Potential of the electronic-nose for the diagnosis of bacterial and fungal diseases in fruit trees

Potential of the electronic-nose for the diagnosis of bacterial andfungal diseases in fruit trees*

F. Spinelli1, M. Noferini1, J. L. Vanneste2 and G. Costa1

1University of Bologna, Dipartimento di Colture Arboree, viale Fanin 46 – 40127, Bologna (Italy); e-mail: [email protected], Ruakura Research Centre, Private Bag 3123, Hamilton (New Zealand)

*Paper presented at the EPPO Conference

ation with the Food and Environment R

2009-05-10 ⁄ 15.

ª 2010 The Authors. Journal compila

The electronic-nose instrumentation has advanced rapidly during the past decade, as the need for

highly sensitive, fast and accurate analytical measurements have considerably stimulated the interest

in developing these sensors as diagnostic tools. Given that the pathogen-induced plant responses also

include changes in emission of volatiles, the electronic-nose may represent a powerful and operator-

friendly alternative for rapid and reliable screening of asymptomatic plant material. In the present

study, the electronic nose EOS835 (Sacmi, Imola, Italy), based on metal oxide semiconductors, was

used. EOS835 was able to detect asymptomatic apple and pear plants experimentally infected with

Erwinia amylovora (fire blight). The electronic nose was also successfully tested for discriminating

Botrytis and Sclerotinia rots on both green and yellow kiwifruits. Even if the electronic-nose can

be successfully used in experimental conditions for early diagnosis of both pre- and post-harvest

diseases, its practical application in open fields, nurseries and packing houses still requires further

studies.

Introduction

Plants accumulate a diverse array of natural products, which are

thought to be involved in their interactions with the environment.

These chemicals play a role in interactions with microbes, ani-

mals, and even other plants, as well as protecting the plant from

ultraviolet radiation and oxidants. Many of these compounds

have been referred to as ‘secondary metabolites’ to distinguish

them from the ‘primary metabolites’ required for the growth of

all plants (Theis & Lerdau, 2003). These secondary metabolites,

are likely to be essential for plants successful competition or

reproduction.

Among the secondary metabolites, a relevant role is played by

volatile organic compounds (VOCs). The importance of these

compounds can be deduced by the considerable amount of photo-

assimilated carbon released back into the atmosphere as VOCs

(Holopainen, 2004). While many plants contain large amounts of

stored VOCs, others do not synthesize and emit them until a

stimulus (such as pathogen infection) is perceived (Alborn et al.,

1997; Holopainen, 2004; Pare et al., 2005). Induced volatiles

(IVOCs) may be emitted hours or days after an attack, both from

the site of injury as well as systemically from undamaged plant

leaves (Pare & Tumlinson, 1997, 1999; Mattiacci et al., 2001).

Recent research showed that specific volatiles are produced

during the plant pathogen interactions (Turner & Magan, 2004).

Therefore the profiling of the IVOCs emission by infected plants

on Diagnostics, organized in cooper-

esearch Agency (Fera), York, GB,

tion ª 2010 OEPP/EPPO, Bulletin OEP

or fruits was considered to represent a novel approach for disease

diagnosis (Spinelli et al., 2006).

However, the techniques, such as the gas chromatography-

mass spectroscopy, currently used for characterizing the VOCs

contributing to the olfactory profile of a specimen are expensive,

time consuming and require highly specialized personnel

(Heinzle, 1992; Saevels et al., 2003a, 2003b, 2004; Turner &

Magan, 2004). An interesting alternative to these analytical

techniques is represented by the electronic-nose (e-nose).

The e-nose, by mimicking the mammalian smell sensor, is an

instrument able to detect the olfactory fingerprint of a specimen

(Pearce, 1997; Pavlou & Turner, 2000). In contrast with gas chro-

matography-mass spectroscopy, the data obtained using an

e-nose consists of olfactory fingerprints; therefore the analysis of

the odour profile is only comparative rather than quantitative

(Tothill, 2001).

The e-nose is typically made of three elements: a sensor array

which is exposed to the volatiles, the converter of signals to a

readable format and the software-based data analyser to produce

characteristic outputs related to the odour encountered (Stetter,

1986; Aishima, 1991; Pearce et al., 1993; Magan & Evans,

2000; Baratto et al., 2005). A variety of sensors are available for

use in e-nose systems. The most common types are metal oxide

or conducting polymer based (Magan & Evans, 2000).

To discriminate between samples, the output from the sensor

array may be interpreted via a variety of methods such as pattern

recognition algorithms, principal component analysis (PCA),

discriminant function analysis, cluster analysis or artificial neural

network.

The qualitative discrimination power of e-nose often has an

uncanny resemblance to the subjective discrimination of odours

P/EPPO Bulletin 40, 59–67 59

60 F. Spinelli et al.

by the human nose (Pearce, 1997; McEntegart et al., 2000;

Pavlou & Turner, 2000; Baratto et al., 2005).

In addition, the e-nose has some important advantages com-

pared to a biological nose: it used detect toxic or otherwise haz-

ardous situations, it can detect substances odourless for biological

nose (e.g. toxic CO), it is suited for repetitive and boring tasks

which the biological nose get accustomed to (Stetter & Penrose,

2001).

The majority of e-nose applications are within foods and

drinks industry (Gardner & Bartlett, 1992), detection of microbial

contamination (bacteria, fungi and yeast), or measure of authen-

ticity (beverages, coffee and meat) (Gardner et al., 1992; Anklam

et al., 1998). However, recently some novel microbiological

applications have been reported, such as the characterisation of

fungi (Keshri et al., 1998; Keshri & Magan, 2000), bacteria (Gib-

son et al., 1997; Gardner et al., 1998; McEntegart et al., 2000;

Momol et al., 2004) and the discrimination between Helicobact-

er pylori and other gastroesophageal isolates (Pavlou et al.,

2001).

In fact, e-nose based approaches are an ideal alternative for

rapid screening of infectious diseases because the results can be

obtained in minutes, rather than the days taken by traditional

techniques (Turner & Magan, 2004; Gardner & Bartlett 1999;

Dickinson et al., 1998).

Many of the instrumentations developed to date for the

medical diagnostics market can be adapted for the agri-food field

(Tothill, 2001).

For example, e-nose can be used to assess fruit quality (Brez-

mes et al., 2001), ripeness (Llobet et al., 1999; Di Natale et al.,

2001), biological and physical damages to fruits (Di Natale et al.,

2001; Tan et al., 2003) and to forecast the optimal harvesting

time (Saevels et al., 2003a, 2003b, 2004) and to monitor the

plant physiological state (Baratto et al., 2005).

In the present study, the e-nose EOS835 (SACMI Inc., Imola,

Italy) has been tested for its efficacy in discriminating asymptom-

atic fire blight infected plants. In addition, EOS835 was also chal-

lenged for the diagnosis of two of the main fungal diseases of

kiwifruits during storage (the grey mould by Botrytis cinerea and

the Sclerotinia sclerotiorum rot).

Material and methods

E-nose equipment and sampling apparatus

The olfactory fingerprints were collected with the e-nose EOS835

developed by SACMI Inc., Imola, Italy (figure 1A). EOS835 is

based on an array of six different sensors constituted of metal-

oxides semiconductors (MOS). These sensors are made by a

heated ceramic core coated by a semiconducting film. The semi-

conductor sensors can detect volatiles by monitoring changes in

the conductance during the oxidative reaction of the molecules

present in the gas phase. The gas samples were collected at a

flow rate of 180 mL min)1. To minimize the influence due to the

possible odors in the external air, the experiments were per-

formed using gas-chromatographic air as a reference. To adjust

the relative humidity of the reference air to levels comparable to

ª 2010 The Authors. Journal co

the sampling chamber, it was fluxed through a porous stone bub-

bling in a jar of sterile water. Air filtered by multiple passages

through active charcoal and silica gel was used as an alternative

to gas-chromatographic air.

The e-nose readings were set according to the following

parameters: ‘Warm Up’: 0 min, ‘Wait’: 0 min, ‘Before’:

0.6 min, ‘During’: 1 min, ‘After’: 0.8 min, ‘Clean End’: 0 min.

Before the e-nose readings, the plant material was enclosed in

an appropriate sampling chamber to allow the headspace to build

up.

Fire blight diagnosis

Preliminary small scale experiments were performed on apple

and pear microcuttings (figure 1B). Apple (cv. Gala) and pear

plants (Abbe Fetel) were grown on Murashige and Skoog and

Quoring Lepoivre medium, respectively. Experimental inocula-

tion was performed by pinching a leaf with tweezers dipped in a

suspension of E. amylovora 1540 (5 · 107 cfu mL)1). Plants

wounded by sterile tweezers were used as control. The precise

bacterial concentration was successively determined by sequen-

tial dilution and plating on Luria Agar Petri dishes that were

successively incubated at 27�C for 24–48 h.

Before the e-nose readings, the vials containing the microcut-

tings were flushed for 5 min with sterile, odourless air (180 mL

min)1) and incubated for 30 min to allow the headspace to build

up.

Further experiments were performed on 2-year old potted

plants enclosed in a Teflon chamber designed for the volatile col-

lection (figure 1C) or in a plastic bag for the e-nose readings

(figure 1D). A constant air flux (12 L min)1) was maintained

inside the enclosed chamber. The plants were kept in field condi-

tions under a shelter to protect them from excessive sunlight and

heating. The photon flux density in the photosynthetically-active

wavelength range, the air temperature, the CO2 assimilation and

the transpiration rate were constantly monitored (data not shown).

Standard cultural management was applied.

The plants were inoculated by cutting all the leaf tips with

infested scissors previously dipped in a bacterial suspension

(5 · 107 cfu mL)1) of a local strain of E. amylovora

(EaDCA289 ⁄ 01). Plants with leaf tips cut with scissors dipped in

sterile water were used as control. Each treatment consisted of

three plants. The e-nose readings were performed until develop-

ment of symptoms.

Botrytis and Sclerotinia rots diagnosis

The e-nose was used at daily intervals to study the olfactory pro-

files of green and yellow-flesh kiwifruits infected with B. cinerea

and S. sclerotiorum. Green kiwifruits ‘Hayward’ and ‘Summer-

kiwi 3373’ were collected at harvest maturity from the experi-

mental vineyard of the Dipartimento Colture Arboree, Universita

di Bologna. The yellow kiwifruits ‘ZESPRI� GOLD’ were

obtained from a private vineyard in Faenza. Before the experi-

mental inoculation, the fruits were maintained at 4�C for 21 days

to simulate storage conditions.

mpilation ª 2010 OEPP/EPPO, Bulletin OEPP/EPPO Bulletin 40, 59–67

A B

C D

Fig. 1 Electronic nose equipment, EOS835 (A) and sampling chambers for the fire blight diagnosis on entire plants (B–D). Small scale experiment on pear

microcuttings (B), medium scale experiment for VOCs sampling and characterization by GC-MS (C) and open field experiment carried out on scions under nursery

conditions (D).

Potential of the electronic nose 61

The pure fungal strains were obtained by DSMZ (Germany)

and grown on malt agar at 22�C for about 5 days. For the inocu-

lation, a 5 mm plug of malt agar was taken from the edge of an

active growing culture and placed on the freshly cut fruit pedicel.

Fruits inoculated with a sterile agar plug were used as control.

After inoculation, the fruits were maintained at room temperature

(20–22�C) for 14 days. Thirty minutes before the e-nose readings

the fruits were enclosed in 500 mL glass jars to allow the head-

space to build up. The glass jars were previously washed several

times in purified water and then dried in an oven at 90�C for

48 h. The heat treatment aimed to minimize the presence of vola-

tile pollutants inside the jars. The olfactory profiles of control and

infected plants were monitored, at 24 h intervals, until symptom

development.

At the same time also the ethylene production was also moni-

tored according to the methodology published by Bregoli et al.,

2005 (data not shown).

Statistical analysis

The olfactory profiles were elaborated using the Nose Pattern

Editor Program (v. 2.6.0) developed by SACMI. Different mathe-

matical algorithms (Classical, Fourier, Single Point, Many Points)

ª 2010 The Authors. Journal compilation ª 2010 OEPP/EPPO, Bulletin OEP

were used. The Many Points algorithm was generally used for

the analysis where a limited number (up to 4) of thesis were

involved.

The transformed data were successively processed by means

of PCA (Principal Component Analysis) using the MATLAB 6.5

software (MathWorks Inc., Natick, MA, US).

Results and discussion

Fire blight diagnosis

On pear plants, the e-nose clearly discriminated between control

and infected microcuttings (Fig. 2A). The medium scale experi-

ments performed on potted plants confirmed the results obtained

with microcuttings (Figure 2B).

Similar results were obtained also on apple microcuttings (data

not shown) and potted plants (Fig. 3). The olfactory profiles of

infected apple plants changed during the time, thus infected plants

become completely distinct from the control ones after only 72 h

from the inoculation (Fig. 3D). At the symptoms appearance

(192 h after the inoculation), the differences among the olfactory

profiles of infected plants drastically decreased and they grouped

in a close cluster (Fig. 3F). This observation might suggest that a

P/EPPO Bulletin 40, 59–67

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PC 2: 24.0%

Control Infected

A B

Fig. 2 Fire blight diagnosis on pear microcuttings (A) and potted plants (cv. Abbe Fetel) (B). The graphs report the principal component analysis (PCA) of the

aroma profiles obtained from healthy (hollow circle) and infected (back cross) plants. The PC value for each axis indicates the proportion of the difference explained

by that PC.


pathogenesis-related IVOCs emission plays a major role in deter-

mining the olfactory fingerprint of symptomatic plants.

Botrytis and Sclerotinia rots diagnosis

The e-nose was effective in detecting green kiwifruits infected by

B. cinerea and S. sclerotiorum (Figs 4–5). On ‘Hayward’ kiwi-

fruits, the e-nose analysis was able to clearly discriminate the dis-

eased fruits after 168 h from the experimental inoculation

(Fig. 4C). In fact, 72 h after the inoculation, the olfactory profiles

of healthy and infected fruits did not show any differentiation

(Fig. 4A) and only after 144 h a partial discrimination was

observed (Fig. 4B). However, the olfactory fingerprints of the

control fruits changed with the progression of ripening and aging

and the differences among them greatly increased. Thus, after

192 h, the distances among the different olfactory profiles of

healthy fruits rose so extensively, that they were no longer distin-

guishable from the infected ones. On the contrary, the differences

among the odorous profiles of infected fruits did not increase and

even after 192 h, all the infected fruits grouped in a quite com-

pact cluster (Fig. 4D). On the base of this observation, we

hypothesized that the VOCs released upon infection gave a stron-

ger contribution than the ones related to ripening to the fruit’s

olfactory fingerprint. Finally, the e-nose was not able to discrimi-

nate between Botrytis or Sclerotinia infected fruits.

The experiment was repeated also on another green flesh kiwi-

fruit variety (‘Summer 3373’). On this cultivar, the e-nose dis-

crimination among healthy and infected fruits was not so clear

and only a partial and transient differentiation between control

and Sclerotinia infected fruits was observed (Fig. 5B). Also in

this experiment, the Botrytis and Sclerotinia infected fruits were

not discriminated by the e-nose.

On yellow flesh kiwifruit ‘ZESPRI GOLD�’, starting from

24 h after the inoculation, the e-nose was able to distinguish

between control and Sclerotinia infected fruits (Fig. 6A). As far

as the Botrytis infected fruits is concerned, the e-nose discrimi-

nated them from the control fruits only after 48 h (Fig. 6B). In


this case, the e-nose only partially distinguished between Botrytis

or Sclerotinia infected fruits.

Ethylene is a key signal in modulating the emissions of other vol-

atiles during fruit ripening and plant defences (O’Donnell et al.,

1996; Lurie et al., 2002; Botondi et al., 2003). Since, metal-oxide

sensors are able to detect ethylene, particularly the SnO2 sensors

employed in EOS835 (Baratto et al., 2005; Defilippi et al., 2009), a

simpleexperimentwasperformed to test the influenceof fruit ethyl-

ene production in shaping the e-nose analysis. For this purpose, in

parallel with the e-nose readings, the ethylene production by Botry-

tis infected kiwifruits was quantified at daily interval (data not

shown). Successively, on a second set of healthy fruits, treated with

water or 1-methylcyclopropene (1-MCP), ethylene was applied at

the same amount produced by infected fruits during the first 72 h

after inoculation (when the olfactory profiles of healthy and

infected fruits start to differentiate). 1-MCP was used since it has

been reported to be a non-toxic antagonist of ethylene action that

blocks the physiological action of ethylene (Sisler et al., 1996).

The same day of the ethylene treatment, half of the untreated

fruits were inoculated with B. cinerea. The e-nose was chal-

lenged for differentiating between the olfactory fingerprints of

ethylene-treated fruits and infected ones (Fig. 7).

The PCA analysis of these fruits clearly showed that the addi-

tion of ethylene to fruits previously treated with 1-MCP does not

significantly influence the e-nose readings. On the other hand, the

cluster of fruit treated with ethylene overlaps with the one of

Botrytis infected fruits (Fig. 7). This observation demonstrates

that, even if ethylene itself is perceived by the e-nose, its concen-

tration in the headspace is only partially responsible for the differ-

entiation of olfactory profiles. However, the olfactory fingerprint

of diseased fruits is mainly shaped by the ethylene-mediated pro-

duction of volatiles and therefore is not strictly pathogen-related.

Conclusions

Our results indicated the e-nose as a possible, effective, fast and

non-destructive tool for early disease diagnosis directly on the


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PC 1: 40.50%

PC 2: 16.97%

PC 1: 70.97%

PC 2: 17.97%

PC 1: 68.03%PC 2: 19.59%

Control Infected

PC 1: 61.30% PC 2: 15.08% PC 1: 78.07%

PC 2: 25.40% PC 1: 53.80%PC 2: 36.36%

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Fig. 3 Fire blight diagnosis on potted apple plants (cv. Gala). The graphs report the principal component analysis (PCA) of the aroma profiles obtained from healthy

(hollow circle) and infected (back cross) plants. The PC value for each axis indicates the proportion of the difference explained by that PC. The different panels

show the discrimination between control and infected plants at different time after inoculation. A, 0 h; B, 24 h; C, 48 h; D, 72 h; E, 96 h; F, 192 h.


whole plant or fruit. However, the use of e-nose for disease diag-

nosis still presents some important limitations.

Some of these bottlenecks are connected with the biological

production of VOCs and IVOCs. In fact, VOCs emission is

highly affected by plant or fruit mass and physiological stage and

by the environmental temperature and humidity (Rapparini &

Predieri, 2003). In addition, the VOC emission during the plant–

pathogen interactions is still uncharacterised. Very few research-

ers have described the IVOCs profile of infected plants; therefore,

the presence of specific odours fingerprints or volatile markers

has not yet been fully clarified. Finally, some of the key com-

pounds released during the pathogenesis (i.e. ethylene and NO)

are not specific for a unique disease and they may result as a


background noise for the detection of still unknown disease-

specific markers.

Those limitations can be overcome by identifying the pos-

sible disease-specific IVOCs produced during the different

plant–pathogen interactions. In fact, the identification of

unique IVOCs might allow the development of highly selec-

tive sensors which will significantly strength the reliability of

the e-nose. Finally, further studies must be designed to

identify how environment stimuli and the physiological state

of the plant influence the production of disease-related

IVOCs.

As far as the instrumental limitations of the e-nose technology,

it must be stressed that this equipment gives only qualitative


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PC 2: 18.99% PC 1: 48.82%

PC 2: 26.86%

Control B. cinerea S. sclerotiorum

PC 1: 56.05%PC 2: 24.79%PC 1: 65.33%

PC 1: 63.42%PC 2: 19.02%

A B

C D

Fig. 4 Diagnosis of Botrytis and Sclerotinia rot on green flesh kiwifruits (cv. Hayward). The graphs report the principal component analysis (PCA) of the aroma

profiles obtained from healthy (hollow circle), Botrytis (back cross) and Sclerotinia (grey asterisk) infected fruits. The PC value for each axis indicates the proportion

of the difference explained by that PC. The different panels show the discrimination between control and infected plants at different time after inoculation. A, 72 h;

B, 144 h; C, 168 h; D, 192 h.

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A B

Fig. 5 Diagnosis of Botrytis and Sclerotinia rot on green flesh kiwifruits (cv. Summerkiwi 3373). The graphs report the principal component analysis (PCA) of the

aroma profiles obtained from healthy (hollow circle), Botrytis (back cross) and Sclerotinia (grey asterisk) infected fruits. The PC value for each axis indicates the

proportion of the difference explained by that PC. The different panels show the discrimination between control and infected plants at different time after inoculation.

A, 0 h; B, 3 h.


results and it cannot identify and quantify the single volatile mol-

ecules forming the characteristic aroma of infected plants. For

this reason, the data obtained by e-nose are mainly comparative:

the fingerprint of the infected plant material needs to be com-

pared with a healthy reference and, possibly, with the fingerprint

of other suspected diseases. Finally, the MOS sensors sensitivity


is also influenced by the changes in relative humidity (RH) and

temperature. On the account of all these consideration, the analy-

sis of the olfactory profile of plants or fruits should be performed

on comparable material kept in standardized conditions. The gas

sample system developed for the analysis by controlling the

temperature and RH, minimizes their influence on the VOCs


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PC 1: 79.65%PC 2: 10.26%

PC 2: 20.00%PC 1: 66.29%PC 2: 6.31%

PC 1: 86.61%

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Control B. cinerea S. sclerotiorum

A B

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Fig. 6 Diagnosis of Botrytis and Sclerotinia rot on yellow flesh kiwifruits (cv. ZESPRI GOLD�). The graphs report the principal component analysis (PCA) of the

aroma profiles obtained from healthy (hollow circle), Botrytis (back cross) and Sclerotinia (grey asterisk) infected fruits. The PC value for each axis indicates the

proportion of the difference explained by that PC. The different panels show the discrimination between control and infected plants at different time after inoculation.

A, 24 h; B, 48 h; C, 148 h; D, 192 h.

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PC 1: 91.70%Control B. cinerea Ethylene 1-MCP + Ethylene

Fig. 7 Influence of ethylene on the e-nose diagnosis of Botrytis infected

green flesh kiwifruits (cv. Hayward). The healthy fruits were divided in

two groups: the fist one was treated with water, the second with 1-MCP.

These fruits were successively treated with the same amount ethylene

produced by infected fruit. Control fruits: (hollow circle), infected fruits

(back cross), healthy fruits treated with 1-MCP and ethylene (hollow

square) or water and ethylene (grey asterisk).



emission and sensors sensitivity. Long term gas sampling by

means of solid phase micro-extraction (SPME) fibres, which can

concentrate IVOCs, might overcome the need of standardized

conditions.

Therefore, further technological and methodological develop-

ments are needed for the practical application of the e-nose diag-

nosis in commercial nurseries and packing houses.

Utilisation potentielle du nez electroniquepour le diagnostic de maladies bacterienneset fongiques des arbres fruitiers

Les instruments de nez electronique ont progresse rapidement

au cours de la derniere decade, car le besoin de mesures tres

sensibles, rapides et precises a considerablement stimule le

developpement de ces capteurs en tant qu’outils de diagnostic.

Etant donne que les reponses induites dans la plante par l’agent

pathogene comprennent aussi des changements dans l’emission

de composes volatiles, le nez electronique peut representer une

alternative puissante et facile a utiliser pour le depistage rapide

et fiable de materiel vegetal asymptomatique. Dans cette etude,

le nez electronique EOS835 (Sacmi, Imola - Italie), base sur

des semiconducteurs a metal-oxyde, a ete utilise. EOS835 a pu

detecter des pommiers et poiriers asymptomatiques infectes

experimentalement avec Erwinia amylovora (feu bacterien). Le



nez electronique a aussi ete teste avec succes pour distinguer

des pourritures a Botrytis et Sclerotinia sur des kiwis verts et

jaunes.

Meme si le nez electronique peut etre utilise avec succes en

conditions experimentales pour le diagnostic precoce des mala-

dies avant et apres la recolte, son application pratique en plein

champ, dans des pepinieres et des stations d’emballage demande

encore d’autres etudes.

References

Aishima T (1991) Aroma discrimination by pattern recognition analysis of

responses from a semiconductor gas sensor array. Journal of Agricultural

and Food Chemistry 39, 752–756.

Alborn HT, Turlings TCJ, Jones TH, Stenhagen G, Loughrin JH & Tumlinson

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Potential of the electronic-nose for the diagnosis of bacterial and fungal diseases in fruit trees

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