zhang, H.; Wang, J. Evaluation of peach qua lity attributes using an electronic nose. Sens. Mater.
2009 , 21, 419–431.
83. Lan, Y.B.; Wang, S.Z.; Yin, Y.G.; Hoffmann, W.C.; Zheng, X.Z. Using a surface plasmon
resonance biosensor fo r rapid detection of Salmonella typhimurium in chicken carcass. J. Bionic Eng.
2008 , 5, 239–246.
84. Weingart, O.G.; Gua, H.; Crevoisier, F.; Heitger, F.; Avondet, M.-A.; Sigrist, H. A bioanalytical
platform for simultaneous detection a nd quantification of biological toxins. Sensors 2012 , 12,
2324–2339.
85. Wilson; A.D.; Lester, D.G. Utili zation of aromascan analysis to identify host species of forest
pathogens from woody samples. Proc. Miss. Assoc. Pl. Pathol. Nematol. 1999 , 17, 13.

Sensors 2013 , 13 2327

86. Baietto, M.; Wilson, A.D.; Bassi, D.; Ferrini, F. Ev aluation of three electron ic noses for detecting
incipient wood decay. Sensors 2010 , 10, 1062–1092.
87. Zondev an, C.; Muresan, S.; de Jonge, H.G.; Thod en van Velzen, E.U.; Wilkinson, C.;
Nijhus, H.H.; Leguijt, T. Controll ing Maillard reactions in the h eating process of blockmilk using
an electronic nose. J. Agric. Food Chem. 1999 , 47, 4746–4749.
88. Navràtil, M.; Cimander, C.; Mandenius, C.F. On-line multisensor monitoring of yogurt and
Filjölk fermentations of production scale. J. Agric. Food Chem. 2004 , 52, 415–420.
89. Lorenz, J.K.; Reo, J.P.; Hendl, O.; Worthington, J.H.; Petrossian, V.D. Evaluation of a taste
sensor instrument (electronic tongue) for use in formulation development. Int. J. Pharm. 2009 ,
367, 65–72.
90. Wilson, A.D. Future applications of electronic-nose technologies in healthcare and biomedicine.
In Wide Spectra of Quality Control ; Ișin, A., Ed.; InTech Publis hing: Rijeka, Croatia, 2011;
pp. 267–290.
91. Pavlou, A.K.; Magan, N.; Sharp, D.; Brown, J.; Ba rr, H.; Turner, A.P. An intelligent rapid odour
recognition model in discrimination of Helicobacter pylori and other gastroesophageal isolates in
vitro . Biosens. Bioelectron. 2000 , 15, 333–342.
92. Pavlou, A.; Turner, A.P.F.; Magan, N. R ecognition of anaerobic bacterial isolates in vitro using
electronic nose technology. Lett. Appl. Microbiol. 2002 , 35, 366–369.
93. Hay, P.; Tummon, A.; Ogunfile, M.; Adebiyi, A.; Adefowora, A. Evaluation of a novel
diagnostic test for bacterial va ginosis: the electronic nose. Int. J. STD Aids 2003 , 14, 114–118.
94. Moens, L.; van Hoeyveld, E.; Peetermans, W. E.; de Boeck, C.; Verhaegen, J.; Bossuyt, X.
Mannose-binding lectin genotype and i nvasive pneumococcal infection. Hum. Immunol. 2006 ,
67, 605–611.
95. Siripatrawan, U. Rapi d differentiation between E. coli and Salmonella typhimurium using metal
oxide sensors integrated with pattern recognition. Sens. Actuator B Chem. 2008 , 133, 414–419.
96. Hamilton, S.; Hepher, M.J.; Sommerville, J. Detection of Serpula lacrymans infestation with a
polypyrrole sensor array. Sens. Actuator B Chem. 2006 , 113, 989–997.
97. Gardner, J.W.; Craven, M.; Dow, C.; Hines, E. L. The prediction of bact eria type and culture
growth phase by an electronic nose with a multi-layer perceptron network. Meas. Sci. Technol.
1998 , 9, 120–127.
98. Hahn, F. Actual pathogen detectio n: Sensors and algorithms—A review. Algorithms 2009 , 2,
301–338.
99. Kuang, Y.; Biran, I.; Walt, D.R. Living bact erial cell array for genotoxin monitoring. Anal.
Chem. 2004 , 76, 2902–2909.
100. Moyo, M.; Okonkwo, J.O.; Agyei, N.M. Recent advanc es in polymeric materials used as electron
mediators and immobilizing matrices in developing enzyme electrodes. Sensors 2012 , 12,
923–953.
101. Yang, M.-Z.; Dai, C.-L.; Lu, D.-H. Polypyrrole porous micro humidity sensor integrated with a
ring oscillator circuit on chip. Sensors 2010 , 10, 10095–10104.
102. Nizhnik, O.; Higuchi, K.; Maenaka, K. Self-ca librated humidity sensor in CMOS without
post-processing. Sensors 2012 , 12, 226–232.

Sensors 2013 , 13 2328

103. Liao, P.-J.; Chang, J.-S.; Chao, S.D.; Chang, H.-C.; Huang, K.-R.; Wu, K.-C.; Wung, T.-S. A
combined experimental and theoretical st udy on the immunoassay of human immunoglobulin
using a quartz crystal microbalance. Sensors 2010 , 10, 11498–11511.
104. Ast, C.; Schmälzlin, E.; Löhmannsröben, H.-G .; van Dongen, J.T. Optical oxygen micro- and
nanosensors for plant application. Sensors 2012 , 12, 7015–7032.
105. Baldwin, E.; Plotto, A.; Manthey, J.; McCollum, G.; Bai, J.; Irey, M.; Cameron, R.; Luzio, G.
Effect of Liberibacter infection (Huanglongbing disease) of citrus on orange fruit physiology and
fruit/fruit juice quality: chemical and physical analyses. J. Agric. Food Chem. 2010 , 58,
1247–1262.
106. Wilson, A.D.; Lester, D.G. Use of an electron ic-nose device for profiling headspace volatile
metabolites to rapidly identify phytopathogenic microbes. Phytopathology 1997 , 87, S116.
107. Wilson, A.D.; Lester, D.G. Applica tion of aromascan analysis to de tect and diagnose oak wilt in
live oaks. Phytopathology 1998 , 88, S97.
108. Prithiviraj, B.; Vikram, A.; Kushalappa, A.C.; Yaylayan, V. Volatile metabolite profiling for the
discrimination of onion bulbs infected by Erwinia carotovora spp. carotovora , Fusarium
oxysporum , and Botrytis allii . Eur. J. Plant Pathol. 2004 , 110, 371–377.
109. Vikram, A.; Hamzehzarghani, H.; Kushalappa, A. C. Volatile metabolites from the headspace of
onion bulbs inoculated with postharvest pat hogens as a tool for disease discrimination. Can. J.
Plant Pathol. 2005 , 27, 194–203.
110. Laothawornkitkul, J.; Moore, J.P.; Taylor, J.E.; Possell, M.; Gibson, T.D.; Hewitt, C.N.;
Paul, N.D. Discrimination of plant volatile si gnatures by an electron ic nose: A potential
technology for plant pest and disease monitoring. Environ. Sci. Technol. 2008 , 42, 8433–8439.
111. Jansen, R.M.C.; Wildt, J.; Kappers, I.F.; Bouwm eester, H.J.; Hofstee, J.W.; van Henten, E.J.
Detection of diseased plants by anal ysis of volatile organic compound emission. Annu. Rev.
Phytopathol. 2011 , 49, 157–174.
112. Spinelli, F.; Cellini, A.; Vanneste, J.L.; Rodr iquez-Estrada, M.T.; Costa, G.; Savioli, S.;
Harren, F.J.M.; Cristescu, S.M. Emission of volatile compounds by Erwinia amylovora :
Biological activity in vitro and possible exploitation for bacterial identification. Trees 2012 , 26,
141–152.
113. Gao, Y.; Jin, Y.; Li, H.; Chen, H. Volatile orga nic compounds and their role s in bacteriostasis in
five conifer species. J. Integr. Plant Biol. 2005 , 47, 499–507.
114. Magan, N.; Evans, P. Volatiles as an indica tor of fungal ac tivity and differentiation between
species, and the potential use of electronic nose technology for early detection of grain spoilage.
J. Stored Prod. Res. 2000 , 36, 319–340.
115. De Lacy Costello, B.J.P.; Ewen, R.J.; Gunson, H.E.; Ratcliffe, N.M.; Spencer-Phillips, P.T.N.
Sensors for Early Warning of Post harvest Spoilage in Potato Tubers ; British Crop Protection
Council: Farnham, UK, 2002; pp. 425–432.
116. Li, Z.; Wang, N.; Vijaya Raghavan, G.S.; Vign eault, C. Ripeness and ro t evaluation of “Tommy
Atkins” mango fruit thro ugh volatiles detection. J. Food Eng. 2009 , 91, 319–324.
117. Li, C.; Krewer, G.W.; Ji, P.; Scherm, H.; Kays, S. J. Gas sensor array for blueberry fruit disease
detection and classification. Postharvest Biol. Technol. 2010 , 55, 144–149.

Sensors 2013 , 13 2329

118. Santonico, M.; Bellincontro, A.; de Santis, D.; di Natale, C.; Mencarelli, F. Electronic nose to
study postharvest dehydration of wine grapes. Food Chem. 2010 , 121, 789–796.
119. Ewen, R.J.; Jones, P.R.H.; Ratcliffe, N.M.; Spencer-Phillips, P.T.N. Identification by gas
chromatography-mass spectrometry of the volatile organic compounds emitted from the woodrotting fungi Serpula lacrimans and Coniophora puteana , and from Pinus sylvestris timber.
Mycol. Res . 2004 , 108, 806–814.
120. Baietto, M. Development of a New Non-Invasive Tool for the Assessment of Decays in the Urban
Environment . Ph.D. Thesis, University of Milan, Milan, Italy, 2008; pp. 81–141.
121. Nylander, C.; Armgarth, M.; Lundström, I. An ammonia detector based on a conducting
polymer. Anal. Chem. Symp. Ser. 1983 , 17, 203–207.
122. Winquist, F.; Spetz, A.; Armgarth, M.; Lundström, I.; Danielsson, B. Biosensors based on
ammonia sensitive me tal-oxide-semicondu ctor structures. Sens. Actuator B Chem. 1985 , 8, 91–100.
123. Persaud, K.C.; Payne, P.A.; Khaffaf, S.M.; Dowdeswell, R.M.; Hobbs, P.J.; Misselbrook, T.H.;
Sneath, R.W. Application of c onducting polymer odor sensing ar rays to agricu ltural malodour
monitoring. In Proceedings of IEE Colloquium on Conduc ting Polymers and Th eir Applications
in Transducers and Instrumentation (Digest No: 1996/242) , London, UK, 30 October 1996;
pp. 6/1–6/4.
124. McGinley, M.A.; McGinley, C.M. Measur ing composting odors fo r decision making. In
Proceedings of the Annual Conference of U.S. Composting Council , San Antonio, TX, USA,
24–26 January 2005; pp. 1–13.
125. Sohn, J.H.; Dunlop, M.; Hudson, N.; Kim, T.I.; Yoo, Y.H. Non-specific conducting
polymer-based array capable of monitoring odour emissions from a biofiltration system in a
piggery building. Sens. Actuator B Chem. 2009 , 135, 455–464.
126. Wilson, A.D. Application of a conductive polymer electronic-nose device to identify aged woody
samples. In Proceedings of The 3rd International IARIA Conference on Sensor Device
Technologies and Applications , Rome, Italy, 19–24 August 2012; pp. 77–82.
127. Mielle, P. Electronic noses: Towards the object ive instrumental characte rization of food aroma.
Trends Food Sci. Technol. 1996 , 7, 432–438.
128. Hruskar, M.; Major, N.; Krpan, M. Application of a potentiometric sensor array as a technique in
sensory analysis. Talanta 2010 , 81, 398–403.
129. Zhang, H.; Wang, J.; Sheng, Y. Predictions of ac idity, soluble solids and firmness of pear using
electronic nose technique. J. Food Eng. 2008 , 86, 370–378.
130. Casilli, S.; de Luca, M.; Apetrei, C.; Parra, V.; Arrieta, Á.A.; Valli, L.; Jiang, J.; Rodríguez-
Méndez, M.L.; de Saja, J.A. Langmuir-Blodgett and Langmuir-Schaefer films of homoleptic and
heteroleptic phthalocyanine complexes as voltamme tric sensors: Applications to the study of
antioxidants. Appl. Surf. Sci. 2005 , 246, 304–312.
131. Cosio, M.S.; Buratti, S.; Mannino, S.; Benedetti, S. Use of an electrochemical method to evaluate
the antioxidant activity of herb extracts from the Labiatae family. Food Chem. 2006 , 97, 725–731.
132. Li, W.; Hosseinian, F.S.; Tsopmo, A.; Friel, J.; Beta, T. Evaluation of antioxidant capacity and
aroma quality of breast milk. Nutrition 2009 , 25, 105–114.
133. Li, W.; Friel, J.; Beta, T. An evaluation of th e antioxidant properties and aroma quality of infant
cereals. Food Chem. 2010 , 121, 1095–1102.

Sensors 2013 , 13 2330

134. Apetrei, C.; Rodríguez-Méndez, M.L.; Parra, V.; Gutierrez, F.; de Saja, J.A. Array of voltammetric
sensors for the discrimination of bitter solutions. Sens. Actuator B Chem. 2004 , 103, 145–152.
135. Thorngate, J.H., III; Noble, A.C. Sensory ev aluation of bitterness and astringency of
3R(-)-epicatechin and 3 S(+)-catechin. J. Sci. Food Agric. 1995 , 67, 531–535.
136. Kaneda, H.; Watari, J.; Takashi o, M. Measuring astri ngency of beverages using a quartz-crystal
microbalance. J. Am. Soc. Brew. Chem. 2003 , 61, 119–124.
137. Legin, A.; Rudnitskaya, A.; Kirsanov, D.; Frolova , Yu.; Clapham, D.; Caricofe, R. Assessment
of bitterness intensity and suppression effects using an electronic tongue. In Proceedings of The
13th International Symposium, Olfaction and Electronic Nose , Brescia, Italy, 15–17 April 2009;
pp. 271–274.
138. Apetrei, C.; Apetrei, I.M.; Villanueva, S.; de Saja, J.A.; Gutierrez-Rosales, F.; Rodriguez-
Mendez, M.L. Combination of an e-nose, an e- tongue and an e-eye for the characterisation of
olive oils with different degree of bitterness. Anal. Chim. Acta 2010 , 663, 91–97.
139. Arrieta, A.A.; Apetrei, C.; R odríguez-Méndez, M.L.; de Saja, J.A. Voltammetric sensor array
based on conducting polymer-m odified electrodes for the di scrimination of liquids. Electrochim. Acta
2004 , 49, 4543–4551.
140. Zhang, C.; Bailey, D.P.; Suslick, K.S. Colorimetric sensor arrays for the analysis of beers:
A feasibility study. J. Agric. Food Chem. 2006 , 54, 4925–4931.
141. Concina, I.; Falasconi, M.; Gobbi, E.; Bianch i, F.; Musci, M.; Mattarozzi, M.; Pardo, M.;
Mangia, A.; Careri, M.; Sbeveglieri , G. Early detection of microbi al contamination in processed
tomato by electronic nose. Food Control 2009 , 20, 837–880.
142. Rudnitskaya, A.; Polshin, E.; Kirsanov, D.; Lammertyn, J.; Nicolai, B.; Saison, D.; Delvaux, F.R.;
Delvaux, F.; Legin, A. Instrumental measurement of beer taste attributes using an electronic
tongue. Anal. Chim. Acta 2009 , 646, 111–118.
143. Polshin, E.; Rudnitskaya, A.; Kirsanov, D.; Legi n, A.; Saison, D.; Delvaux, F.; Delvaux, F.R.;
Nicolaï, B.M.; Lammertyn, J. Electronic tongue as a screening tool for ra pid analysis of beer.
Talanta 2010 , 81, 88–94.
144. Brown, R.J.C.; Keates, A.C.; Brewer, P.J. Sens itivities of a standard test method for the
determination of the pHe of bioetha nol and suggestions for improvement. Sensors 2010 , 10,
9982–9993.
145. Yang, Z.; Dong, F.; Shimizu, K.; Kinoshita, T. ; Kanamori, M.; Morita, A.; Watanabe, N.
Identification of coumarin-enric hed Japanese green teas and th eir particular flavor using
electronic nose. J. Food Eng. 2009 , 92, 312–316.
146. Medeiros, E.S.; Gregorio, R.; Martinez, R.A.; Mattoso, L.H.C. A taste sensor array based on
polyaniline nanofibers for orange juice quality assessment. Sens. Lett. 2009 , 7, 24–30.
147. Rudnitskaya, A.; Rocha, S.M.; Legin, A.; Pereira, V.; Marques, J.C. Evaluation of the feasibility
of the electronic tongue as a rapid analytical to ol for wine age predicti on and quantification of
the organic acids and phenolic com pounds. The case-study of Madeira wine. Anal. Chim. Acta
2010 , 662, 82–89.
148. Labrador, R.H.; Masot, R.; Alcañiz, M.; Baigts, D.; Soto, J.; Martínez-Mañez, R.; García-Breijo, E.;
Gil, L.; Barat, J.M. Prediction of NaCl, nitrate and nitrite contents in minced meat by using a
voltammetric electronic tongue and an impedimetric sensor. Food Chem. 2010 , 122, 864–870.

Sensors 2013 , 13 2331

149. Chen, Q.; Zhao, J.; Guo, Z.; Wang, X. Determin ation of caffeine content and main catechins
contents in green tea ( Camellia sinensis L.) using taste sensor technique and multivariate
calibration. J. Food Compos. Anal. 2010 , 23, 353–358.
150. Holscher, W.; Vitzthum, O.G.; Steinhart, H. Id entification and sensorial evaluation of aroma
impact-compounds in roasted Colombian coffee. Café Cacao Thé 1990 , 34, 205–212.
151. Holscher, W.; Vitzthum, O.G.; Steinhart, H. Prenyl alcohol—Source fo r odorants in roasted
coffee. J. Agric. Food Chem. 1992 , 40, 655–658.
152. Grosch, W. Flavour of coffee. Food 1998 , 42, 344–350.
153. Lindinger, C.; Labbe, D.; Pollien, P.; Rytz, A.; Ju illerat, M.A.; Yeretzian, C.; Blank, I. When
machine tastes coffee: Instrumental approach to predict the sensory profile of espresso coffee.
Anal. Chem. 2008 , 80, 1574–1581.
154. Hartman, J.D. A possible method for the ra pid estimation of flavours in vegetables. Proc. Am.
Soc. Hort. Sci. 1954 , 64, 335–342.
155. Ulmer, H.; Mitrovics, J.; Noet zel, G.; Wiemar, U.; Gopel, W. Odours and flavours identified
with hybrid modular sensor systems. Sens. Actuator B Chem. 1992 , 43, 24–33.
156. Gardner, J.W.; Pearce, T.C.; Frie l, S.; Bartlett, P.N.; Blair, N. A. Multisensor sy stem for beer
flavour monitoring using an array of conducti ng polymers and predic tive classifiers.
Sens. Actuator B Chem. 1994 , 18, 240–243.
157. Strassburger, K.J. Electronic nose evaluation in the flavor industry: It really works! Food Test.
Anal. 1996 , 2, 22–37.
158. Irmler, S.; Heusler, M.L.; Raboud, S.; Schlichthe rle-Cerny, H.; Casey, M.G.; Eugster-Meier, E.
Rapid volatile metabolite profiling of Lactobacillus casei strains: Selection of flavour producing
cultures. Aust. J. Dairy Technol. 2006 , 61, 123–127.
159. Beullens, K.; Meszaros, P.; Vermeir, S.; Kirsanov, D.; Legin, A.; Buysens, S.; Cap, N.;
Nicolai, B.M.; Lammertyn, J. Analysis of toma to taste using two type s of electronic tongues.
Sens. Actuator B Chem. 2008 , 131, 10–17.
160. Sun, H.; Mo, Z.H.; Choy, J.T.S.; Zhu, D.R.; Fung, Y.S. Piezoelectric quartz crystal sensor for
sensing taste-causing compounds in food. Sens. Actuator B Chem. 2008 , 131, 148–158.
161. Wang, B.; Xu, S.; Sun, D.W. Application of the elec tronic nose to the iden tification of different
milk flavorings. Food Res. Int. 2010 , 43, 255–262.
162. García-Martínez, T.; Bellincontro, A.; de Lerm a, M.D.L.N.L.; Peinado, R.A.; Mauricio, J.C.;
Mencarelli, F.; Moreno, J.J. Discrimination of sweet wines partially fermented by two
osmo-ethanol-tolerant yeasts by gas chroma tographic analysis and electronic nose. Food Chem.
2011 , 127, 1391–1396.
163. Calkin, R.R.; Jellinek, J.S. Perfumery: Practice and Principles ; Wiley-Interscience: New York,
NY, USA, 1994; pp. 273–304.
164. Hanaki, S.; Nakamoto, T.; Moriizumi, T. Artific ial odor recognition system using neural network
for estimating sensory quanti ties of blended fragrance. Sens. Actuator B Chem. 1996 , 57, 65–71.
165. Anselmi, C.; Centini, M.; Fede li, P.; Paoli, M.L.; Sega, A.; S cesa, C.; Pelosi, P. Unsaturated
hydrocarbons with fruity and floral odors. J. Agric. Food Chem. 2000 , 48, 1285–1289.

Sensors 2013 , 13 2332

166. Maul, F.; Sargent, S.A.; Balaban, M.O.; Baldwin, E.A.; Huber, D.J.; Sims, C.A. Aroma volatile
profiles from ripe tomatoes are influenced by phys iological maturity at harvest: An application
for electronic nose technology. J. Am. Soc. Hort. Sci. 1998 , 123, 1094–1101.
167. Supriyadi, S.; Shimuzu, K.; Suzuky, M.; Yosh ida, K.; Muto, T.; Fujita, A.; Tomita, N.;
Watanabe, N. Maturity discrimination of snake fruit ( Salacca edulis Reinw.) cv. Pondoh based
on volatiles analysis usi ng an electronic nose device equipped w ith a sensor array and fingerprint
mass spectrometry. Flavour Fragr. J. 2004 , 19, 44–50.
168. Falasconi, M.; Pardo, M.; Sber veglieri, G.; Riccò, I.; Brescian i, A. The novel EOS835 electronic
nose and data analysis for evaluating coffee ripening. Sens. Actuator B Chem. 2005 , 110, 73–80.
169. Pathange, L.P.; Mallikarjunan, P.; Marini, R. P.; O’Keefe, S.; Vaughan, D. Non-destructive
evaluation of apple maturity us ing an electronic nose system. J. Food Eng. 2006 , 77, 1018–1023.
170. Gòmez, A.H.; Hu, G.; Wang, J.; Pereira, A.G. Ev aluation of tomato maturity by electronic nose.
Comput. Electron. Agric. 2006 , 54, 44–52.
171. Lebrun, M.; Plotto, A.; Goodner, K.; Ducamp, M. N.; Baldwin, E. Discrimination of mango fruit
maturity by volatiles us ing the electronic nose and gas chromatography. Postharvest Biol. Technol.
2008 , 48, 122–131.
172. Du, X.; Bai, J.; Plotto, A.; Bald win, E.A.; Whitaker, V.; Souseff, R. Electronic nose for detecting
strawberry fruit maturity. Proc. Fla. State Hort. 2010 , 123, 259–263.
173. Pani, P.; Leva, A.A.; Riva, M.; Maestrelli, A. ; Torreggiani, D. Influence of an osmotic
pre-treatment on structure-pr operty relationships of air- dehydrated tomato slices. J. Food Eng.
2008 , 86, 105–112.
174. Defilippi, B.G.; Juan, W.S.; Valdés, H.; Moya-León, M.A.; Infante, R.; Campos-Vargas, R. The
aroma development during storage of Castlebrite apricots as ev aluated by gas chromatography,
electronic nose, and sensory analysis. Postharvest Biol. Tecnol. 2009 , 51, 212–219.
175. Söderström, C.; Borén, H.; Winquist, F.; Krantz-R ülcker, C. Use of an electronic tongue to
analyze mold growth in liquid media. Int. J. Food Microbiol. 2003 , 83, 253–261.
176. Perkowski, J.; Bu śko, M.; Chmielewski, J.; Góral, T.; Tyra kowska, B. Content of trichodiene and
analysis of fungal volatiles (electronic nose) in wheat and triticale grain naturally infected and
inoculated with Fusarium culmorum . Int. J. Food Microbiol. 2008 , 126, 127–134.
177. Pisanelli, A.M.; Qutob, A.A.; Travers, P.; Szys zko, S.; Persaud, K.C. Applications of multi-array
polymer sensors to food industries. Life Chem. Rep. 1994 , 11, 303–308.
178. Persaud, K.C.; Qutob, A.A.; Travers, P.; Pisanell i, A.M.; Szyszko, S. Odor evaluation of foods
using conducting polymer arrays and neural net pattern recognition. In Olfaction and Taste XI ;
Kurihara, K., Suzuki, N., Ogawa, H., Eds. ; Springer-Verlag: Tokyo, Japan, 1994; pp. 708–710.
179. Bartlett, P.N.; Elliott, J.M.; Gardner, J.W. Elec tronic noses and their applications in the food
industry. Food Technol. 1997 , 51, 44–48.
180. Schaller, E.; Bosset, J.O.; Esher, F. El ectronic noses and thei r application to food. LWT Food Sci.
Technol. 1998 , 31, 305–316.
181. Deisingh, A.K.; Stone, D.C.; Thompson, M. Applicat ions of electronic nose s and tongues in food
analysis. Int. J. Food Sci. Technol. 2004 , 39, 587–604.
182. Berrueta, L.A.; Alonso-Salces , R.M.; Heberger, K. Supervis ed pattern recognition in food
analysis. J. Chromatogr. A 2007 , 1158 , 196–214.

Sensors 2013 , 13 2333

183. Scampicchio, M.; Ballabio, D.; Arecchi, A.; Co sio, S.M.; Mannino, S. Ampe rometric electronic
tongue for food analysis. Microchim. Acta 2008 , 163, 11–21.
184. Peres, A.; Dias, L.; Barcelos, T.; Sa Morais, J. ; Machado, A. An electronic tongue for juice level
evaluation in non-alcoholic beverages. Proc. Chem. 2009 , 1, 1023–1026.
185. Dias, L.G.; Peres, A.M.; Barcelos, T.P.; Sá Mo rais, J.; Machado, A.A.S.C. Semi-quantitative and
quantitative analysis of soft dr inks using an electronic tongue. Sens. Actuator B Chem. 2011 ,
154, 111–118.
186. Iiyama, S.; Ezaki, S.; Toko, K. Sensitivity-im provement of taste sensor by change of lipid
concentration in membrane. Sens. Actuator B Chem. 2009 , 141, 343–348.
187. Berdagué, J.L.; Talou, T. Examples of appl ications for meat products of semiconductor gas
sensors. Sci. Aliments 1993 , 13, 141–148.
188. Grigioni, G.M.; Margar ía, C.A.; Pensel, N.A.; S ánchez, G.; Vaudagna, S.R. Warmed-over
flavour analysis in low temperature-long tim e processed meat by an “electronic nose”. Meat Sci.
2000 , 56, 221–228.
189. Russell, P. Sensory analysis. Milk Ind. 1995 , 97, 11–12.
190. Romero, G.; Díaz, J.R.; Sabater, J.M.; Perez, C. Evaluation of commercial probes for on-line
electrical conductivity m easurements during goat gland milking process. Sensors 2012 , 12,
4493–4513.
191. Lerma-Garcia, M.J.; Simo-Alfonso, E.F.; Bendini , A.; Cerretani, L. Metal oxide semiconductor
sensors for monitoring of oxidative status evolution and sensory analysis of virgin olive oils with
different phenolic content. Food Chem. 2009 , 117, 608–614.
192. Mildner-Szkudlarz, S.; Jelen, H.H. Detection of olive oil adulteration with rapeseed and
sunflower oils using MOS electronic nose and SMPE-MS. J. Food Qual. 2010 , 33, 21–41.
193. Zhang, C.; Suslick, K.S. Colorimetric sensor array for soft drink analysis. J. Agric. Food Chem.
2007 , 55, 237–242.
194. Ohata, M.; Tominaga, T.; Dubourdieu, D.; Kubot a, K.; Sugawara, E. Quantification and odor
contribution of 2-furanmethanethiol in differe nt types of fermented soybean paste miso. J. Agric.
Food Chem. 2009 , 57, 2481–2485.
195. Zhang, H.; Balaban, M.; Portier, K.; Sims, C.A. Quantification of spice mixture compositions by
electronic nose: Part II. Comparis on with GC and sensory methods. J. Food Sci. 2005 , 70,
E259–E264.
196. Rajamäki, T.; Alatomi, H.; Titvanen, T.; Skyttä , E.; Smolander, M.; Ahvenainen, R. Application
of an electronic nose for quality assessment of modified atmosphere packaged poultry meat.
Food Control 2004 , 17, 5–13.
197. Echeverria, G.; Graell, J.; Lop ez, M.L.; Brezmes, J.; Correig, X. Volatile production in “Fuji”
apples stored under different atmospheres m easured by headspace/gas chromatography and
electronic nose. Acta Hort. 2005 , 682, 1465–1470.
198. Arrieta, A.; Rodriguez-Mendez, M.L.; de Sa ja, J.A. Langmuir-Blodgett film and carbon paste
electrodes based on phthalocyanines as sensing units for taste. Sens. Actuator B Chem. 2003 , 95,
357–365.

Sensors 2013 , 13 2334

199. Daillant-Spinnler, B.; MacFie, H.J.H.; Beyts, P.K.; Hedderley, D. Relationships between
perceived sensory properties and major preference di rections of 12 varieties of apples from the
Southern Hemisphere. Food Qual. Prefer. 1996 , 7, 113–126.
200. Bartoshuk, L.M. Comparing se nsory experiences across indi viduals: Recent psychophysical
advances illuminate genetic va riation in taste perception. Chem. Sens. 2000 , 25, 447–460.
201. Bleibaum, R.N.; Stone, H.; Tan, T.; Labreche, S.; Saint-Martin, E.; Isz, S. Comparison of
sensory and consumer results with electronic nose and tongue sensors for apple juices. Food
Qual. Prefer. 2002 , 13, 409–422.
202. Hayasaka, Y.; Baldock, G.A.; Pollnitz, A.P. Contributions of mass spectrometry in The
Australian Wine Research Instit ute to advances in knowledge of grape and wine constituents.
Aust. J. Grape Wine Res. 2005 , 11, 188–204.
203. Falcão, L.D.; de Revel, G.; Rosier, J.P.; Bo rdignon-Luiz, M.T. Aroma impact components of
Brazilian Cabernet Sauvignon wine s using detection frequency an alysis (GC-olfactometry).
Food Chem. 2008 , 107, 497–505.
204. Vera, L.; Mestres, M.; Boqué, R.; Busto, O.; Guasc h, J. Use of synthetic wine for models transfer
in wine analysis by HS-MS e-nose. Sens. Actuator B Chem. 2010 , 143, 689–695.
205. Santos, J.P.; Lozano, J.; Aleixandre, M.; Arroyo, T.; Cabellos, J.M.; Gil, M.; Horrillo, M.D.C.
Threshold detection of aromatic compounds in wi ne with an electronic nose and a human sensory
panel. Talanta 2010 , 80, 1899–1906.
206. Aguilera, T.; Lozano, J.; Paredes, J.A.; Álvare z, F.J.; Suárez, J.I. Electronic nose based on
independent component analysis combined with partial least squares and artificial neural
networks for wine prediction. Sensors 2012 , 12, 8055–8072.
207. Shurmer, H.V.; Gardner, J.W.; Chan, H.T. Th e application of discrimination techniques to
alcohols and tobacco using tin oxide sensors. Sens. Actuator B Chem. 1989 , 18, 359–369.
208. Aishima, T. Discrimination of liqueur aromas by pattern recognition analysis of responses from a
gas sensor array. Anal. Chim. Acta 1991 , 243, 293–300.
209. Solís-Solís, H.M.; Calderón-Santoyo, M.; Gutierrez-Martinez, P.; Schorr-Galindo, S.;
Ragazzo-Sánchez, J. Discrimination of eight varieties of apricot ( Prunus armeniaca L.) by
electronic nose LLE and SPME using GC-MS and multivariate analysis. Sens. Actuator B Chem.
2007 , 125, 415–421.
210. Gatti, E.; Defilippi, B.G.; Predieri, S.; Infante, R. Apricot ( Prunus armeniaca L.) quality and
breeding perspectives. J. Food Agric. Environ. 2009 , 7, 573–580.
211. Ponzoni, A.; Depari, A.; Falasconi, M.; Comini , E.; Flammini, A.; Marioli, D.; Taroni, A.;
Sberveglieri, G. Bread baking aromas de tection by low-cost electronic nose. Sens. Actuator B
Chem. 2008 , 130, 100–104.
212. Wu, R.J.; Yeh, C.H.; Yu, M.R.; Chen, H.W. Applica tion of taste sensor array to sports drinks by
using impedance measurement technology. Sens. Lett. 2008 , 6, 765–770.
213. Rodriguez-Mendez, M.L.; Apetrei, C.; de Saja, J.A. Evaluation of the polyphenolic content of extra
virgin olive oils using an a rray of voltammetric sensors. Electrochim. Acta 2008 , 53, 5867–5872.
214. Ciosek, P.; Kraszewska, Z.; Wroblenski, W. Polyurethane membranes used in integrated
electronic tongue for the recogni tion of tea and herbal products. Electroanalysis 2009 , 21,
2036–2043.

Sensors 2013 , 13 2335

215. Ding, F.; Liu, B.; Deng, X.; Wang, Z.; Xie, Z.; Fang, Y.; Xu, J. Delayed bitterness of six sweet
oranges ( Citrus sinensis Osbeck). J. Huazhong Agric. Univ. 2010 , 29, 497–501.
216. Li, Z.; Vijaya Raghavan, G.S.; Wang, N. Carrot volatiles monitoring and control in microwave
drying. LWT Food Sci. Technol. 2009 , 43, 291–297.
217. Gursoy, O.; Somervuo, P.; Alatossava, T. Prel iminary study of ion m obility based electronic
nose MGD-1 for discrimination of hard cheeses. J. Food Eng. 2009 , 92, 202–207.
218. Pais, V.F.; Oliveira, J.A.B.P.; Gomes, M.T.S.R. An electronic nose based on coated piezoelectric
quartz crystals to certify ewes’ cheese and to discriminate between cheese varieties. Sensors
2012 , 12, 1422–1436.
219. Ravi, R.; Harte, J.B. Milling and physicochemical properties of chickpea ( Cicer arietinum L.)
varieties. J. Sci. Food Agric. 2009 , 89, 258–266.
220. Goodner, K.; Baldwin, E.A.; Jordan, M.; Shaw, P.E. The comparison of an electronic nose and
gas chromatograph of differe ntiating NFC orange juices. Proc. Fla. State Hort. 2002 , 114,
158–160.
221. Reinhard, H.; Sager, F.; Zoller, O. Citrus juice classification by SPM E-GC-MS and electronic
nose measurements. LWT Food Sci. Technol. 2008 , 41, 1906–1912.
222. Gardner, J.W.; Shurmer, H.V.; Tan, T.T. App lication of an artificia l electronic nose to the
discrimination of coffee. Sens. Actuator B Chem. 1992 , 6, 71–75.
223. Suslick, B.A.; Feng, L.; Suslick, K.S. Discrimi nation of complex mixtures by a colorimetric
sensor array: Coffee aromas. Anal. Chem. 2010 , 82, 2067–2073.
224. Rodriguez, J.; Duran, C.; Reyes, A. Electroni c nose for quality control of Colombian coffee
through the detection of defects in “Cup Tests”. Sensors 2010 , 10, 36–46.
225. Oliveri, P.; Baldo, M.A.; Daniele, S.; Forina , M. Development of a voltammetric electronic
tongue for discrimination of edible oils. Anal. Bioanal. Chem. 2009 , 395, 1135–1143.
226. Van Ruth, S.M.; Rozijn, M.; Koot, A.; Garcia, R.P.; van der Kamp, H.; Codony, R.
Authentication of feeding fats: Cl assification of animal fats, fish oils and recycled cooking oils.
Anim. Feed Sci. Technol. 2010 , 155, 65–73.
227. Cano, M.; Roales, J.; Castillero, P.; Mendoza, P.; Calero, A.M.; Jim énez-Ot, C.; Pedrosa, J.M.
Improving the training and data pr ocessing of an electr onic olfactory system for the classification
of virgin olive oil into quality categories. Sens. Actuator B Chem. 2011 , 160, 916–922.
228. Hong, E.J.; Park, S.J.; Choi, J.Y.; Noh, B.S. Disc rimination of palm olein oil and palm stearin oil
mixtures using a mass spectrometry based electronic nose. Food Sci. Biotechnol. 2011 , 20,
809–816.
229. Vernat-Rossi, V.; Vernat, G.; Berdagué, J.L. Di scrimination of agroalimentary products by gas
sensors with semiconductors functioning with ambi ent air of the laborato ry. Various approaches
of signal treatment. Analysis 1996 , 24, 309–315.
230. Jonsson, A.; Winquist, F.; Schnürer, J.; Sundgren, H.; Lundtröm, I. Electronic nose for microbial
quality classification of grains. Int. J. Food Microbiol. 1997 , 35, 187–193.
231. Wei, Z.; Wang, J.; Liao, W. Technique potential for classification of honey by electronic tongue.
J. Food Eng. 2009 , 94, 260–266.

Sensors 2013 , 13 2336

232. Zakaria, A.; Shakaff, A.Y.; Masnan, M.J.; Ahmad, M.N.; Adom, A.H.; Jaafar, M.N.; Ghani, S.A.;
Abdullah, A.H.; Aziz, A. H.A.; Kamarudin, L.M.; et al. A biomimetic sensor fo r the classification of
honeys of different floral origin and the detection of adulteration. Sensors 2011 , 11, 7799–7822.
233. Ciosek, P.; Wroblewski, W. Mi niaturized electronic tongue w ith an integrated reference
microelectrode for the recognition of milk samples. Talanta 2008 , 76, 548–556.
234. Men, H.; Ge, Z.; Guo, Y.; An, L.; Peng, Y. Biomim etic electronic tongue for classification of
mineral water. In Proceedings of International Conferen ce on Civil Engineering, Architecture
Measuring Technology and Mechatronics Automation ; Zhangjiajie, Hunan, China, 11–12 April
2009; pp. 621–624.
235. Sghaier, K.; Barhoumi, H.; Maaref, A.; Siadat , M.; Jaffrezic-Renault, N. Classification and
discrimination of different Tunisian wa ter samples using an electronic tongue. Sens. Lett. 2009 ,
7, 683–688.
236. Oshita, S.; Shima, K.; Haruta, T.; Seo, Y. ; Kawagoe, Y.; Nakayama, S.; Takahara, H.
Discrimination of odors emanating from “La Fr ance” pear by semi-conducting polymer sensors.
Comput. Electron. Agric. 2000 , 26, 209–216.
237. Zheng, X.Z.; Lan, Y.B.; Zhu, J.M.; Westbr ook, J.; Hoffmann, W.C.; Lacey, R.E. Rapid
identification of rice samples using an electronic nose. J. Bionic Eng. 2009 , 6, 290–297.
238. Gasso-Tortajada, V.; Ward, A.J.; Mansur, H.; Brøchner, T.; Sørensen, C.G.; Green, O. A novel
acoustic sensor approach to classify seeds based on sound absorption spectra. Sensors 2010 , 10,
10027–10039.
239. Palit, M.; Tudu, B.; Dutta, P.K.; Dutta, A.; Jana, A.; Roy, J.K.; Bhattacharyya, N. Classification
of black tea taste and correlation with tea tast er’s mark using voltammetric electronic tongue.
IEEE Trans. Instrum. Meas. 2009 , 10, 1–10.
240. Xiao, H.; Wang, J. Discrimina tion of Xihulongjing tea grade using an electronic tongue. Afr. J.
Biotechnol. 2009 , 8, 6985–6992.
241. Scarpa, A.; Bernardi, S.; Fachechi, L.; Olim pico, F.; Passamano, M.; Greco, S. Polypyrrole
polymers used for 2,4,6-trichloroanisole discri mination in cork stoppers by LibraNose.
In Proceedings of the 11th Meeting of Chemical Societies , Brescia, Italy, 2–7 July 2006.
242. McKellar, R.C.; Vasantha Rupasinghe, H.P.; Lu, X.; Knight, K.P. The electronic nose as a tool
for the classification of fruit and grape wines from differen t Ontario wineries. J. Sci. Food Agric.
2005 , 85, 2391–2396.
243. Fu, J.; Huang, C.; Xing, J.; Zhe ng, J. Pattern classification usi ng an olfactory model with PCA
feature selection in electronic noses: St udy and application. Sensors 2012 , 12, 2818–2830.
244. Parra, V.; Arrieta, A.A.; Fernandez-Esc udero, J.A.; Garcia, H.; Apetrei, C.;
Rodriguez-Mendez, M.L.; Saja, J.A. E-tongue ba sed on a hybrid array of voltammetric sensors
based on phthalocyanines, perylene derivati ves and conducting polymers: Discrimination
capability towards red wines elaborated with different varieties of grapes. Sens. Actuator B
Chem. 2006 , 115, 54–61.
245. Lozano, J.; Santos, J.P.; Carmen Horrillo, M. Enrichment sampling methods for wine
discrimination with gas sensors. J. Food Compost. Anal. 2008 , 21, 716–723.

Sensors 2013 , 13 2337

246. Cynkar, W.; Dambergs, R.; Smith, P.; Cozzoli no, D. Classification of Tempranillo wines
according to geographic origin: Combination of mass spectrometry based electronic nose and
chemometrics. Anal. Chim. Acta 2010 , 660, 227–231.
247. Musto, C.J.; Lim, S.H.; Suslick, K.S. Colorimetr ic detection and identi fication of natural and
artificial sweeteners. Anal. Chem. 2009 , 81, 6526–6533.
248. Jonsdottir, R.; Olafsdottir, G.; Martinsdottir, E. ; Stefansson, G. Flavor ch aracterization of ripened
cod roe by gas chromatography; sensor y analysis; and electronic nose. J. Agric. Food Chem.
2004 , 52, 6250–6256.
249. Trihaas, J.; Nielsen, V. El ectronic nose technology in quality assessment: Monitoring the
ripening process of Danish blue cheese. J. Food Sci. 2005 , 70, E44–E49.
250. Biolatto, A.; Grigioni, G.; Irurueta, M.; Ra ncho, A.M.; Taverna, M.; Pensel, N. Seasonal
variation in the odour characteri stics of whole milk powder. Food Chem. 2007 , 103, 960–967.
251. Vestergaard, J.S.; Martens, M.; Turkki, P. Anal ysis of sensory quality changes during storage of
a modified atmosphere packaged meat product (p izza topping) by an electronic nose system.
LWT Food Sci. Technol. 2007 , 40, 1083–1094.
252. Vestergaard, J.S.; Martens, M.; Turkky, P. Application of an electronic nose system for
prediction of sensory quality changes of a meat product (pizza topping) during storage. LWT
Food Sci. Technol. 2007 , 40, 1095–1101.
253. Cacic, F.; Primorac, L.; Kenjeric, D.; Benedetti, S.; Madic, M.L. Applic ation of electronic nose
in honey geographical or igin characterization. J. Cent. Eur. Agric. 2009 , 10, 19–26.
254. Casale, M.; Casolino, C.; Oliveri, P.; Forina, M. The potential of co upling information using
three analytical techniques for identifying the ge ographical origin of Liguria extra virgin olive
oil. Food Chem. 2010 , 118, 163–170.
255. Berna, A.Z.; Trowell, S.; Clifford, D.; Cynka r, W.; Cozzolino, D. Geographical origin of
Sauvignon Blanc wines pred icted by mass spectrometry and meta l oxide based electronic nose.
Anal. Chim. Acta 2009 , 648, 146–152.
256. He, W.; Hu, X.; Zhao, L.; Liao, X.; Zhang, Y.; Zhang, M.; Wu, J. Evaluation of Chinese tea by
the electronic tongue: Correla tion with sensory properties a nd classification according to
geographical origin and grade level. Food Res. Int. 2009 , 42, 1462–1467.
257. Paixao, T.R.L.C.; Bertotti, M. Fabrication of disposable voltammetric electronic tongues by
using Prussian blue films electrodeposited ont o CD-R gold surfaces and recognition of milk
adulteration. Sens. Actuator B Chem. 2009 , 137, 266–273.
258. Dias, L.A.; Peres, A.M.; Veloso, A.C.A.; Reis , F.S.; Vilas-Boas, M.; Machado, A.A.S.C. An
electronic tongue taste evaluation: Identification of goat milk adulteration with bovine milk.
Sens. Actuator B Chem. 2009 , 136, 209–217.
259. Marina, A.M.; Man, Y.B.C.; Amin, I. Use of th e SAW sensor electronic nose for detecting the
adulteration of virgin coconut oi l with RBD palm kernel olein. J. Am. Oil Chem. Soc. 2010 , 87,
263–270.
260. Hilding-Ohlsson, A.; Fauerbach, J.A.; Sacco, N. J.; Bonetto, M.C.; Cortón, E. Voltamperometric
discrimination of urea and melamine adulterated skimmed milk powder. Sensors 2012 , 12,
12220–12234.

Sensors 2013 , 13 2338

261. Ahn, S.; Walt, D.R. Detection of Salmonella spp. using microsphere-based, fiber-optic DNA
microarrays. Anal. Chem. 2005 , 77, 5041–5047.
262. Balasubramanian, S.; Panigrahi, S.; Logue, C. M.; Doetkott, C.; Marchello, M.; Sherwood, J.S.
Independent component analysis-processe d electronic nose data for predicting Salmonella
typhimurium populations in contaminated beef. Food Control 2008 , 19, 236–246.
263. Wang, Y.; Ye, Z.; Ying, Y. New trends in impedime tric biosensors for the detection of foodborne
pathogenic bacteria. Sensors 2012 , 12, 3449–3471.
264. Winquist, F.; Sundgren, H.; Lundstrom, I. A practic al use of electronic nose: quality estimation
of cod fillet bought over the counter. In Proceedings of The 8th International Conference on
Solid-State Sensors and Ac tuators and Eurosensors IX ; Stockholm, Sweden, 25–29 June 1995;
Volume 1, pp. 695–698.
265. Olafsdottir, G.; Chanie, E.; Westad, F.; Jonsdo ttir, R.; Thalmann, C.R.; Bazzo, S.; Labreche, S.;
Marcq, P.; Lunby, F.; Haugen, J.E. Prediction of mi crobial and sensory quality of cold smoked
Atlantic salmon ( Salmo salar ) by electronic nose. J. Food Sci. 2005 , 70, S563–S574.
266. Olafsdottir, G.; Jonsdottir, R.; Lauzon, H.L.; Luten, J.; Kristbergsson, K. Characterization of
volatile compounds in chilled cod ( Gadus morhua ) fillets by gas chromatography and detection
of quality indicators by an electronic nose. J. Agric. Food Chem. 2005 , 53, 10140–10147.
267. Haugen, J.E.; Chanie, E.; Westad, F.; Jonsdottir, R.; Bazzo, S.; Labreche, S.; Marcq, P.; Lundby, F.;
Olafsdottir, G. Rapid control of smoked Atlantic salmon ( Salmo salar ) quality by electronic
nose: correlation with classical evaluation methods. Sens. Actuator B Chem. 2005 , 116, 72–77.
268. Barat, J.M.; Gil, L.; Garcia-Brejio, E.; Aristo ry, M.; Toldra, F.; Martinez-manez, R.; Soto, J.
Freshness monitoring of sea bream ( Sparus aurata ) with a potentiometric sensor. Food Chem.
2008 , 108, 681–688.
269. Peris, M.; Escuder-Gilabert, L. A 21st century technique for f ood control: Electronic noses.
Anal. Chim. Acta 2009 , 638, 1–15.
270. Ghasemi-Varnamkhasti, M.; Mohtasebi, S.S.; Siadat, M. Biomimetic-based odor and taste
sensing systems to food quality and safety charac terization: An overview on basic principles and
recent achievements. J. Food Eng. 2010 , 100, 377–387.
271. Bai, J.; Baldwin, E.A.; Soliva Fortuny, R.C.; Mattheis, J.P.; Stanley, R.; Perera, C.; Brecht, J.K.
Effect of pretreatment of inta ct “Gala” apple with ethanol vapor , heat, or 1-methylcyclopropene
on quality and shelf life of fresh-cut slices. J. Am. Soc. Hort. Sci. 2004 , 129, 583–593.
272. Plotto, A.; Baldwin, E.; McCollum, G.; Manthey, J.; Narciso, J.; Irey, M. Effect of Liberibacter
infection (Huanglongbing or “Greening” disease) of citrus on orange juice flavor quality by
sensory evaluation. J. Food Sci. 2010 , 75, S220–S230.
273. Li, C.; Heinemann, P.; Sherry, R. Neural networ k and Bayesian network fusion models to fuse
electronic nose and surface acoustic wave se nsor data for apple defect detection. Sens. Actuator
B Chem. 2007 , 125, 301–310.
274. Winquist, F.; Hornsten, E.G.; Sundgren, H.; Lundstr om, I. Performance of an electronic nose for
quality estimation of ground meat. Meas. Sci. Technol. 1993 , 4, 1493–1500.
275. Ghasemi-Varnamkhasti, M.; Mohtasebi, S.S.; Siadat, M.; Balasubramanian, S. Meat quality
assessment by electronic nose (m achine olfaction technology). Sensors 2009 , 9, 6058–6083.

Sensors 2013 , 13 2339

276. Sangam, V.G.; Sandesh, M.; Krishna, S.; Mahade vanna, S. Design of simple instrumentation for
the quality analysis of milk (casein analysis). Sci. Technol. 2010 , 119, 65–71.
277. Shen, N.; Moizuddin, S.; Wilson, L.; Duvick, S. White, P.; Pollak, L. Relationship of electronic
nose analyses and sensory evaluation of vegetable oils during storage. J. Am. Oil Chem. Soc.
2001 , 78, 937–940.
278. Ragazzo-Sanchez, J.A.; Chalier, P.; Cheva lier-Lucia, D.; Calderon-Santoyo, M.; Ghommidh, C.
Off-flavours detection in alcoholic beve rages by electronic no se coupled to GC. Sens. Actuator B
Chem. 2009 , 140, 29–34.
279. Fujita, A.; Isogai, A.; Endo, M.; Utsunomiya, H.; Na kano, S.; Iwata, H. Effects of sulfur dioxide
on formation of fishy off-odor and undesirable taste in wine consumed with seafood. J. Agric.
Food Chem. 2010 , 58, 4414–4420.
280. Simon, J.E.; Hetzroni, A.; Bordelon, B.; Miles, G. E.; Charles, D.J. Electro nic sensing of aromatic
volatiles for quality sorting of blueberries. J. Food Sci. 1996 , 61, 967–972.
281. Costa, G.; Noferini, M.; Montefiori, M.; Brig ati, S. Non-destructive assessment methods of
kiwifruit quality. Acta Hort. 2003 , 610, 179–189.
282. Benedetti, S.; Toppino, P.M.; Riva, M. Study of the shelf life of manufactured Taleggio cheese:
2. Applications of the electronic nose. Sci. Technol. Lattiero Casearia 2002 , 53, 259–282.
283. Riva, M.; Benedetti, S.; Mannino, S. Shelf lif e of fresh cut vegetables as measured by an
electronic nose: Preliminary study. Ital. Food Sci. 2002 , 27, 5–11.
284. Riva, M.; Benedetti, S.; Sinelli, N. Combined techniques of NIRS and Electronic Nose for the
study of the shelf life of la ttiero-caseari products. Ingred. Aliment. 2004 , 3, 19–23.
285. Niruntasuk, K.; Innawong, B.; Pa rakulsulsatid, P. Shelf life de termination of vacuum fried
mango chips using electronic nose. In The Proceedings of the 44th Kasetsart University Annual
Conference , Kasetsart, Thailand, 30 Ja nuary–2 February 2006; pp. 200–209.
286. Labreche, S.; Bazzo, S.; Cade, S.; Chanie, E. Shelf life determination by electronic nose:
Application to milk. Sens. Actuat or B Chem. 2005 , 106, 199–206.
287. Chantarachoti, J.; Oliveira, A.C.M.; Himelbloom, B.H. ; Crapo, C.A.; McLachlan, D.G.
Portable electronic nose for detection of spoiling Alaska pink salmon (Oncorhynchus
gorbuscha) . J. Food Sci. 2006 , 71, S414–S421.
288. Casalinuovo, I.A.; di Pierro, D.; Coletta, M.; di Francesco, P. Application of electronic noses for
disease diagnosis and food spoilage detection. Sensors 2006 , 6, 1428–1439.
289. Gómez, A.H.; Wang, J.; Hu, G.; García Pereira, A. Monitoring storage shelf life of tomato using
electronic nose technique. J. Food Eng. 2008 , 85, 625–631.
290. Wang, Y.; Wang, J.; Zhou, B.; Lu, Q. Monitoring st orage time and quality at tribute of egg based
on electronic nose. Anal. Chim. Acta 2009 , 650, 183–188.
291. Limbo, S.; Torri, L.; Sinelli, N. ; Franzetti, L.; Casiraghi, E. Ev aluation and predictive modeling
of shelf life of minced beef st ored in high-oxygen modified atmo sphere packaging at different
temperatures. Meat Sci. 2010 , 84, 129–136.
292. Torri, L.; Sinelli, N.; Limbo, S. Shelf life evaluation of fresh-cut pi neapple by using an electronic
nose. Postharvest Biol. Tecnol. 2010 , 56, 239–245.

Sensors 2013 , 13 2340

293. Argyri, A.A.; Panagou, E.Z.; Tarantilis, P.A.; Poly siou, M.; Nychas, G.J.E. Rapid qualitative and
quantitative detection of beef fillets spoilage based on Fourier transform infrared spectroscopy
data and artificial neural networks. Sens. Actuat or B Chem. 2010 , 145, 146–154.
294. Gil, L.; Barat, J.M.; Garcia-Breijo, E.; Ibañez, J.; Martínez-Máñez, R.; Soto, J.; Llobet, E.;
Brezmes, J.; Aristoy, M.C.; Toldráe, F. Fish fre shness analysis using metallic potentiometric
electrodes. Sens. Actuat B Chem. 2008 , 131, 362–370.
295. Ólafsson, R.; Martinsd όttir, E.; Ólafsd όttir, G.; Sigfússon, T.I.; Gardne r, J.W. Monitoring of fish
freshness using tin oxide sensors. In Sensors and Sensory Systems for an Electronic Nose ;
Gardner, J.W., Bartlett, P.N., Eds.; Kluw er: Dordrecht, The Netherlands, 1992; pp. 257–272.
296. Schweizer-Berberich, P.M.; Vaihinger, S.; Gope l, W. Characterisation of food freshness with
sensor arrays. Sens. Actuator or B Chem. 1994 , 18, 282–290.
297. Davide, F.A.M.; di Natale, C.; D’Amico, A. Self -organizing sensory maps in odour classification
mimicking. Biosens. Bioelectron. 1995 , 10, 203–218.
298. Di Natale, C.; Davide, F.A.M.; D’Amico, A.; Sber veglieri, G.; Nelli, P. ; Faglia, G.; Perego, C.
Complex chemical pattern recogniti on with sensor array: The disc rimination of vintage years of
wine. Sens. Actuat or B Chem. 1995 , 25, 801–804.
299. Di Natale, C.; Brunink, J.A.J.; Bungaro, F. Recognition of fish storage time by a
metalloporphorins-coated QMB sensor array. Meas. Sci. Technol. 1996 , 7, 1103–1114.
300. Egashira, M. Functional design of semiconducto r gas sensors for measurement of smell and
freshness. In Proceedings of The International Conference on Solid-State Sensors and Actuators ,
Chicago, IL, USA, 16–19 June 1997; pp. 1385–1388.
301. Du, W.-X.; Lin, C.-M.; Huang, T.; Kim, J.; Marsha ll, M.; Wei, C.-I. Potent ial application of the
electronic nose for quality assessment of salm on filets under variou s storage conditions. J. Food Sci.
2002 , 67, 307–313.
302. Zhang, H.; Wang, J.; Tian, X.; Yu, H.; Yu, Y. Op timization of sensor array and detection of
stored duration of wheat by electronic nose. J. Food Eng. 2007 , 82, 403–408.
303. Limbo, S.; Sinelli, N.; Torri, L.; Riva, M. Fres hness decay and shelf life predictive modelling of
European sea bass ( Dicentrarchus labrax ) applying chemical methods and electronic nose.
LWT Food Sci. Technol. 2009 , 42, 977–984.
304. Barbri, N.E.; Mirhisse, J.; Ionescu, R.; Bari, N.E.; Correig, X.; Bouchikhi, B.; Llobet, E. An
electronic nose system based on a micro-machined gas sensor array to assess the freshness of
sardines. Sens. Actuat or B Chem. 2009 , 141, 538–543.
305. Musatov, V.Y.; Sysoev, V.V.; Sommer, M.; Kiselev, I. Assessment of meat freshness with oxide
sensor microarray electronic nose: A practical approach. Sens. Actuat or B Chem. 2010 , 144, 99–103.
306. Tian, X.-Y.; Cai, Q.; Zhang, Y. -M. Rapid classification of hairta il fish and pork freshness using
an electronic nose based on the PCA method. Sensors 2012 , 12, 260–277.
307. Ampuero, S.; Zesiger, T.; Gustafsson, V.; Lunden, A.; Bosset, J.O. Determination of
trimethylamine in milk using an MS based electronic nose. Eur. Food Res. Technol. 2002 , 214,
163–167.
308. Campagnoli, A.; Pinotti, L.; Tognon, G.; Cheli, F. ; Baldi, A.; Dell’Orto, V. Potential application
of electonic nose in processed animal pr oteins (PAP) detection in feedstuffs. Biotechnol. Agron. Soc.
2004 , 8, 253–255.

Sensors 2013 , 13 2341

309. Logrieco, A.; Arrigan, D.W.M.; Brengel-Pes ce, K.; Siciliano, P.; Tothill, I. DNA arrays,
electronic noses and tongues, biosensors and rece ptors for rapid detection of toxigenic fungi and
mycotoxins: A review. Food Addit. Contam. 2005 , 22, 335–344.
310. Zhang, S.; Xie, C.; Bai, Z.; Hu, M.; Li, H. ; Zeng, D. Spoiling and formaldehyde-containing
detections in octopus with an E-nose. Food Chem. 2009 , 113, 1346–1350.
311. Chobta ng, J.; de Boer, I.J.M.; Hooge nboom, R.L.A.P.; Haasnoot, W.; Kijlstra, A.; Meerburg, B.G.
The need and potential of biosensors to detect dioxins and dioxin-like polychlorinated biphenyls
along the milk, eggs, and meat food chain. Sensors 2011 , 11, 11692–11716.
312. Baranauskien, R.; Bylait, E.; Ukauskait, J.; Ve nskutonis, R.P. Flavor retention of peppermint
(Mentha piperita L.) essential oil spray dried in modi fied starches duri ng encapsulation and
storage. J. Agric. Food Chem. 2007 , 55, 3027–3036.
313. Köster, E.P. Does olfactory me mory depend on remembering odors? Chem. Sens. 2005 , 30,
i236–i237.
314. Köster, M.A.; Prescott, J.; Köster, E.P. Incidental learning and memory fo r three basic tastes in
food. Chem. Sens. 2004 , 29, 441–453.
315. Le Berre, E.; Thomas-Danguin, T.; Béno, N.; Courea ud, G.; Etiévant, P.; Prescott, J. Perceptual
processing strategy and exposure influe nce the perception of odor mixtures. Chem. Sens. 2008 ,
33, 193–199.
316. Murphy, C.; Cain, W.S.; Bartoshuk, L.M. Mutual action of taste and olfaction. Sens. Process.
1977 , 1, 204–211.
317. Murphy, C.; Cain, W.S. Taste and olfaction: independence vs. interaction. Physiol. Behav. 1980 ,
24, 601–605.
318. Auvray, M.; Spence, C. The multisensory perception of flavor. Conscious. Cogn. 2008 , 17,
1016–1031.
319. Lee, S.; Park, T. Recent advances in the development of bioelectronic nose. Biotechnol. Bioproc.
Eng. 2010 , 15, 22–29.
320. Baldwin, E.A.; Bai, J.; Plotto, A.; Dea, S. Elec tronic noses and tongues: Application for the food
and pharmaceutical industries. Sensors 2011 , 11, 4744–4766.
321. Davis, T.W.; Kuo, C.-M.; Liang, X.; Yu, P.-S. Sap flow sensors: Cons truction, quality control
and comparison. Sensors 2012 , 12, 954–971.
322. Contreras-Medina, L.M.; Os ornio-Rios, R.A.; Torres-Pach eco, I.; Romero-Troncoso, R.;
Guevara-Gonz ález, R.G.; Millan-Almaraz, J.R. Smart sensor for real-time quantification of
common symptoms present in unhealthy plants. Sensors 2012 , 12, 784–805.
323. Baietto, M.; Wilson, A.D. Relative in vitro wood decay resistance of sapwood from landscape
trees of southern temperate regions. Hort. Sci. 2010 , 45, 401–408.
324. Garneau, F.X.; Riedl, B.; Hobbs, S.; Pichette, A.; Gagnon, H. The use of sensor array technology
for rapid differentiation of the sapwood and heartw ood of Eastern Canadian spruce; fir and pine.
Holz Roh Werkst. 2004 , 62, 470–473.
325. Murphy, G.; Franich, R. Early experience with aroma tagging and electronic nose technology for
log tracking. For. Prod. J. 2004 , 54, 28–35.

Sensors 2013 , 13 2342

326. Murphy, G. Early experience with aroma ta gging and electronic nose technology for log and
forest products tracking. In Proceedings of The Second Inte rnational Precision Forestry
Symposium , Seattle, WA, USA, 15–17 June 2003; p. 165.
327. Zheng, Y.; Liu, J.; Wang, D.; Yang, R. Laser scanning measurements on trees for logging
harvesting operations. Sensors 2012 , 12, 9273–9285.
328. Baby, R.E.; Cabezas, M.; Walsoe de Reca, E.N. Electronic nose: A useful tool for monitoring
environmental contamination. Sens. Actuator B Chem. 2000 , 69, 214–218.
329. Canhoto, O.; Magan, N. Potential for the detection of microorganisms and heavy metals in potable
water using electronic nose technology. Biosens. Bioelectron. 2003 , 18, 751–754.
330. Goschnick, J.; Koronczi, I.; Frietsch, M.; Kiselev, I. Water pollution recognition with the electronic
nose KAMINA. Sens. Actuator B Chem. 2005 , 106, 182–186.
331. Bourgeois. W.; Stuetz, R.M. Use of a chemical sensor array for detecting pollutants in domestic
wastewater. Water Res. 2002 , 36, 4505–4512.
332. Lamagna, A.; Reich, S.; Rodríquez, D.; Boselli, A.; Cicerone, D. The use of an electronic nose to
characterize emissions from a highly polluted river. Sens. Actuator B Chem. 2008 , 131, 121–124.
333. Falasconi, M.; Gobbi, E.; Pardo, M.; Torre, M.D.; Bresciani, A.; Sberveglieri, G. Detection of
toxigenic strains of Fusarium verticillioides in corn by electronic olfactory system. Sens. Actuator B
Chem. 2005 , 108, 250–257.
334. Cheli, F.; Campagnoli, A.; Pinotti, L.; Savoini, G.; Dell’Orto, V. Electronic nose for determination of
aflatoxins in maize. Biotechnol. Agron. Soc. 2009 , 13, 39–43.
335. Campagnoli, A.; Cheli, F.; Savoini, G.; Crotti, A.; Pastori, A.G.M.; Dell’Ort o, V. Application of an
electronic nose to detection of aflatoxins in corn. Vet. Res. Commun. 2009 , 33, S273–S275.
336. Tang, K.; Chiu, S.; Pan, C.; Hsieh, H.; Liang, Y.; Liu, S. Development of a portable electronic
nose system for the detection and classification of fruity odors. Sensors 2010 , 10, 9179–9193.
337. Cimander, C.; Mandenius, C. Online monitoring of a bioprocess based on a multi-analyser system
and multivariate statistical process modeling. J. Chem. Technol. Biotechnol. 2002 , 77, 1157–1168.
338. Cimander, C.; Carlsson, M.; Mandenius, C. Sensor fusion for on-line monitoring of yoghurt
fermentation. J. Biotechnol. 2002 , 99, 237–248.
339. Markoma, M.A.; Md Shakaff, A.Y.; Adom, A. H.; Ahmad, M.N.; Hidayat, W.; Abdullah, A.H.;
Fikri, N.A. Intelligent electronic nose system for basal stem rot disease detection. Comput.
Electron. Agric. 2009 , 66, 140–146.
340. Sironi, S.; Capelli, L.; Céntola, P.; Del Rosso, R.; Grande, M.I. Continuous monitoring of odours
from a composting plant using electronic noses. Waste Manag. 2007 , 27, 389–397.
341. Littarru, P. Environmental odours assessment from wast e treatment plants: Dynamic olfactometry in
combination with sensorial analysers “electronic noses”. Waste Manag. 2007 , 27, 302–309.
342. Maarse, H.; Visscher; C.A. Volatile Compounds in Foods: Quantitative and Qualitative Data ;
TNO Food Analysis Institute: Zeist, The Netherlands, 1996; p. 17.
343. Blank, I.; Sen, A.; Grosch, W. Potent odorants of the roasted powder and br ew of Arabica coffee.
Z. Lebensmittelunters. Forsch. 1992 , 195, 239–245.
344. Sarrazin, C.; Le Quéré, J.-L.; Gretsch, C.; Li ardon, R. Representativene ss of coffee extracts: A
comparison of different extraction methods. Food Chem . 2000 , 70, 99–106.

Sensors 2013 , 13 2343

345. Dharmawan, J. Characterization of Volatile Compounds in Selected Citrus Fruits from Asia .
Ph.D. Thesis, National University of Singapore, Singapore, Singamore, 2008; p. 128.
346. Zheng, L.-Y.; Sun, G.-M.; Liu, Y.-G.; Lv, L.-L .; Yang, W.-X.; Zhao, W. -F.; Wei, C.-B. Aroma
volatile compounds from two fresh pineapple varieties in China. Int. J. Mol. Sci. 2012 , 13,
7383–7392.
347. Beaulieu, J.C.; Lea, J.M. Characterization a nd semiquantitative analys is of volatiles in
seedless watermelon varieties us ing solid-phase microextraction. J. Agric. Food Chem. 2006 , 54,
7789–7793.
348. Sáenz, C.; Cedrón, T.; Cabredo, S. Classifi cation of wines from five Spanish origin
denominations by aromatic compound analysis. J. AOAC Int. 2010 , 93, 1916–1922.
349. Chang, C.-I.; Hung, P.-H.; Wu, C.-C.; Che ng, T.C.; Tsai, J.-M.; Lin, K.-J.; Lin, C.-Y.
Simultaneous detection of multiple fish pathogens using a nake d-eye readable DNA microarray.
Sensors 2012 , 12, 2710–2728.
350. Zhang, J.; Li, P.; Hu, X.; Zhang, Q.; Ding, X. ; Zhang, W. Microarray technology for major
chemical contaminants analysis in food: Current status and prospects. Sensors 2012 , 12,
9234–9252
351. Liu, G.; Lao, R.; Xu, L.; Xu, Q.; Li, L.; Zha ng, M.; Shen, H.; Mathur, S.; Fan, C.; Song, S.
Detection of single-nucleotide polymorphism on uidA gene of Escherichia coli by a multiplexed
electrochemical DNA biosensor with oligonuc leotide-incorporated nonfouling surface. Sensors
2011 , 11, 8018–8027.
352. Geschwin dner, S.; Carlsson, J.F.; Knecht, W. Applicati on of optical biosensors in small-molecule
screening activities. Sensors 2012 , 12, 4311–4323.
353. Zhang, Q.; Xue, C.; Yuan, Y.; Lee, J.; Sun, D.; Xiong, J. Fiber surface modification technology
for fiber-optic localized surface plasmon resonance biosensors. Sensors 2012 , 12, 2729–2741.
354. Hussain, I.; Brust, M.; Papworth, A.J.; Cooper, A.I. Preparation of acr ylate-stabilized gold and
silver hydrosols and gold-polymer composite films. Langmuir 2003 , 19, 4831–4835.
355. Liu, F.-J.; Huang, L.-M.; Wen, T.-C.; Li, C.-F.; Huang, S.-L.; Gopalan, A. Platinum particles
dispersed polyaniline-modified electrodes cont aining sulfonated polyelectrolyte for methanol
oxidation. Synth. Met. 2008 , 158, 767–774.
356. O’Mullane, A.P.; Dale, S.E.; Macpherson, J.V.; Unwin, P.R. Fabrication and electrocatalytic
properties of polyaniline/Pt nanoparticle composite. Chem.Commun. 2004 , 14, 1606–1607.
357. Sakmeche, N.; Bazzaoui, E.A.; Fall, M.; Aeiyac h, S.; Jouini, M.; Lacroix, J.C.; Aaron, J.J.;
Lacaze, P.C. Application of sodium dodecyl sul phate (SDS) micellar solution as an organised
medium for electropolymerization of thiopene derivatives in water. Synth. Met. 1997 , 84,
191–192.
358. Ogura, K.; Nakaoka, K.; Nakayama, M. Studies on ion transport during pot ential cycling of a
Prussian blue (inner) polyaniline (outer) bila yer electrode by quartz crystal microbalance and
Fourier transform infrared reflection spectroscopy. J. Electroanal. Chem. 2000 , 486, 119–125.
359. Lupu, S.; Mihailiciuc, C.; Piga ni, L.; Renato, S.; Nicolae, T. ; Chiara, Z. Electrochemical
preparation and characterization of bilayer film composed by Prussian blue and conducting
polymer. Electrochem. Commun. 2002 , 4, 750–758.

Sensors 2013 , 13 2344

360. Schilirò, T.; Pignata, C.; Rovere, R.; Fea, E. ; Gilli, G. The endocrine disrupting activity of
surface waters and of wastewat er treatment plant effluents in relation to chlorination.
Chemosphere 2009 , 75, 335–340.
361. Olowu, R.A.; Arotiba, O.; Mailu, S.N.; Waryo, T.T.; Baker, P.; Iwuoha, E. Electrochemical
aptasensor for endocrine disrupting 17 β-Estradiol based on a poly(3,4-ethylenedioxylthiopene)-
Gold nanocomposite platform. Sensors 2010 , 10, 9872–9890.
362. Gallardo, J.; Alegret, S.; Muñoz, R.; Leija, L.; Hernandez, P.R.; del Valle, M. An electronic
tongue using potentiometric al l-solid-state PVC-membrane sensors for the simultaneous
quantification of ammonium a nd potassium ions in water. Anal. Bioanal. Chem. 2003 , 377,
248–256.
363. Gallardo, J.; Alegret, S.; Muñoz, R.; Leija, L. ; Hernandez, P.R.; del Valle, M. Use of an
electronic tongue based on all-solid state potentiometric sensors fo r the quantitation of alkaline
ions. Electroanalysis 2005 , 17, 348–355.
364. Gutierrez, M.; Alegret, S.; del Valle, M. Potentiometric bioelectronic tongue for the analysis of
urea and alkaline ions in clinical samples. Biosens. Bioelectron. 2007 , 22, 2171–2178.
365. Cortina, M.; Duran, A.; Alegret, S.; del Va lle, M. A sequential inje ction electronic tongue
employing the transient response from potentiom etric sensors for anion multidetermination.
Anal. Bioanal. Chem. 2006 , 385, 1186–1194.
366. Gutes, A.; Calvo, D.; Cespedes, F.; del Valle , M. Automatic sequentia l injection analysis
electronic tongue with integrated reference electrode for th e determination of ascorbic acid, uric
acid and paracetamol. Microchim. Acta 2007 , 157, 1–6.
367. Krantz-Rülcker, C.; Stenberg, M.; Winquist , F.; Lundström, I. Electronic tongues for
environmental monitoring based on sensor arrays and pattern re cognition: A review. Anal. Chim.
Acta 2001 , 426, 217–226.
368. Vlasov, Y.; Legin, A.; Rudnitskaya, A. Cross-se nsitivity evaluation of chemical sensors for
electronic tongue: determination of heavy metal ions. Sens. Actuator B Chem. 1997 , 44, 532–537.
369. Gallardo, J.; Alegret, S.; del Valle, M. A flow-injectio n electronic tongue based on
potentiometric sensors for the determination of nitrate in the presence of chloride. Sens. Actuator
B Chem. 2004 , 101, 72–80.
370. Gutes, A.; Cespedes, F.; Alegret, S.; del Vall e, M. Sequential inject ion system with higher
dimensional electrochemical sensor signals: Part 1. Voltammetric e-tongue for the determination
of oxidizable compounds. Talanta 2005 , 66, 1187–1196.
371. Gutes, A.; Cespedes, F.; del Valle, M.; Loutha nder, D.; Krantz-Rülcker, C.; Winquist, F. A flow
injection voltammetric electronic tongue applied to paper mill industrial waters. Sens. Actuator B
Chem. 2006 , 115, 390–395.
372. Valdes-Ramírez, G.; Gutierrez, M.; del Valle, M.; Ramírez-Silva, M.T.D.; Fournier, D.; Marty,
J.L. Automated resolution of dichlorvos a nd methylparaoxon pesticid e mixtures employing a
flow injection system with an inhibition electronic tongue. Biosens. Bioelectron. 2009 , 24,
1103–1108.
373. Gutes, A.; Ibañez, A.B.; Cespedes, F.; Alegret, S.; del Valle, M. Simultaneous determination of
phenolic compounds by means of an automa ted voltammetric “electronic tongue”. Anal. Bioanal.
Chem. 2005 , 382, 471–476.

Sensors 2013 , 13 2345

374. Gutierrez, M.; Alegret, S.; Cacer es, R.; Casadesús, J.; Marf, O.; del Valle, M. Application of a
potentiometric electronic tongue to fertigation strategy in greenhouse cultivation. Comput.
Electron. Agric. 2007 , 57, 12–22.
375. Vlasov, Y.; Legin, A.; Rudnitskaya, A.; di Natale , C.; D’amico, A. Nonspecific sensor arrays
(“electronic tongue”) for chemi cal analysis of liquids. Pure Appl. Chem. 2005 , 77, 1965–1983.
376. Escuder-Gilabert, L.; Peris, M. Review: Highlights in recent applications of electronic tongues in
food analysis. Anal. Chim. Acta 2010 , 665, 15–25.
377. Riul, A., Jr.; Dantas, C.A.R.; Miyazaki, C.M.; Oliveira, O.N. Recent a dvances in electronic
tongues. Analyst 2010 , 135, 2481–2495.
378. del Valle, M. Sensor arrays an d electronic tongue systems. Int. J. Electrochem. 2012 , 2012 , 1–11.
379. Guiseppi-Elie, A. Electroconductive hydrogels: Synthesis, characterization and biomedical
applications. Biomaterials 2010 , 31, 2701–2716.
380. Park, S.; Lee, H.J.; Koh, W.-G. Multiplex immunoassay platforms based on shape-coded
poly(ethylene glycol) hydrogel micropa rticles incorporating acrylic acid. Sensors 2012 , 12,
8426–8436.
381. Spinelli, F.; Noferini, M.; Costa, G. Near infrar ed spectroscopy (Nirs): Pers pective of fire blight
detection in asymptomatic plant material. Acta Hort. 2006 , 704, 87–90.
382. Sankaran, S.; Mishra, A.; Ehsani, R.; Davis, C. A review of advanced techniques for detecting
plant diseases. Comput. Electron. Agric. 2010 , 72, 1–13.
383. Brudzewski, K.; Osowski, S.; Ul aczyk, J. Differential electronic nose of two chemo sensor arrays
for odor discrimination. Sens. Actuator B Chem. 2010 , 145, 246–249.
384. Choi, S.-I.; Kim, S.-H.; Yang, Y.; Jeong, G.-M. Data refinement and channel selection for a
portable e-nose system by the use of feature feedback. Sensors 2010 , 10, 10387–10400.
385. Twomey, K.; de Eulate, E.A.; Alderman, J.; Arri gan, D.W.M. Fabrication and characterization of
a miniaturized planar voltammetric sensor array for use in an electronic tongue. Sens. Actuator B
Chem. 2009 , 140, 532–541.
386. Sun, Y.-F.; Liu, S.-B.; Meng, F.-L.; Liu, J.-Y .; Jin, Z.; Kong, L.-T.; Liu, J.-H. Metal oxide
nanostructures and their gas se nsing properties: A review. Sensors 2012 , 12, 2610–2631.
387. Chen, P.-C.; Shen, G.; Zhou, C. Chemical sensor s and electronic noses based on 1-D metal oxide
nanostructures. IEEE Trans. Nanotechnol. 2008 , 7, 668–682.
388. García-González, D.L.; Aparicio, R. Sensors: From biosensors to the electronic nose.
Grasas y Aceites 2002 , 53, 96–114.
389. Staples, E.J. Electr onic nose simulation of ol factory response containi ng 500 orthogonal sensors
in 10 seconds. In Proceedings of the 1999 IEEE Ultrasonics Frequency Control and
Ferroelectrics Symposium , Lake Tahoe, CA, USA, 17–20 October 2000; pp. 307–313.
390. White, J.; Kauer, J.S.; Dickinson, T.A.; Walt, D. R. Rapid analyte recognition in a device based
on optical sensors and the olfactory system. Anal. Chem. 1996 , 68, 2191–2202.
391. Campagnoli, A.; Cheli, F.; Polidori, C.; Zani nelli, M.; Zecca, O.; Savoini, G.; Pinotti, L.;
Dell’Orto, V. Use of the electronic nose as a sc reening tool for the recognition of durum wheat
naturally contaminated by deoxyni valenol: A preliminary approach. Sensors 2011 , 11, 4899–4916.

Sensors 2013 , 13 2346

392. Fujioka, K.; Arakawa, E.; Kita, J.; Aoyama , Y.; Okuda, T. Yoshinobu, M.; Yamamoto, K.
Combination of real-value smell and me taphor expression aids yeast detection. PLoS One 2009 ,
4, e7939.
393. Falasconi, M.; Concina, I.; Gobbi, E.; Sberveglie ri, V.; Pulvirenti, A.; Sb erveglieri, G. Electronic
nose for microbiological quali ty control of food products. Int. J. Electrochem. 2012 , 2012 , 1–12.
394. Wang, D.; Wang, X.; Liu, T.; Liu, Y. Prediction of total viable counts on chilled pork using an
electronic nose combined with support vector machine. Meat Sci. 2012 , 90, 373–377.
395. Pallottino, F.; Costa, C.; Antonucci, F.; Strano, M. C.; Calandra, M.; Solainia, S.; Menesattia, P.
Electronic nose application for determination of Penicillium digitatum in Valencia oranges.
J. Sci. Food Agric. 2012 , 92, 2008–2012.
396. Gobbi, E.; Falasconi, M.; Tore lli, E.; Sberveglieri, G. Elect ronic nose predicts high and low
fumonisin contamination in maize cultures. Food Res. Int. 2011 , 44, 992–999.
397. Zhang, B.; Xi, W.P.; Wei, W.W.; Shen, J.Y.; Fer guson, I.; Chen, K.S. Changes in aroma-related
volatiles and gene expression during low temperatur e storage and subsequent shelf-life of peach
fruit. Postharvest Biol. Tecnol. 2011 , 60, 7−16.
398. Zhang, X.; Qi, Y.; Yang, X.; Jia, H. Evaluatio n of maturity of peac h by electronic nose. J. South
China Agric. Univ. 2012 , 1, 1–4.
399. Olunloyo, V.O.S.; Ibidapo, T.A.; Dinrifo, R.R. Ne ural network-based electronic nose for cocoa
beans quality assessment. Agric. Eng. Int. CIGR J . 2011 , 13, 1–17.
400. Messina, V.; Domínguez, P.G.; Sancho, A.M.; Walsöe de Reca, N.; Carrari, F.; Grigioni, G.
Tomato quality during short-term storage assessed by colour an d electronic nose. Int. J.
Electrochem. 2012 , 2012 , 1–7.
401. Romani, S.; Cevoli, C.; Fabbri, A.; Alessandrin i, L.; Rosa, M.D. Evaluation of coffee roasting
degree by using electronic nose and artificial neural network for off-line quality control. J. Food
Sci. 2012 , 77, C960–C965.
402. Kubiak, A.; Wenzl, T.; Ulberth, F. Evaluation of the quality of postharvest rapeseed by means of
an electronic nose. J. Sci. Food Agric. 2012 , 92, 2200–2206.
403. Yang, Y.; Zhao, Y.; Zhang, S.; Ni, Y.; Zhan, J. Qualitative analysis of age and brand of
unblended brandy by electronic nose. In Computer and Computing Tech nologies in Agriculture
V, IFIP Advances in Informa tion and Communication Technology ; Springer: Berlin, Germany;
2012, pp. 619–628.
404. Berna, A. Metal oxide sensors for electronic noses and their application to food analysis. Sensors
2010 , 10, 3882–3910.
405. Vallone, S.; Lloyd, N.W.; Ebeler, S.E.; Zakharov, F. Fruit volatile analysis using an electronic
nose. J. Vis. Exp. 2012 , 61, e3821.
406. Demir, N.; Ferraz, A.C.; Sargent, S.A.; Balaba n, M.O. Classification of impacted blueberries
during storage using an electronic nose. J. Sci. Food Agric. 2011 , 91, 1722–1727.
407. Mamat, M.; Samad, S.A.; Hannan, M.A. An elect ronic nose for reliable m easurement and correct
classification of beverages. Sensors 2011 , 11, 6435–6453.
408. Ghasemi-Varnamkhasti, M.; Mohtasebi, S.S.; Si adat, M.; Razavi, S.H.; Ahmadi, H.; Dicko, A.
Discriminatory power assessment of the sensor array of an electronic nose system for the
detection of non-alc oholic beer aging. Czech J. Food Sci. 2012 , 30, 236–240.

Sensors 2013 , 13 2347

409. Ghaffari, R.; Zhang, F.; Iliescu, D.; Hines, E.; Leeson, M.; Napier, R.; Clarkson, J. Early
detection of diseases in tomato crops: An elect ronic nose and intelligent systems approach. In
Proceedings of The 2010 International Joint Conferen ce on Neural Networks (IJCNN) ,
Barcelona, Spain, 18–23 July 2010, pp. 1–6.
410. Lee, W.S.; Alchanatis, V.; Yang, C.; Hirafuji, M.; Moshoue, D.; Li, C. Sensing technologies for
precision specialty crop production. Comput. Electron. Agric. 2010 , 74, 2–33.
411. Zhou, B.; Wang, J. Detection of insect infesta tions in paddy field using an electronic nose. Int. J.
Agric. Biol. 2011 , 13, 707–712.
412. Li, C.; Schmidt, N.E.; Gitaitis, R. Detection of onion postharvest dis eases by analyses of
headspace volatiles using a gas sensor array and GC-MS. Food Sci. Technol. 2011 , 44,
1019–1025.
413. Abdullah, A.H.; Adom, A.H.; Shakaff, A.Y.M.; Ahmad, M.N.; Zakaria, A.; Saad, F.S.A.; Isa,
C.M.N.C.; Masnan, M.J.; Kamarudin, L.M. Hand-he ld electronic nose sens or selection system
for basal stamp rot (BSR) disease detection. In Proceedings of 2012 3rd International
Conference on Intelligent Systems, Modeling and Simulation (ISMS) , Kota Kinabalu, Malaysia,
8–10 February 2012, pp. 737–742.
414. Fogg, S. Electronic nose could be integrated into smartphones. Available online: http:// www.
newelectronics.co.uk/electronics-n ews/lectronic-nose-could-be-integrated-into-smartphones/44415/
(accessed on 22 August 2012).
415. Kinkeldei, T.; Zysset, C.; Münzenrieder, N.; Petti, L.; Tröster, G. In tube integrated electronic
nose system on a flexible polymer substrate. Sensors 2012 , 12, 13681–13693.
416. Santos, J.P.; Aleixandrea, M.; Cruz, C. Ha nd held electronic nose for VOC detection.
Chem. Eng. Trans. 2012 , 30, 181–186.
417. Hamedani, N.F.; Mahjoub, A.R.; Ali khodadadi, A. ; Mortazavi, Y. CO and ethanol selective
sensor of La 2O3-doped ZnO nanostructures synthesized by microwave assisted fast method. In
Proceedings of the 14th International Meeti ng on Chemical Sensors (IMCS) , Frankfurt,
Germany; 20–23 May 2012, pp. 1641–1643.
418. Amin, M.; Manzoor, U.; Islam, M.; Bhatti, A.S.; Shah, N.A. Synthesis of ZnO nanostructures for
low temperature CO and UV sensing. Sensors 2012 , 12, 13842–13851.
419. Mirabbaszadeh, K.; Mehrabian, M. Synthesis and properties of ZnO na norods as ethanol gas
sensors. Phys. Scr. 2012 , 85, 1–8.
420. Tian, X.Y.; Cai, Q.; Ye, Z.X.; Guo, W.; Lu, Y. W.; Zhang, Y.M. Detection of TVOC and odor in
industrial park using electronic nose. Huan Jing Ke Xue 2011 , 32, 3635–3640.
421. Oladipupo, O.O.; Eletta, O.A. Neuro-identifica tion of some commonly used volatile organic
compounds using electronic nose. Chem. Process Eng. Res. 2012 , 2, 43–53.
422. Kim, H.; Konnanath, B.; Sattigeri, P.; Wang, J. ; Mulchandani, A.; Myung, N.; Deshusses, M.A.;
Spanias, A.; Bakkaloglu, B. Electronic-nose fo r detecting environmen tal pollutants: Signal
processing and analog front-end design. Analog Integr. Circ. Signal Process . 2012 , 70, 15–32.
423. Pan, L.; Yang, S.X. An electroni c nose network system for onlin e monitoring of livestock farm
odors. IEEE Trans. Mechatron. 2009 , 14, 371–376.
424. Brattoli, M.; de Gennaro, G.; de Pinto, V.; Lo iotile, A.D.; Lovascio, S.; Penza, M. Odour
detection methods: Olfactometry and chemical sensors. Sensors 2011 , 11, 5290–5322.

Sensors 2013 , 13 2348

425. Abdullah, A.H.; Shakaff, A.Y.M.; Adom, A.H.; Zakaria, A.; Saad, F.S.A.; Kamarudin, L.M.
Chicken farm malodour monitoring us ing portable electronic nose system. Chem. Eng. Trans.
2012 , 30, 55–60.
426. Dentoni, L.; Capelli, L.; Sironi, S.; Rosso, R.D. ; Zanetti, S.; Torre, M.D. Development of an
electronic nose for environmental odour monitoring. Sensors 2012 , 12, 14363–14381.
427. Lan, J.; Liu, B.; Chen, Z.; Song, Z.; Lin, J. Discriminate model of electronic nose for
distinguishing volatiles of microbial fermentatio n bed in swine house. Fujian J. Agric. Sci. 2012 ,
1, 1–4.
428. Zhang, M.; Wang, X.; Liu, Y.; Xu, X.; Zhou, G. Species discrimination among three kinds of
puffer fish using an electronic nose comb ined with olfactory sensory evaluation. Sensors 2012 ,
12, 12562–12571.
429. De Cesare, F.; di Mattia, E.; Pantalei, S. ; Zampetti, E.; Vinciguerra, V.; Macagnano, A.
Electronic nose technology to measure soil microbi al activity and classify soil metabolic status.
Nat. Precedings 2011 , doi:10.1038/npre.2011.6364.1.
430. Zhang, F.; Iliescu, D.D.; Hines, E.L.; Leeso n, M.S. Tomato plant health monitoring: An
electronic nose approach. In Intelligent Systems for Machin e Olfaction: Tools and Methods ;
Hines, E.L., Leeson, M.S., Eds.; IGI Global: Hershey, PA, USA, 2011; pp. 231–248.
© 2013 by the authors; licensee MD PI, Basel, Switzerland. This arti cle is an open access article
distributed under the terms and conditions of the Creative Commons Attribution license
(http://creativecommons .org/licenses/by/3.0/).