This article appeared in a journal published by Elsevier. The attachedcopy is furnished to the author for internal non-commercial researchand education use, including for instruction at the authors institution

and sharing with colleagues.

Other uses, including reproduction and distribution, or selling orlicensing copies, or posting to personal, institutional or third party

websites are prohibited.

In most cases authors are permitted to post their version of thearticle (e.g. in Word or Tex form) to their personal website orinstitutional repository. Authors requiring further information

regarding Elsevier’s archiving and manuscript policies areencouraged to visit:

http://www.elsevier.com/copyright

Author's personal copy

Electronic nose and Alicyclobacillus spp. spoilage of fruit juices: An emergingdiagnostic tool

E. Gobbi a,b,*, M. Falasconi c, I. Concina c, G. Mantero a, F. Bianchi d, M. Mattarozzi d, M. Musci d,G. Sberveglieri c

a Biodiversity s.p.a., via Corfù 71, Brescia, Italyb Dipartimento di Biologia e Protezione delle Piante, Università di Udine, Via delle Scienze 208, I-33100 Udine, Italyc Sensor Laboratory, Dipartimento di Chimica e Fisica per l’Ingegneria e i Materiali, Università di Brescia & CNR-IDASC, Via Valotti 9, I-25123 Brescia, Italyd Dipartimento di Chimica Generale ed Inorganica, Chimica Analitica, Chimica Fisica, Università degli Studi di Parma, Viale Usberti 17/A, 43100 Parma, Italy

a r t i c l e i n f o

Article history:Received 15 September 2009Received in revised form 16 April 2010Accepted 27 April 2010

Keywords:Fruit juiceMicrobiological spoilageAlicyclobacillusVolatile compoundsElectronic nose

a b s t r a c t

The application of an electronic nose equipped with a Metal Oxide Semiconductor sensor array for thedetection of Alicyclobacillus acidoterrestris and A. acidocaldarius artificially inoculated in peach, orangeand apple fruit juices is described in this study. Overall the system was able to detect the presence of Ali-cyclobacillus spp. in all the tested fruit juices at 24 h from inoculation. The electronic nose could detectbacterial concentration as low as <102 colony forming unit/ml and it was also able to classify bacterialcontamination independently of the Alicyclobacillus species. The gas chromatography–mass spectrometrycharacterization of the volatile profile of orange juices confirmed the existence of quantitatively differentpatterns between contaminated and uncontaminated samples.

� 2010 Elsevier Ltd. All rights reserved.

1. Introduction

In the fruit juice industry, spoilage by microbial contaminationmay occur at any step during processing, altering anyone, or all, ofjuice attributes such as: flavour, odour and appearance, as well asshelf life, rendering the product unacceptable.

Generally, spoilage problems have been limited by the naturallow pH of the juices and also by applying high temperature treat-ments during manufacturing (Jensen, 1999; Walls & Chuyate,1998). Few deteriogenic microorganisms, such as some Bacillusand Clostridium, are capable of surviving in such conditions causingserious quality problems (Jay, 1998; Murdock & Hatcher, 1975). Inaddition to these organisms, in early 1980s, novel spore-formingacidophilic and thermophilic bacteria emerged as spoilage deter-minants (Cerny, Hennlich, & Poralla, 1984). They were later classi-fied into a new genus, Alicyclobacillus, based on their fatty acidcomposition and 16S rDNA sequences (Wisotzkey, Jurtshuk, Fox,Deinhard, & Poralla, 1992). Since then, the fruit juice industryhas dealt with the spoilage of shelf-stable fruit juice by Alicycloba-cillus spp. with increasing frequency (Pettipher & Osmundson,

2000; Pettipher, Osmundson, & Murphy, 1997; Walls & Chuyate,1998; Yamazaki, Teduka, & Shinano, 1996).

Alicyclobacillus spp. are aerobic, gram-positive, endospore-form-ing, non pathogenic, thermoacidophilic bacteria isolated mainlyfrom soil and hot springs. The spores are resistant to high temper-ature, thus they can survive ordinary pasteurization regimes usedin the juice industry. The genus Alicyclobacillus comprises differentspecies; however, only few, mainly Alicyclobacillus acidoterrestris,could represent a threat to the fruit beverages industry (Walker& Phillips, 2005). Moreover Alicyclobacilli are not always associ-ated to alterations of the product (Previdi, Colla, & Vicini, 1995).Spoilage has been observed predominantly in apple juice, but alsoin a large variety of fruit juices and beverages, as well as preserves,tomato products, tea and other herbal drinks (Splittstoesser, Chu-rey, & Lee, 1994; Splittstoesser, Lee, & Churey, 1998; Wisse & Par-ish, 1998). It is manifested as off-flavours, due mainly to theproduction of 2-methoxyphenol (guaiacol) (Yamazaki et al.,1996), 2,6-bromophenol (Borlinghaus & Engel, 1997), 2,6-chloro-phenol (Jensen & Whitfield, 2003), and as a light sediment (Brown,1995).

Spoilage of fruit juices by Alicyclobacilli is extremely difficult tobe revealed at early stages. This can be very detrimental for man-ufacturers because it often results in withdrawal of productbatches from the market with consequent economic and imagedamages for the company. Therefore spoilage is regarded as an

0956-7135/$ - see front matter � 2010 Elsevier Ltd. All rights reserved.doi:10.1016/j.foodcont.2010.04.011

* Corresponding author at: Biodiversity s.p.a., via Corfù 71, Brescia, Italy. Tel.: +39030221095; fax: +39 030 2450064.

E-mail address: micol@uniud.it (E. Gobbi).

Food Control 21 (2010) 1374–1382

Contents lists available at ScienceDirect

Food Control

journal homepage: www.elsevier .com/locate / foodcont

Author's personal copy

industry-wide issue that requires effective control measures to bedeveloped.

For these reasons, a severe, standardized and well timed controlof the microbiological quality of food is essential to enable produc-ers to act rapidly and effectively if necessary.

In this respect, culture-dependent conventional microbiologicalmethods present some drawbacks, mainly related to the highdetection limit and to the underestimation of the true microbialcommunity. Moreover they cannot perform real-time, on-lineand in situ measurements. On the other hand, innovative tech-niques such as DNA analysis, though very accurate and rather fast,can be too complicated and expensive to be integrated into theindustrial processing routine.

Volatile organic compounds (VOCs) can be generated by micro-organisms while metabolizing nutrients and could have some de-grees of specificity with the taxonomy of the producers (Bungeet al., 2008; Phillips et al., 2007; Wood et al., 2006). Thus someVOCs have been used as microbial specific markers (Fischer, Schw-albe, Ostrowski, & Dott, 1998; Mayrhofer et al., 2006) and as indi-cators of their growth in different foods such as meat (Balamatsia,Paleologos, Kontominas, & Savvaidis, 2006; Edwards, Dainty, &Hibbard, 1987), fish (Jørgensen, Huss, & Dalgaard, 2001), vegeta-bles and fruits (Lloyd, Grimm, Klich, & Beltz, 2005; Ragaert et al.,2006), and dairy products (Morales, Fernández-García, & Nuñez,2005).

In fruit juices, guaiacol has been acknowledged as the predom-inant taint metabolite of A. acidoterrestris (Chang & Kang, 2004), to-gether with 2,6-bromophenol and 2,6-chlorophenol, though theirassociation to Alicyclobacilli is still disputed (Chang & Kang,2004). Olfactory evaluation of guaiacol has been already provedas potentially useful for diagnosing the presence of Alicyclobacillusspp. (Bahçeci & Acar, 2007; Orr, Shewfelt, Huang, Tefera, & Beuchat,2000; Ziegler, Siegmund, & Pfannhauser, 2004). On the other hand,guaiacol is not always associated to the presence of the bacteria;therefore targeting its presence cannot guarantee the detectionof the bacteria and its consequent eradication from fruit juices,so preventing from any risk of further spoilage.

Technologies based on semi-selective gas sensors arrays for thedetection of VOCs, also termed electronic noses (EN) (Röck, Barsan,& Weimar, 2008), appear to be a promising alternative for the earlydiagnosis of microbial contamination in food (Peris & Escuder-Gila-bert, 2009).

In the food industry EN technology has been used also for qual-ity control, process monitoring, freshness evaluation, shelf-lifeinvestigation and authenticity assessment (Reid, O’Donnell, &Downey, 2006) with a good degree of success over a wide spec-trum of food types.

The EN use of semi-selective sensors prevents any real iden-tification or quantification of the individual compounds presentin the headspace of a food sample. Indeed ENs try to mimicthe working mechanism of biological olfaction: it captures theglobal odour fingerprint, stores it into a multidimensional pat-tern, and finally recognizes unknown odour samples by meansof a learning procedure.

EN technology owns several advantages such as a relatively lim-ited sample preparation, the rapidity of analysis and the ability,once the instrument has been trained, to operate stand-alone.

State-of-the-art ENs have also three major drawbacks: thetraining procedure can be lengthy and laborious; the gas sensorsare not sensitive enough with respect to human perception thresh-olds or to classical analytical techniques, like gas chromatography–mass spectrometry (GC–MS); finally sensors are subjected to longterm drift which can impair the classification performance overtime. Much work has been done – and still is carried out – in orderto fix these problems and to ameliorate the sensing technology(Pardo & Sberveglieri, 2004).

In the fruit juice industry, processes which will inhibit thegrowth of Alicyclobacilli are studied. However, since all these mea-sures raise production costs, they should only be introduced whenthere is an acute risk of spoilage. Conventional analysis methodsare complex and need variable times (12 h–5 days) to deliver a re-sult. By then, the juice can already be spoilt. With the aid of a gas-sensor system, contamination can be detected within minutesoffering therefore an effective method to detect Alicyclobacillusspp. early enough to prevent future spoilage in fruit juices.

The objective of the present research was to determine theapplicability of a state-of-the-art technology based EN (specifically,sensors and pattern recognition software) to the early detection ofAlicyclobacillus spp. in different fruit juices. Three juices, namelypeach, orange and apple were artificially contaminated with thebacteria and tested.

In parallel with the EN measurements, GC–MS analyses werecarried out, in order to identify the changes in the samples volatileprofile due to bacterial growth.

2. Materials and methods

2.1. Microorganisms

The strains A. acidoterrestris ATCC 49025 and A. acidocaldariusATCC 27009 were obtained from American Type Culture Collection(LGC American Type Culture Collection, VA, USA).

2.2. Juice samples

Commercially available apple, orange and peach juices wereused in this study. For each experiment, the same brand and batchof juices were used.

Juices were autoclaved for 20 min at 120 �C to ensure the ab-sence of living organisms other than the inoculated Alicyclobacilli.

2.3. Culture conditions

The strains were revitalized from the lyophilised stock accord-ing to the recommendations of manufacturers and successivelythe cultures were streaked onto Alicyclobacillus BAT (BAT) agar(Biokar Diagnostics, France) and incubated at 37 �C for 24 h. Thenthe plates were stored at 4 �C and maintained by weekly transferas working stock cultures. Fresh cultures of A. acidoterrestris ATCC49025 and A. acidocaldarius ATCC 27009 were made by transferringa loop of cells from the working stock cultures into the BAT brothand incubating overnight at 37 �C. Two hundreds microliters wereheat treated at 70 �C for 10 min: 100 ll were immediately used toinoculate the juice samples, while other 100 ll were enumeratedon BAT agar and incubated for 48 h at 37 �C to determine the exactcolony forming units (c.f.u.) numbers inoculated in the juicesamples.

Table 1Sensor array of the electronic nose EOS835 used in this work. The two sensors markedwith an asterisk were not considered for data analysis.

Sensor name Sensitive layer

Layer Catalyst

CJ1315 SnO2 SiO2

SB0222 SnO2 AgZH0531 WO3

a –SJ0729 SnO2 –SD0315 (�) SnO2 MoWHT15 (�) WO3

b –

a Prepared by sputtering.b Prepared by physical vapour deposition.

E. Gobbi et al. / Food Control 21 (2010) 1374–1382 1375

Author's personal copy

In parallel to EN analyses, one aliquot (100 ll) from each ENsample was removed, serially diluted, and surface plated in dupli-cate on BAT agar medium to determine the exact final concentra-tion. The plates were incubated at 45 �C for 48 h, the appearingcolonies were counted and the number of c.f.u. ml�1 calculated.

2.4. Sample preparation for EN analysis

2.4.1. Contamination detection in peach, orange and apple juiceTwo aliquots (400 ml) for each of the following juices, peach, or-

ange and apple, were dispensed in 500 ml Duran bottles and auto-

Fig. 1. Box plots of the feature R/R0 for the four sensors recorded on the headspace of contaminated (‘‘yes” label) and uncontaminated (‘‘no” label) juice samples: peach(column (a)), orange (column (b)) and apple (column (c)).

1376 E. Gobbi et al. / Food Control 21 (2010) 1374–1382

Author's personal copy

claved. One aliquot was inoculated with the overnight culture of A.acidoterrestris ATCC 49025 at the concentrations of 6 � 102 c.f.u.ml�1 of juice. The other was used as negative control. The artificiallycontaminated and uncontaminated juice samples were incubated at45 �C for 24 h. Each sample was analyzed after 24 h for volatile pat-tern by EN. In parallel, aliquots were removed from contaminatedand uncontaminated samples of the orange juice for the GC–MSanalysis described below.

2.4.2. EN specificityThree 750 ml aliquots of peach juice were dispensed in 1 l Dur-

an bottles and autoclaved; two of them were inoculated with theovernight cultures of A. acidoterrestris ATCC 49025 and A. acido-caldarius ATCC 27009 respectively at the concentrations of10 c.f.u./ml of juice. The three aliquots were then incubated at55 �C for 24 h, successively sub samples were taken from each ali-quot and analyzed for volatile pattern by EOS835.

2.4.3. Analytical sensitivity of ENThree 200 ml aliquots of orange juice were dispensed in Duran

bottles and autoclaved. Two aliquots were inoculated with theovernight culture of A. acidoterrestris ATCC 49025 at the concentra-tions of >102 and 102 c.f.u. ml�1 of juice respectively. All the sam-ples were incubated at 37 �C for 24 h and then sub samples weretaken and analyzed for volatile pattern by EOS835.

2.5. Sample analysis

2.5.1. Electronic noseMeasurements of the juice samples headspaces were carried

out by the commercial electronic nose EOS835 (Sacmi Imola scarl,Italy) which was described in a previous work (Falasconi, Pardo,Sberveglieri, Riccò, & Bresciani, 2005a). The EN was equipped withan array (Table 1) of custom semiconductor metal oxide (SMO)conductometric sensors (Sberveglieri, 1995) that are partially

Fig. 2. PCA score plot of the peach juice data related to both the uncontaminated(‘‘no” label) and contaminated (‘‘yes” label) by A. acidoterrestris samples at 24 hfrom inoculation.

Fig. 4. PCA score plot of the apple juice data related to both the uncontaminated(‘‘no” label) and contaminated (‘‘yes” label) by A. acidoterrestris samples at 24 hfrom inoculation.

Fig. 5. PCA score plot of the peach juice data related to uncontaminated andcontaminated by A. acidoterrestris ATCC 49025 and A. acidocaldarius ATCC 27009samples.

Fig. 3. PCA score plot of the orange juice data related to both the uncontaminated(‘‘no” label) and contaminated (‘‘yes” label) by A. acidoterrestris samples at 24 hfrom inoculation.

E. Gobbi et al. / Food Control 21 (2010) 1374–1382 1377

Author's personal copy

selective towards different volatile organic compounds. A suitableelectronics was used to measure the variations of conductivity thatoccur when the sensing layer interact with the VOCs contained inthe headspace of the measured sample. The sensor chamber waskept at a temperature of 55 �C and was also equipped with a rela-tive humidity sensor for constantly monitoring its inner relativehumidity.

The EN was equipped with the automated and programmablestatic headspace system HT200 (HTA srl, Brescia, Italy), supportinga 40 loading sites carousel and a shaking incubator to equilibratethe sample headspace.

The individual samples consisted in 10 ml aliquots asepticallyremoved from the sample juice and placed into sterile 20 ml chro-matographic vials, crimped with an aluminium cap and a coatedTeflon septum. In each experiment they were placed in a random-ized fashion into the HT200 carousel.

The sensor baseline was performed by using synthetic chro-matographic air with a continuous flow rate of 10 ml/min. Eachvial was incubated at 40 �C for 10 min into the HT200 oven byshaking it for the entire incubation. The sample headspace (4 ml)was then drawn out from the vial in static headspace fashion andinjected into the carried flow (speed 4 ml min�1) through a prop-erly modified gas chromatography injector (kept at 60 �C to pre-vent any condensation). The sensors’ recovery time was 28 min.

2.5.2. Data analysis for ENAll the sensor response patterns were digitized and recorded

using the software package included in the electronic nose. Thedata were analyzed with Exploratory Data Analysis (EDA), a writ-ten-in-house software package based on MATLAB� that includesthe usual descriptive statistics functions such as feature plots,box plots, Pearson correlation matrix among the features, and prin-cipal component analysis (PCA) (Massart et al., 1997) with theaddition of utilities for easy data manipulation (e.g. data sub sam-pling, data set fusion) and plots customization (Pardo et al., 2000;Vezzoli et al., 2008).

Initially, the raw measurements (sensor resistance vs. time)were checked in order to purge the data either from human errorsor instrumental malfunctioning, and to remove outliers.

Pre-processing of EN data consisted in extracting the most sig-nificant features from the sensors response curves. Previous workshave shown that the amplitude of sensor response normalized forthe baseline is often well correlated with the type of odour and inmany cases drives the main part of the information. For this reason,in this study, we extracted the classical feature R/R0, where R0 isthe baseline resistance of the sensor in air and R is the amplitudeof the maximum/minimum resistance during the time exposureto the odorant, leading to a 6-dimensional feature space. Data werenormalized by taking z-score values across rows (i.e. zero mean andunit variance) which makes the pattern less dependent on the con-centration (Massart et al., 1997).

Box plots were used to assess individual sensors and featuresperformance and to identify outliers. In each box plot differentproperties of the data distribution are summarized with indica-tions of the three quartile values, being the median the line inthe middle; moreover the whiskers extending from each box showthe extent of the tails of the sample distribution, outliers being thedata with values beyond the ends of the whiskers.

Pearson correlation matrix is a graphical tool used to visualizesimilarities and differences between sensors responses; correlationvalues span from 0 to 1, where 0 means no correlation (completelydifferent behaviour between the sensors, i.e. completely differentinformation) and 1 indicates the maximum correlation degree(identical behaviour); for this reason, uncorrelated features arepreferred.

PCA was finally used for visualising the multidimensional dataand evaluating the unsupervised EN classification capability.

2.5.3. Dynamic headspace-GC–MS characterization of the volatileprofile

One milliliters aliquot of orange juice was placed in a 250 mlErlenmeyer flask at 40 �C and diluted by adding 9 ml of UltraResi-Analysed water. After an equilibration time of 15 min, puri-fied helium (75 ml min�1) was passed through the system for10 min and the extracted volatiles were adsorbed on a glass tube(16 � 0.4 cm i.d.) trap filled with Tenax TA (90 mg, 20–35 mesh)(Chrompack, Middelburg, The Netherlands). The volatile com-pounds were subsequently thermally desorbed and submitted toGC–MS analysis by using the same conditions reported in a previ-ous study (Bianchi et al., 2009).

3. Results

3.1. Microbiology

When the A. acidoterrestris strains ATCC 49025 and the A. acido-caldarius strain ATCC 27009 were inoculated into peach and orangejuices, the population levels were not increased after 24 h of incu-bation as confirmed by the c.f.u. counting (data not shown); for thisreason only the inocula concentrations were reported and consid-ered as variables. Differently these bacteria grew regularly in dif-ferent apple juices.

3.2. Electronic nose

3.2.1. Contamination detection in peach, orange and apple juiceWhen the headspaces of peach and orange juices were consid-

ered only four sensors (CJ1315, SB0222, ZH0531, and SJ0729) outof six resulted to be able to distinguish the contaminated samplesby exploring their box plots. On the contrary, the sensors SD0315and WHT15 were not able to discriminate between the two clas-ses; therefore they were discarded from subsequent analyses.

Although the four sensors were generally able to separate con-taminated and uncontaminated samples, the discrimination per-formance was dependent on the fruit juice and on the sensortype as shown by the individual sensor behaviour (Fig. 1).

In the apple juice the sensors were poorly effective in separat-ing the two classes, though only SJ0729 showed a complete overlapof contaminated and uncontaminated samples (Fig. 1, column c).

With regard to peach (Fig. 1, column a) and orange (Fig. 1, col-umn b) juices, the sensors showed some specificity related to thejuice matrix as far as behaviour and performance. For examplethe sensor CJ1315 showed a counter-correlated behaviour onpeach juice with respect both to orange and apple juices (Fig. 1,first row), similarly to SB0222 but differently from ZH0531. Onthe other hand, SB0222 separated better the contaminated samplesin peach juice, while SJ0729 performed better in orange.

When PCA was performed on the data from the headspaces of12 peach samples at 24 h from inoculation the uncontaminatedand contaminated samples were distributed along PC1 accordingto the presence of Alicyclobacillus spp. Two clusters were clearlyseparated corresponding to the control (uncontaminated) peachjuice samples on the left-hand side and to the Alicyclobacillusspp. contaminated on the right-hand side respectively (Fig. 2).The first two principal components (PC1 and PC2) accounted for80.5% and 19.4% of the variance (information) of the original datamatrix respectively.

Similarly, two clusters were present in the PCA obtained fromthe data of the headspaces of 12 orange juice samples at 24 h frominoculation (Fig. 3); these groups corresponded to the contami-

1378 E. Gobbi et al. / Food Control 21 (2010) 1374–1382

Author's personal copy

nated with A. acidoterrestris (on the left) and uncontaminated sam-ples (on the right), and they were mainly separated on the PC1,that accounted for 76% of the variance.

Thus in peach and orange juices, the capability of EOS835 to dis-tinctly detect the presence of A. acidoterrestris cells was confirmedas early as 24 h from inoculation, before any evidence of spoilageand visible alteration, could be noted.

Differently, in the apple juice, the discrimination between thecontaminated and uncontaminated samples by the EOS835 was lessevident, as shown is the PCA plot (Fig. 4).

3.2.2. EN specificityThe headspaces of the peach juice samples, both uncontami-

nated and artificially contaminated by one of the two Alicyclobacil-lus species (A. acidoterrestris or A. acidocaldarius), were used to testthe specificity of the EOS835 to perform intragenus classification.These tests aimed to verify the capability of the EN to diagnosejuice contamination in spite of the presence of different speciesof bacteria.

When the box plots of the four sensors were examined, two ofthem (CJ1315, SJ0729) were not able to distinguish the juice sam-ples contaminated by the two species, while there was little sepa-ration of uncontaminated samples. Conversely, the two othersensors (SB0222 and ZH0531) appeared able to discriminate be-tween the two Alicyclobacillus species, as well as to distinctly sep-arate the contaminated samples from the uncontaminated ones(data not shown).

In the PCA score plot obtained with the four sensors (Fig. 5), thepeach juice samples were separated in two clusters along the PC1.One cluster consisted of uncontaminated samples (on the right)while the second large cluster (on the left) was made of samplescontaminated by the two different species. The A. acidoterrestrisand A. acidocaldarius samples were grouped closely together, yetit was possible to evidence a weak discrimination between thetwo classes that did not impair the classification of contaminatedsamples.

3.2.3. Analytical sensitivity of ENIn order to assess the analytical sensitivity of the EOS835, orange

juice was contaminated with low concentrations of A. acidoterres-tris (up to 102 c.f.u. ml�1).

The PCA score plot of the orange juice samples (Fig. 6a) showednot only a distinct separation of the sterile control samples fromthe artificially contaminated samples, even if these were inocu-lated with very low concentrations of A. acidoterrestris, but also adistinction of the two different concentrations of bacterial cells(100 c.f.u./ml and <100 c.f.u./ml). The separation of the sampleswas on the PC1 mainly.

Not all the sensors have the same sensitivity. Indeed, by exam-ining the sensor box plots, it was evident that SJ0729 (Fig. 6b) wasthe best one in discriminating among the different bacterial cellsconcentrations and detecting even the lowest bacterial load.

3.3. Dynamic headspace-GC–MS analyses

Dynamic headspace (DHS)-GC–MS analyses were performed onuncontaminated and contaminated orange samples after 24 h frominoculum in order to evaluate possible differences occurring in thechemical volatile profile during the bacterium growth.

From a qualitatively point of view, juice volatile chemical pro-file is not heavily altered by A. acidoterrestris, the same volatilecompounds being present both in uncontaminated and contami-nated juice. The main difference lies in the relative amount of somecompounds: the volatile profile of contaminated samples shows anincrease in the amount of some volatile (as some terpenic alcohols,aldehydes and ketones), whereas a decrease of other compounds

could be detected (as some terpenic hydrocarbons) with respectto the volatile profile of the uncontaminated juice (Fig. 7).

4. Discussions

It has long been established that smell can be used to diagnosefood spoilage (Haugen, 2001, Karlshøj, Nielsen, & Larsen, 2007) asdifferent bacteria generate a characteristic ‘‘smell” and can there-fore be differentiated from uncontaminated samples, allowing adiagnosis.

Recently, the same EN used in this study was shown to diagnoseFusarium spp. and fumonisins contamination in maize (Falasconiet al., 2005b) as well as the presence of some bacteria in tomatosauce (Concina et al., 2009). Here its application to the diagnosisof Alicyclobacillus spp. in different fruit juices was investigated.

Indeed this study is the first report of the application of an EN tothe detection of A. acidoterrestris and A. acidocaldarius in differentfruit juices. Exploratory data analysis showed how contaminatedsamples could be separated from uncontaminated ones by theEOS835 and this result was constantly confirmed for all the testedjuices and in all the experiments, also in the presence of verylow concentrations of bacterial cells. Hence, this work demon-strates that EN technology can be applied to detect early contam-inated peach, orange and apple juices even before any visible signof spoilage.

Fig. 6. EN sensitivity. (a) PCA score plot of data related to orange juice samplesinoculated with 100 and <100 c.f.u./ml of A. acidoterrestris; (b) Box plot of the bestperforming sensor SJ0729 on the same measurements.

E. Gobbi et al. / Food Control 21 (2010) 1374–1382 1379

Author's personal copy

Some specificity of the sensors response was observed for thedifferent juice matrices. This has implication on the EN trainingwhich has to be done separately on different juices, i.e. the trainingdata collected on one juice matrix are not transferable to otherjuices.

Alicyclobacillus spp. appeared to alter the volatile chemical pro-file of fruit juices, thus allowing discrimination between the sam-ples even after 24 h from inoculation. The EN response could becaused by the combined effects of: (i) microbial metabolites and(ii) volatile cellular compounds. At this stage, it is not possible todetermine precisely which volatiles are responsible for the sensorresponse; however, the GC–MS characterization of the volatile pro-file of orange juices has confirmed the existence of different VOCspatterns between contaminated and uncontaminated samples con-sisting exclusively in quantitative variation of some compounds.Taking into account that all the factors affecting the developmentof VOCs, as thermal treatment and incubation, were maintainedconstant for both contaminated and uncontaminated samples,these observed differences can be ascribed to the microbial spoil-age. Thus, the orange juice samples discrimination, evident in theEN data, could be associated to the different composition of theirvolatile fractions as shown by GC–MS. On the basis of these find-ings observed for orange juice, also the differences revealed byEN in the peach juice samples, could be ascribed to the same rea-sons as in orange juice.

In a pilot study we rarely found sterile commercial juices to beused as negative control. For this reason, sterilization of juices wasnecessary here. Obviously the treatment affected both their micro-

biological features and their headspace composition although theVOCs profile modifications seems to consist only in slight changesas verified by GC–MS in autoclaved and not autoclaved orangesamples (Bianchi et al., in press). Therefore it is reasonable to con-sider that such changes do not to impair the significance of thisstudy even if the obtained results should be confirmed on not auto-claved juices.

The limit of detection of EN was not easily determined as thebacteria showed a ‘‘fastidious” behaviour when cultivated both inpeach and orange juices, in addition to most media as commonlyreported. The cultural difficulties shown mostly by the culture col-lections strains could have been derived from the inactivation ofsome metabolic pathways due to their long permanence in culture,or from not optimal growth conditions such as low incubationtemperature and available oxygen amount (Walker & Phillips,2005). Nonetheless in this study priority was given to an experi-mental set up close to the conditions of shelf storage at room tem-perature. Also c.f.u. counting from juices was troublesome at thetested concentrations. In literature bacterial enumeration at lowconcentrations is reported as imprecise and difficult (Duvenage,2006; Lee, Chang, Shin, & Kang, 2007). Consequently, in peachand orange juices, it can only be stated that EOS835 was able to de-tect contamination in samples inoculated 24 h earlier with as littleas 10 and <102 c.f.u./ml respectively.

Contrarily, all the tested A. acidoterrestris strains grew well inapple juice but unfortunately in this matrix the EOS835 perfor-mance was not satisfying. Up to date, only hypotheses can be doneto explain this finding: it could be related to a reduced amount of

Fig. 7. Representative TIC GC–MS chromatograms of the volatile fraction obtained from (a) uncontaminated and (b) contaminated orange juice.

1380 E. Gobbi et al. / Food Control 21 (2010) 1374–1382

Author's personal copy

contamination-specific VOCs changes in apple juice, below the lim-it of detection of the instrument; or because the apple juice vola-tiles masked Alicyclobacillus spp. featuring VOCs; or finallybecause the sensitivity of the sensors changed with the matrixand was reduced in apple juice.

Despite these weaknesses, EN technology holds a large poten-tial for future developments since it offers certain advantages, suchas cost- and time effectiveness, robustness, simplicity, and opera-tor independence, in contrast to molecular or immunological basedassays. Great efforts are devoted to improve the chemical sensingtechnology to produce more sensitive, selective, and stable sensors.Yet, partial selectivity is not always a disadvantage. In this study,for example, the EN intragenus specificity lower than the genusspecificity can be an advantage because from a practical point ofview it is possible to perform the EN training to diagnose bacterialcontamination over a limited numbers of species without havingthe classification capability of contaminated samples substantiallyaffected by other species.

The described method is not yet fully optimized for ‘‘field”application, being this work focused on proving the feasibilityand potentiality of the method. Together with an appropriate clas-sification model, EN technology has the potential to become a rapidand automated system for the early diagnosis of fruit juice contam-ination by Alicyclobacillus spp. Also, with an appropriate amount ofmeasurement, output data can be processed and analyzed by moresophisticated statistical learning algorithms which can contributeto improve the EN performance.

References

Bahçeci, K., & Acar, J. (2007). Determination of guaiacol produced by Alicyclobacillusacidoterrestris in apple juice by using HPLC and spectrophotometric methods,and mathematical modeling of guaiacol production. European Food Research andTechnology A, 225(5–6), 873–878.

Balamatsia, C. C., Paleologos, E. K., Kontominas, M. G., & Savvaidis, I. N. (2006).Correlation between microbial flora, sensory changes and biogenic aminesformation in fresh chicken meat stored aerobically or under modifiedatmosphere packaging at 4 �C: Possible role of biogenic amines as spoilageindicators. Antonie van Leeuwenhoek, 89(1), 9–17.

Bianchi, F., Careri, M., Mangia, A., Mattarozzi, M., Musci, M., Concina, I., et al. (2009).Differentiation of the volatile profile of microbiologically contaminated cannedtomatoes by dynamic headspace extraction followed by gas chromatography–mass spectrometry analysis. Talanta, 77, 962–970.

Bianchi, F., Careri, M., Mangia, A., Mattarozzi, M., Musci, M., Concina, I., & Gobbi, E.(in press). Characterization of the volatile profile of orange juice contaminatedwith Alicyclobacillus acidoterrestris. doi:10.1016/j.foodchem.2010.05.023.

Borlinghaus, A., & Engel, R. (1997). Alicyclobacillus incidence in commercial applejuice concentrate (AJC) supplies: Method development and validation. FruitProcessing, 7, 262–266.

Brown, K. L. (1995). New microbiological spoilage challenges in aseptics:Alicyclobacillus acidoterrestris spoilage in aseptically packed fruit juices. In T.Ohlsson (Ed.), Advances in aseptic processing and packaging technologies(pp. 1–14). Sweden: Gothenburg.

Bunge, M., Araghipour, N., Mikoviny, T., Dunkl, J., Schnitzhofer, R., Hansel, A., et al.(2008). On-line monitoring of microbial volatile metabolites by proton transferreaction–mass spectrometry. Applied and Environmental Microbiology, 74(7),2179–2186.

Cerny, G., Hennlich, W., & Poralla, K. (1984). Spoilage of fruit juice by bacilli:Isolation and characterization of the spoilage organism. Zeitschrift furLebensmittel-Untersuchung und-Forschung, 179, 224–227.

Chang, S., & Kang, D. H. (2004). Alicyclobacillus spp. in the fruit juice industry:History, characteristics, and current isolation/detection procedures. CriticalReviews in Microbiology, 30, 55–74.

Concina, I., Falasconi, M., Gobbi, E., Bianchi, F., Musci, M., Mattarozzi, M., et al.(2009). Early detection of microbial contamination in processed tomatoes byelectronic nose. Food Control, 20, 873–880.

Duvenage, W. (2006). Detection and isolation of Thermophilic Acidophilic Bacteriafrom fruit juices. Thesis of Master of Science in Food Science, StellenboschUniversity. <https://etd.sun.ac.za/jspui/bitstream/10019/56/1/DuvenW.pdf>.

Edwards, R. A., Dainty, R. H., & Hibbard, C. M. (1987). Volatile compounds producedby meat pseudomonads and relate reference strains during growth on beefstored in air at chill temperatures. Journal of Applied Bacteriology, 62(5),403–412.

Falasconi, M., Gobbi, E., Pardo, M., Della Torre, M., Bresciani, A., & Sberveglieri, G.(2005b). Detection of toxigenic strains of Fusarium verticillioides in corn byelectronic olfactory system. Sensors and Actuators B, 108, 250–257.

Falasconi, M., Pardo, M., Sberveglieri, G., Riccò, I., & Bresciani, A. (2005a). The novelEOSP835 electronic nose and data analysis for evaluating coffee ripening. Sensorsand Actuators B, 110(1), 73–80.

Fischer, G., Schwalbe, R., Ostrowski, R., & Dott, W. (1998). Airborne fungi and theirsecondary metabolites in working places in a compost facility. Mycoses, 41(9–10), 383–388.

Haugen, J. E. (2001). Electronic noses in food analysis. Advances in ExperimentalMedicine and Biology, 488, 43–57.

Jay, J. M. (1998). High-temperature food preservation and characteristics ofthermophilic microorganisms. In Modern Food Microbiology (5th ed., pp. 354–355). New York: Chapman & Hall.

Jensen, N. (1999). Alicyclobacillus – a new challenge for the food industry. FoodAustralia, 51, 33–36.

Jensen, N., & Whitfield, F. B. (2003). Role of Alicyclobacillus acidoterrestris in thedevelopment of a disinfectant taint in shelf-stable fruit juice. Letters in AppliedMicrobiology, 36, 9–14.

Jørgensen, L. V., Huss, H. H., & Dalgaard, P. (2001). Significance of volatilecompounds produced by spoilage bacteria in vacuum-packed cold-smokedsalmon (Salmo salar) analyzed by GC–MS and multivariate regression. Journal ofAgricultural and Food Chemistry, 49(5), 2376–2381.

Karlshøj, K., Nielsen, P. V., & Larsen, T. O. (2007). Differentiation of closely relatedfungi by electronic nose analysis. Journal of Food Science, 72(6), M187–M192.

Lee, S. Y., Chang, S. S., Shin, J. H., & Kang, D. H. (2007). Membrane filtration methodfor enumeration and isolation of Alicyclobacillus spp. From apple juice. Letters inApplied Microbiology, 45(5), 540–546.

Lloyd, S. W., Grimm, C. C., Klich, M. A., & Beltz, S. B. (2005). Fungal infections offresh-cut fruit can be detected by the gas chromatography–mass spectrometricidentification of microbial volatile organic compounds. Journal of FoodProtection, 68(6), 1211–1216.

Massart, D. L., Vandeginste, B. G. M., Buydens, L. M. C., De Jong, S., Lewi, P. J., &Smeyers-Verbeke, J. (1997). Principal components. In Handbook of chemometricsand qualimetrics (pp. 519–556). Amsterdam: Elsevier Science.

Mayrhofer, S., Mikoviny, T., Waldhuber, S., Wagner, A. O., Innerebner, G., Frank-Whittle, I. H., et al. (2006). Microbial community related to volatile organiccompound (VOC) emission in household biowaste. Environmental Microbiology,8, 1960–1974.

Morales, P., Fernández-García, E., & Nuñez, M. (2005). Production of volatilecompounds in cheese by Pseudomonas fragi strains of dairy origin. Journal ofFood Protection, 68(7), 1399–1407.

Murdock, D. I., & Hatcher, W. S. (1975). Growth of microorganisms in chilled orangejuice [Lactobacillus, Leucomostoc]. Journal of Milk and Food Technology, 38(7),393–396.

Orr, R. V., Shewfelt, R. L., Huang, C. J., Tefera, S., & Beuchat, L. R. (2000).Detection of guaiacol produced by Alicyclobacillus acidoterrestris in applejuice by sensory and chromatographic analyses, and comparison with sporeand vegetative cell populations. Journal of Food Protection, 63(11),1517–1522.

Pardo, M., Niederjaufner, G., Benussi, G., Comini, E., Faglia, G., Sberveglieri, G.,et al. (2000). Data preprocessing enhances the classification of differentbrands of Espresso coffee with an electronic nose. Sensors and Actuators B,69(3), 397–403.

Pardo, M., & Sberveglieri, G. (2004). Electronic olfactory systems based on metaloxide semiconductor sensor arrays. MRS Bulletin, 29(10), 703–708.

Peris, M., & Escuder-Gilabert, L. (2009). A 21st century technique for food control:Electronic noses. Analytica Chimica Acta, 638(1), 1–15.

Pettipher, G. L., & Osmundson, M. E. (2000). Methods for the detection,enumeration and identification of Alicyclobacillus acidoterrestris. FoodAustralia, 57, 293.

Pettipher, G. L., Osmundson, M. E., & Murphy, J. M. (1997). Methods for thedetection, enumeration and identification of Alicyclobacillus acidoterrestris andinvestigation of growth and production of taint in fruit juice-containing drinks.Letters in Applied microbiology, 24, 185–189.

Phillips, M., Cataneo, R. N., Condos, R., Ring Erickson, G. A., Greenberg, J., LaBombardi, V., et al. (2007). Volatile biomarkers of pulmonary tuberculosis in thebreath. Tuberculosis, 87(1), 44–52.

Previdi, P., Colla, F., & Vicini, E. (1995). Characterization of Alicyclobacillus, asporeforming thermophilic acidophilic bacterium. Industria Conserve, 70,128–132.

Ragaert, P., Devlieghere, F., Devuyst, E., Dewulf, J., Van Langenhove, H., &Debevere, J. (2006). Volatile metabolite production of spoilage micro-organisms on a mixed-lettuce agar during storage at 7 degrees C in airand low oxygen atmosphere. International Journal of Food Microbiology,112(2), 162–170.

Reid, L. M., O’Donnell, C. P., & Downey, G. (2006). Recent technological advances forthe determination of food authenticity. Trends in Food Science and Technology,17(7), 344–353.

Röck, F., Barsan, N., & Weimar, U. (2008). Electronic nose: Current status and futuretrends. Chemical Reviews, 108, 705–725.

Sberveglieri, G. (1995). Recent developments in semiconducting thin-film gassensors. Sensors and actuators B, 23(2–3), 103–109.

Splittstoesser, D. F., Churey, J. J., & Lee, C. Y. (1994). Growth characteristics ofaciduric sporeforming bacteria isolated from fruit juices. Journal of FoodProtection, 57, 1080.

Splittstoesser, D. F., Lee, C. Y., & Churey, J. J. (1998). Control of Alicyclobacillus in thejuice industry. Dairy Food and Environmental Sanitation, 18, 585–587.

E. Gobbi et al. / Food Control 21 (2010) 1374–1382 1381

Author's personal copy

Vezzoli, M., Ponzoni, A., Pardo, M., Falasconi, M., Faglia, G., & Sberveglieri, G. (2008).Exploratory data analysis for industrial safety application. Sensors and ActuatorsB, 131(1), 100–109.

Walker, M., & Phillips, C. A. (2005). The effect of intermittent shaking, headspaceand temperature on the growth of Alicyclobacillus acidoterrestris in stored applejuice. International Journal of Food Science and Technology, 40, 557–562.

Walls, I., & Chuyate, R. (1998). Alicyclobacillus – Historical perspective and preliminarycharacterization study. Dairy Food and Environmental Sanitation, 18, 499–503.

Wisotzkey, J. D., Jurtshuk, P., Jr., Fox, G. E., Deinhard, G., & Poralla, K. (1992).Comparative sequence analysis on the 16S (rDNA) of Bacillus acidocaldarius,Bacillus acidoterrestris and Bacillus cycloheptanicus and a proposal for creation ofa new genus Alicyclobacillus gen Nov. International Journal of SystematicBacteriology, 42, 263–269.

Wisse, C. A., & Parish, M. E. (1998). Isolation and enumeration of sporeforming,thermoacidophilic, rod-shaped bacteria from citrus processing environments.Dairy Food and Environmental Sanitation, 18, 504–509.

Wood, W. L., Higbee, D. J., Gooldy, M., Glogowski, S., Fitzpatrick, R., Karalus, R. J.,et al. (2006). Analysis of volatile bacterial metabolites by GC–MS. LC GC NorthAmerica, 24, 170–188.

Yamazaki, K., Teduka, H., & Shinano, H. (1996). Isolation and identification ofAlicyclobacillus acidoterrestris from acidic beverages. Bioscience, Biotechnology,and Biochemistry, 60, 543–545.

Ziegler, B., Siegmund, B., & Pfannhauser, W. (2004). Determination of off-flavourcompounds in apple juice caused by microrganisms using headspace solid phasemicroextraction-gas chromatography–mass spectrometry. Analytica ChimicaActa, 520, 3–11.

1382 E. Gobbi et al. / Food Control 21 (2010) 1374–1382