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Oct 4, 2012 - Abstract—This paper presents three different applications of an electronic nose (EN) based on a metal oxide sensor array, in order to illustrate ...
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IEEE SENSORS JOURNAL, VOL. 12, NO. 11, NOVEMBER 2012

Electronic Noses as Flexible Tools to Assess Food Quality and Safety: Should we Trust Them? Isabella Concina, Matteo Falasconi, and Veronica Sberveglieri

Abstract— This paper presents three different applications of an electronic nose (EN) based on a metal oxide sensor array, in order to illustrate the broad spectrum of potential uses of the technique in food quality control. The following scenarios are considered: 1) the screening of a typical error that may occur during the processing of tomato pulp, which leads to sensory damage of the product; 2) the detection of microbial contamination by Alicyclobacillus spp. (ACB) affecting soft drinks; and 3) the proof of evidence of extra virgin olive oil fraudulently adulterated with hazelnut oil. In each case, the EN is able to identify the spoiled product by means of the alterations in the pattern of volatile compounds, reconstructed by principal component analysis of the sensor responses.

the flavor. Between them, a final pasteurisation step is carried out in order to lower the microbial load, during which errors can occur, related to an over-heating, in either temperature or time. Although products’ safety is retained, sensory quality could be altered by the appearance of off-flavors, such as dimethyl sulfide and acetaldehyde, rendering unpleasant the consumption (due to the appearance of “cooked” smell of final products [2]). The evaluation of this problem is currently done by trained human panels, smelling some samples within a given lot ready for the market.

Index Terms— Electronic nose (EN), extra virgin olive oil, food quality control, soft drinks, tomato pulp.

B. Scenario Description: Alicyclobacillus spp. in Soft Drinks

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I. I NTRODUCTION

N THE last decade, Electronic Noses (ENs) have become very popular as monitoring tools in evaluating food quality and safety [1]. Since most food adulterations are reflected on volatile chemical profile, ENs appear as excellent candidates for process monitoring, freshness evaluation, shelf-life investigation, sensory and authenticity assessment, microbial contamination diagnosis. In this paper, three significant applications of ENs in food control are discussed, covering three relevant issues in the field of food quality control and aiming to illustrate the broad spectrum of potential uses of sensor technology in this field. A. Scenario Description: Tomato Pulp The characteristic tomato and tomato-derived products flavor is determined by volatile substances (about 400 volatiles have been identified), which develop during ripening, comminution and process of the fruits. Several heat treatments are envisaged in the process of the raw matter, which change both qualitatively and quantitatively the chemicals responsible for Manuscript received November 1, 2011; accepted March 28, 2012. Date of publication April 26, 2012; date of current version October 4, 2012. This work was supported by the FIRB Project Rete Nazionale di Ricerca sulle Nanoscienze ItalNanoNet, Protocollo: RBPR05JH2P, 2009–2013, MIUR. The associate editor coordinating the review of this paper and approving it for publication was Dr. Perena I. Gouma. I. Concina and M. Falasconi are with the CNR-IDASC SENSOR Laboratory, Brescia 25131, Italy, and also with the University of Brescia, Brescia 25133, Italy (e-mail: [email protected]; [email protected]). V. Sberveglieri is with the CNR-IDASC SENSOR Laboratory, Brescia 25131, Italy, and also with the University of Modena and Reggio Emilia, Reggio Emilia 42122, Italy (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/JSEN.2012.2195306

Alicyclobacillus spp. (ACB) were first identified in 1982 [3] in aseptically filled apple juice from Germany. ACB are Gram positive, thermoacidiphilic, spore-forming bacteria often isolated from soils in volcanic areas and hot springs. They are extensively studied, because of their ability to survive the sterilisation process, developing some days after bottling, thus eluding routine controls carried out before marketing the final products. The source of contamination is almost impossible to identify, since ACB have been found not only in raw fruit used in process lines, but also in sugar, salt and water [4], [5]. ACB spoil the final products by the production of taint volatile compounds (namely 2-methoxy- and 2,6-alo-phenols), which impart unpleasant smell and taste, often described as “medicinal” and “hospital.” Every year, these bacteria cause relevant damages, both economical and for corporate image, with an incredible diffusion [6]. A strategy to prevent their presence has not yet been found, being thus an early identification of presence the only defence for the producers. Unfortunately at present analytical techniques, both chemical and microbiological, are able to diagnose ACB’s presence only through the detection of these metabolites, i.e., once the products are already contaminated. C. Scenario Description: Fraudulent Dilution of Olive Oil With Hazelnut Oil Dilution of high quality extravirgin olive oil with low quality hazelnut oil is common fraud exerted in the Mediterranean area, which has serious reflections on both economy and health. On one hand, olive oil production plays a fundamental role in farmers’ income mainly living in less favoured European regions, where olive oil market is a promising sector for sustainable development. On the other hand, hazelnut oil poses a threat on consumers’health, presenting some risks to allergic consumers [7]. Differently from other vegetable oils, such as

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TABLE I M ETAL O XIDE S ENSOR A RRAY U SED IN T HIS PAPER Sensor number 1 2 3 4 5 6

Sensor code CJ1208 ST0311 SD0403 BB4108 SJ1001 SnRAu48

Sensitive layer SnO2 cat SiO2 SnO2 cat Ag SnO2 cat Mo WO3 SnO2 SnO2 cat Au

(a)

(a) (b) Fig. 2. PCA score plot of (a) a soft drinks based on peach juice for which a clear discrimination between uncontaminated (black xs) and contaminated (green circles) samples is feasible on PC3 axis. In (b), the PCA score plot is reported for an apple-juice-based soft drink, for which EN was not able to identify the spoiled samples.

(b) Fig. 1. (a) PCA score plot showing the discrimination along the PC1 axis of in-standard tomato pulp (orange circles) and oversterilized product (black xs). (b) Pearson correlation matrix and (c-g) box plots referring to tomato pulp analysis.

those obtained from rapeseed, sunflower, palm, hazelnut oil possesses a very similar composition to EVOO with respect to fatty acids content, which makes difficult to distinguish adulterated products trough the analytical strategies defined by the European regulation [CEE n. 796/2002]. II. E XPERIMENTAL A. EN and Data Analysis The EN EOS835 , described in former works [8], was equipped by a custom array of six thin film semiconductor metal oxide (MOx) sensors (Table I). The temperature of the sensor chamber was kept constant (55 °C) and monitored by a dedicated sensor. The use of synthetic dry air as carrier gas allowed keeping very stable baseline sensors conditions. Sensor stability over the time has been monitored by during each measurement session by the use of a standard (1-butanol), toward which sensors are expected to give the same response over the time.

Sampling was performed by an automated autosampler (HT200, HTA srl, Italy), equipped by a carousel loading 40 positions and a shaker/oven. 20 ml vials were used, each of them filled with 10 ml of sample and sealed with silicone coated rubber caps. Each sample was incubated under stirring at 40 °C for 10 minutes in order to generate the sample headspace. As a first step in EN’s data pre-processing, significant features were extracted from the sensors’ response curves. The chosen feature was then R/R0, where R is the minimum of sensor resistance during the exposure to sample headspace, and R0 is the baseline resistance, already self-normalized between 0 and 1. Explorative data analysis was performed by Principal Component Analysis (PCA). Data were processed by EDA software, a home written software developed in MATLAB at SENSOR laboratory [9]. B. Sample Preparation 1) Over-Sterilization of Tomato Pulp: Commercial tomato samples were submitted to an over-sterilisation procedure, simulating what could happen during the process of the justpacked processed product. To this aim, commercial tomato pulp of the same production lot was acquired. Samples labelled as “Original” were analyzed as received and used as reference for an in-standard product. The other samples, labelled as “Adulterated,” were submitted, before opening the packaging,

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Fig. 3. Box plots and Pearson correlation matrix for the five sensor responses evaluated in oil dilution analysis. Hazelnut samples are read by every sensor with the lowest variance in the analyzed batch, while the class labeled as dil, comprising the EVOO diluted with different percentage of hazelnut oil, present the maximum variance.

to a 1 minute sterilisation step in autoclave (120 °C). After cooling, the samples were opened and divided in 10 ml aliquots. 2) Soft Drinks Contamination by Alicyclobacillus spp. (ACB): Commercially flavored, low energy non-carbonated drinks, containing 3% of fruit juice, provided by a producer as typically shelf-stable, high-acid, non-carbonated products packaged in PET containers, whithout preservatives were considered. The beverages were naturally contaminated by ACB, and submitted as received to EN analysis. Uncontaminated samples of each beverage were used as control. Before opening, each drink bottle was vigourously stirred for homogeneity purposes. 3) Olive Oil Adulteration With Hazelnut Oil: A commercial extra virgin olive oil (EVOO) was diluted with increasing volumes of hazelnut oil (5 to 25% V/V); undiluted oil was

used as reference sample. Pure EVOO and diluted samples were randomly analyzed. III. R ESULTS AND D ISCUSSION A. Tomato Pulp Fig. 1 summarizes the results obtained for tomato pulp samples. Fig. 1 (top) shows the PCA score plot of data: data related to original pulp, commercialized as an in-standard product, are perfectly separated on PC1 axis from data related to oversterilised tomato. The Pearson correlation matrix indicate two blocks in the sensor array: sensors 1, whose box plot is reported as representative of the first block, and 2, both based on SnO2 but catalysed by SiO2 and Ag, respectively, behave in the same way, while a sub-matrix is constituted by the other four sensors (Fig. 1 (bottom)). 1 and 4 (based on WO3 ) revealed

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the best sensors in the array: medians of the two classes show no overlap, while the variance keeps quite low. Sensor 5 (based on uncatalysed SnO2 ) is on the contrary almost blind toward tomato samples discrimination: medians result indeed very near as feature value and a large variance is detected. The other sensors (3 and 6, based on SnO2 catalysed Mo and Au, respectively) are able to keep separated the medians of the two classes, but variances are overlapped, as indicated by the extent of the whiskers, thus indicating a not perfect discrimination capability. B. Soft Drinks Fig. 2 reports the PCA score plots for peach juice- (top) and apple juice- (bottom) based soft drinks. EN response toward microbial presence could be caused by the combined effects of 1) microbial metabolites produced during the growth of microorganisms and 2) volatile cellular compounds. As for peach- and pear-based beverages (PCA score plot for peach juice-based beverage is reported as representative), EN EOS835 demonstrated a noteworthy ability in discriminating uncontaminated samples from contaminated ones, reaching a classification rate between 98 and 100%. Real time polymerase chain reaction (RtPCR) identified very low levels of Alicyclobacillus spp. (tens of copies/ml). Besides, relatively low amounts of 2-methoxy-phenol, the principal main taint metabolite, were quantified by high performance liquid chromatography, thus suggesting that the discrimination capability is not related to the presence of guaiacol, as it is for classical analysis. The case of apple juice-based soft drink revealed completely different: EN was found to be completely blind toward this beverage, as it can be seen from the PCA score plot, where data related to contaminated and uncontaminated samples are utterly overlapped each other. This result was surprising, since RtPCR indicated very similar level of contamination. Similar results were obtained when fruit juices were considered [10]. A kind of mask effect seems exerted by apple juice toward the sensor array, but at present we are not able to fully explain this finding. A detailed discussion on the analysis of soft drinks can be found in [11]. C. Olive Oil In Fig. 3 are reported the box plots and the Pearson correlation matrix for the sensor array. Sensor labelled as BB4108, based on WO3, revealed blind toward EVOO and was thus discarded from data analysis. Remaining five sensors result divided in two blocks, being the sensors 1 and 5 highly correlated each other. The second block is constituted by sensors 2, 3, 4, which behave very similarly each other, but are uncorrelated with sensors 1 and 5. Data referring to hazelnut oil present a very little variance, as evidenced from the short extent of the whiskers in box plots. A larger variance results on the contrary associated to pure and diluted EVOO samples, whose median present however no overlap in their values. The variance associated with EVOO-based samples is not surprising. As mentioned above, though rich in anti-oxidant

Fig. 4. PCA score plot of pure EVOO (lilac crosses) and EVOO diluted with 5% (black xs), 10% (green xs), and 25% (rust xs) of hazelnut oil.

compounds, EVOO is subjected to several processes changing its olfactory fingerprint. Exposure to air (and thus to oxygen), for instance once a bottle has been opened, is the first cause of these processes, followed by exposition to light. In the case of EN analysis, samples are randomly smelt and each measurement session lasts 20 hours during which samples were exposed to both air and light. However, detected variance does not impair the discrimination between diluted and pure EVOO, as evidenced in the PCA score plot shown in Fig. 4. A particular attention has been devoted to dilution percentages in the range 5-25%, identified by the European Commission as the most probable amounts used for fraudulent dilution. In Fig. 4 the PCA score plot for analyzed samples is reported (data referring to hazelnut oil have been not plotted). On PC1 axis data referring to pure and diluted EVOO are reasonably separated. The small drift along this axis is attributed to changes in pure EVOO occurring during the analysis time, which is reflected in a similar drift visible also for diluted samples. This drift tends to similarly shift all data, which are then slightly overlapped on principal component plane. Diluted samples result not perfectly separated according to the percentage of dilution volume, being identifiable an overlap of data on the plane, thus impairing a quantitative identification of the fraud. As predictable, the better separation capability is obtained for 25% diluted sample, though a noteworthy discrimination is also obtained for the other percentage dilutions. We emphasize that no sample pre-treatments were applied, since methods currently proposed to expose the fraud are based on immunoassays for trace analysis of hazelnut protein [13], DNA analysis [14], infrared spectroscopy [15], liquid chromatography [16], and all of them envisage pre-treatments of the matrix. Despite the good results presented above, it is worth to mention that attention must be paid when analysing EVOO: it is a quite delicate matrix, subjected to relevant variations, which can be either slow or fast depending on many parameters, such as quality of the original products and storage conditions.

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the variances overlap. Similar considerations can be done by taking into account the same lot analyzed during different measurement sessions, performed within two weeks (Fig. 5(c) (d)). If no precautions are taken in storing EVOO (such as elimination of empty volumes above the sample and repair from light), its evolution over the time is reflected on the olfactory fingerprint and sensors separate the same product according the measurement sessions. IV. C ONCLUSION This work testifies how ENs can serve as flexible analysing tools to face different kinds of problems in the food quality control field. All the presented studies were conducted on real food matrices, without any experimental artefact. No pretreatments, such as extraction, concentration, were applied during the analysis. This point is quite relevant and perhaps it represents the main advantage provided by ENs, because food analysis usually envisages sample preparation, which necessarily means to both extend the analysis time and alter the native matrix. At the same time, it highlights that a strict control over the machine must be always exerted. The case of EVOO is paradigmatic: it should on one hand encourage the use of an internal standard and on the other hand a prudent manipulation of data provided by the machine. On the other hand, the case of ACB contamination in soft drinks indicates that analysing very similar matrices does not guarantee the same degree of analytical success. It is worth to note that the training step plays a fundamental role in reliable EN analysis and it should be repeated as frequently as possible, especially when the sample itself is subjected to rather fast variation over the time. Although we do not believe ENs could become the analytical panacea in the food sector, they hold a real potential as monitoring instruments in this field. This is particularly true when the problem requires a yes/no response. More specific analyses will then be able to specify the qualitative and quantitative nature of the problem under study. R EFERENCES

Fig. 5. Box plots of sensors 1 and 2 responses toward (a) and (b) different lots and related PCA score plots (e), (c), and (d) EVOO from the same lot analyzed in different measurement sessions.

In order to exemplify the problems that can be encountered, let us consider two cases. In the first one different EVOO lots by the same producer are analyzed and the response of sensors considered by means of their box plots (Fig. 5(a) and (b)). Each lot is discriminated by sensors, though

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[7] M. Arlorio, J. D. Coisson, M. Bordiga, F. Travaglia, C. Garino, L. Zuidmeer, R. Van Ree, M. G. Giuffrida, A. Conti, and A. Martelli, “Olive oil adulterated with hazelnut oils: Simulation to identify possible risks to allergic consumers,” Food Addiditives Contaminants A, vol. 27, no. 1, pp. 11–18, Apr. 2010. [8] M. Falasconi, M. Pardo, M. D. Torre, A. Bresciani, and G. Sberveglieri, “The novel EOS835 electronic nose and data analysis for evaluating coffee ripening,” Sensors Actuat. B, vol. 110, no. 1, pp. 73–80, Sep. 2005. [9] M. Vezzoli, A. Ponzoni, M. Pardo, M. Falasconi, G. Faglia, and G. Sberveglieri, “Exploratory data analysis for industrial safety application,” Sensors Actuat. B, vol. 131, no. 1, pp. 100–109, Apr. 2008. [10] E. Gobbi, M. Falasconi, I. Concina, G. Mantero, F. Bianchi, M. Mattarozzi, M. Musci, and G. Sberveglieri, “Electronic nose and Alicyclobacillus spp. spoilage of fruit juices: An emerging diagnostic tool,” Food Control, vol. 21, no. 10, pp. 1374–1382, Oct. 2010. [11] I. Concina, M. Borsnek, S. Baccelliere, M. Falasconi, E. Gobbi, and G. Sberveglieri, “Alyciclobacillus spp.: Detection in soft drinks by electronic nose,” Food Res. Int., vol. 43, no. 8, pp. 2108–2114, Oct. 2010. [12] D. L. García-Gonzalez and R. Aparicio, “Detection of defective virgin olive oils by metal-oxide sensors,” Eur. Food Res. Technol., vol. 215, no. 2, pp. 118–123, Aug. 2002. [13] M. G. Bremer, N. G. E. Smits, and W. Haasnoot, “Biosensor immunoassay for traces of hazelnut protein in olive oil,” Anal. Bioanal. Chem., vol. 395, no. 1, pp. 119–126, Sep. 2009. [14] M. Woolfe and S. Primrose, “Food forensics: Using DNA technology to combat misdescription and fraud,” Trends Biotechnol., vol. 22, no. 5, pp. 222–226, May 2004. [15] V. Baeten, J. A. F. Pierna, P. Dardenne, M. Meurens, D. L. GarcíaGonzález, and R. Aparicio-Ruiz, “Detection of the presence of hazelnut oil in olive oil by FT-raman and FT-MIR spectroscopy,” J. Agric. Food Chem., vol. 53, no. 16, pp. 6201–6206, Aug. 2005. [16] D. L. García-Gonzalez, M. Viera-Macías, R. Aparicio-Ruiz, M. T. Morales, and R. Aparicio, “Validation of a method based on triglycerides for the detection of low percentages of hazelnut oil in olive oil by column liquid chromatography,” J. AOAC Int., vol. 90, no. 5, pp. 1346–1353, Sep.–Oct. 2007.

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Isabella Concina received the Laurea degree in chemistry from the University of Padova, Padova, Italy, in 2002. Her dissertation was titled “Metalorganic Chemistry.” She received the Ph.D. degree in chemistry sciences from Padova University, Padova, Italy, in 2006. Her dissertation was titled “Synthesis and catalytic reactivity of metal nanoparticles embedded in linear polymers.” She was with the Regional Agency for Environmental Prevention and Protection of Veneto, on the optimization of analytical techniques for detection of micropollutants in superficial waters and on detection and sample preparation techniques of food. She is currently a Principal Investigator in olfaction with SENSOR Laboratory, Brescia, Italy, where she works on food quality and safety control with the Electronic Olfactory System.

Matteo Falasconi received the Laurea degree (Hons.) in physics from the University of Pavia, Pavia, Italy, with a thesis on nonlinear optical properties of porous silicon, and the Ph.D. degree in material engineering from the University of Brescia, Brescia, Italy, in 2000 and 2005, respectively. He is involved in the functional characterization of thin-film metal oxide gas sensors and the development of sensing devices for food quality control, security, and medical applications through cooperation with private companies and research institutes. His current research interests include statistical learning methods for artificial and biological olfaction.

Veronica Sberveglieri received the Laurea degree in food control and safety from Modena ad Reggio Emilia University, Modena, Italy, where she is currently pursuing the Ph.D. degree. Her current research interests include coffee analysis, especially related to headspace characterization, with both chromatographic and sensor techniques.