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Electronic Tongue Distinguishes Onions and Shallots A. Legin, A. Rudnitskaya and B. Seleznev Laboratory of Chemical Sensors St-Petersburg University St-Petersburg Russia

G. Sparfel Laboratoire d’Amélioration des Plantes maraîchères INRA Domaine de Kerdevez 29250 Plougoulm France

C. Doré Station de Génétique et d’Amélioration des Plantes INRA Route de Saint-Cyr 78000 Versailles France

Keywords: edible Allium, grey shallot, Jersey (pink) shallot, recognition, sensor array Abstract

Onion and Jersey shallot belong to the same species (Allium cepa), but are from two different groups: A. cepa group cepa and A. cepa group aggregatum. Grey shallot belongs to another species, Allium oschaninii. Onions are seed-propagated, whereas shallots are vegetatively multiplied. Onions and shallots differ in taste, however, both contain sulphur organic compounds making sensory evaluation diffi-cult. There is a practical need to reliably discriminate onion from shallot. The aim of this study was to apply the electronic tongue (e-tongue) in the classification of several cultivars of onions and shallots. The e-tongue is an analytical instrument, comprising an array of cross-sensitive chemical sensors and multivariate data processing tools. It has been previously and successfully applied in the classification, quantitative analysis and taste quantification of different beverages and foodstuffs. The sensor array comprised 21 potentiometric chemical sensors containing different active sub-stances. Data processing was aimed at sample recognition and classification and was performed mainly by Principal Component Analysis (PCA). The e-tongue was capable of separating onions from shallots as well as detecting heterogeneous samples. Grey shallot was separated from all other tested material, a result which concurs with its botanical nature. The separation of onions and shallots could not be solely attributed to their respective dry matter content but to intrinsic features of their chemical composition, which e-tongue was able to detect. Résumé

L’oignon et l’échalote appartiennent tous les deux à l’espèce Allium cepa mais à des groupes différents, le groupe cepa et le le groupe aggregatum respectivement, tandis que l’échalote grise appartient à l’espèce A. oschaninii. Les oignons et les échalotes ont un goût différent, mais ils contiennent tous des composés soufrés organiques, ce qui rend l’analyse sensorielle difficile. Il y a un besoin réel de méthodes de distinction entre oignons et échalotes. Le but de cette étude est d’utiliser la langue électronique pour le classement de plusieurs variétés d’oignon et d’échalote. La langue électronique est un appareil d’analyse qui comprend une série de capteurs chimiques à sensibilité croisée et d’instruments d’analyse de données multivariables. Les mesures sont faites en solutions homogénéisées à l’aide de 21 capteurs chimiques potentiométriques et les données sont traitées sous forme d’analyses en composantes principales. Les résultats obtenus montrent que la langue électronique est capable de distinguer les oignons des échalotes ainsi que des variétés hybrides hétérogènes. Cette distinction n’est pas principalement due à la teneur respective en matière sèche des différentes variétés; elle résulte plutôt de caractéristiques intrinsèques de la compo-

Proc. XXVI IHC – IVth Int. Symp. Taxonomy of Cultivated Plants Ed. C.G. Davidson and P. Trehane Acta Hort. 634, ISHS 2004 Publication supported by Can. Int. Dev. Agency (CIDA)

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sition chimique de chacune des variétés. INTRODUCTION

Allium, which belongs to the Liliaceae family, is a very large genus in which six subgenera and 43 sections have been recognised (Hanelt et al., 1992; Klaas, 1998). Included within the genus Allium are around 700 different species and some, such as garlic, onion, shallot, leek and chive are edible and used as vegetables or condiments throughout the world.

As shown by morphology, floral biology, isozymes and molecular markers (Jones and Mann, 1963; Messiaen et al., 1993; Maass, 1996; Cohat and Le Nard, 1998; Friesen and Klaas, 1998), onion and Jersey (or pink) shallot belong to the same species (Allium cepa L.), but are from different groups: A. cepa group cepa and A. cepa group aggregatum whereas grey shallot belongs to another species, Allium oschaninii O. Fedtsch. It is a specific type of shallot grown in Southern and Eastern France and differs from Jersey shallot especially in its coriaceous external “shell”.

Onions are seed-propagated and sown, whereas shallots are vegetatively multi-plied and bulbs planted. Shallots are more expensive than onions due to differences in the method of production. This has given rise to cases where onions, or mixtures of onion type bulbs and shallot type bulbs, have been marketed and sold as shallots.

Shallot dry matter content is commonly higher than onion one. Shallots have a specific taste and flavour and represent one of the condiments typical of French cuisine. So onions and shallots do differ in taste, but they all contain sulphur organic compounds making sensory evaluation difficult (Block et al., 1992). Therefore, a rapid analytical technique for the discrimination of onions and shallots would be of practical interest and importance to growers and those involved in post harvest applications.

One of the more promising analytical methods for this purpose is the electronic tongue. The electronic tongue is an analytical instrument which has an array of low-selective chemical sensors with partial sensitivity to different solution components, along with an appropriate pattern recognition instrument, capable of recognising the quantitative and qualitative composition of simple and complex solutions (Legin et al., 2002a). In an earlier work, the electronic tongue was successfully applied in the recognition and classification of different foodstuffs, including non-liquid food, such as meat and fruits (Legin et al., 2002b).

The aim of this study was to demonstrate the applicability of the electronic tongue in the discrimination of onion and shallot cultivars. MATERIALS AND METHODS

Three series of experiments were performed. Each set of samples included cultivars of onion and shallot: 8 in the first experiment, 20 in the second and 14 in the third one. Also two hybrid cultivars of seed-propagated shallot were included in the experiment. It is important to note that these two hybrid cultivars are heterogeneous, that is, some bulbs look like shallot, some resemble onion and some are intermediate between onion and shallot. All samples were supplied under a code number (Table 1) and their names and dry matter content were not disclosed before experiments.

No data on the use of the electronic tongue on Allium sp. are available. Therefore, in order to determine the best methods of sample preparation, homogenate concentration, as well as optimising the potentiometric chemical sensors in the electronic tongue, a first preliminary series of experiments was carried out. The second series gathered a wide range of cultivars whereas the third one mostly concerned the possible influence of dry matter content.

The bulbs were stored at 4°C throughout the experiment. Since the electronic tongue works in liquid media some sample preparation before measurements was necessary. A pulp (from 5 to 15 g of bulb) was prepared by vigorously grinding each sample (a piece of a bulb) with a plastic, metal-free grinder. The pulp was immediately placed into 50 ml of distilled water and stirred intensively for a few minutes.

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Measurements with the electronic tongue were performed. The sensor array was immediately dipped into the homogenate and, after three minutes stirring, sensor readings were recorded. At least two replicas of each sample were run. All samples were measured in random order.

In the course of the experiments, it was found that a quantity of 15 g of onion in 50 ml of water was often necessary to obtain a satisfactory level of reproducibility. Therefore, the concentration of homogenate was increased up to 15 g in 50 ml of water, which was favourable for stability of sensor readings and final classification. The whole procedure was aimed at performing sample preparation and analysis as quickly as possible, so as to avoid sample oxidation and any other deleterious chemical changes.

The sensor array was comprised of 21 potentiometric chemical sensors. Sensors used were constructed with either, a chalcogenide glass and crystalline membranes, or polymer (PVC) plasticised membranes containing different active substances. A pH glass electrode was also included in the sensor array. Details of sensors’ composition and preparation can be found elsewhere (Legin et al., 2002a). Potentiometric measurements taken with the sensor array were made using a high-input impedance multichannel voltmeter versus conventional Ag/AgCl reference electrode.

During data processing both the whole array and various optimised sub-arrays were considered. Data processing was aimed at sample recognition. The main method was Principal Component Analysis (PCA). The Soft Independent Modelling of Class Analogy (SIMCA) method was also used for numeric classification. The Unscramble from CAMO ASA (Norway) was used as the software engine. If not otherwise stated the results of recognition described below are given for 0.95 confidence level. RESULTS AND DISCUSSION First Series

The first experiment was aimed at evaluation of the possibility of applying the electronic tongue for measurements in homogenates made from onions or shallots. It was found that the samples must be chopped and the measurements can be made in the homogenate. Different samples of onion and shallot were discriminated by the electronic tongue. Second Series

The results of recognition of the samples using the electronic tongue are shown in the form of PCA score plot (Fig. 1 and 2).

Fig. 1 demonstrates an electronic tongue classification of the 20 samples of cultivars of onion and shallot used. Some samples were well separated from all the other ones (L, G, T, B). They corresponded, respectively, to Griselle (the grey shallot) and to the three onion cultivars of the experiment. Some more samples were clearly separated from each other, for example, A from D and E, C from H, Q and R. It is interesting to note that shallots, onions and grey shallots were separated on PCA plots into three independent areas, no onion cultivar were located amongst the shallots and vice versa. Grey shallot individuality was clearly demonstrated, which is in full accordance with its different botanical nature. However, some shallot samples could not be distinguished. This was probably due to an insufficient reproducibility of the sensor system output. Thus, the concentration of homogenate was increased from 5 to 15 g per 50 ml of water to improve reproducibility and subsequently the ability of the electronic tongue to discriminate between onions and shallots.

Fig. 2 deals with subsets of samples obtained with an increased concentration of homogenate. Significant improvements were seen in sample discrimination. It was possible to distinguish more samples in Fig. 2. The samples C, D, K and P do not coincide any more and also were separated, but they are still rather close to each other.

It must be pointed out that the first two principal components in the Fig. 1 and 2 stood for 50 (32 and 18)% and 59 (31 and 28)% of the total variance, respectively. This

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means that the output produced by the sensor array of the electronic tongue is multi-dimensional and contains several meaningful components, which can not be attributed to noise. The multidimensionality of electronic tongue output was due to the sensitivity and cross-sensitivity of the sensors in the array and to different inorganic and organic compounds (and their combinations) present in onions and shallots. Therefore, the 2D PCA plots shown in this paper should be treated as a reasonable visual approximation of the real distribution of the classes of onions and shallots in the multidimensional space of the sensor outputs. The difference observed in the topology was mainly due to the number of samples considered on each picture.

Beside PCA recognition a numeric classification of onions and shallots was performed. Classification was done using the SIMCA method (Fig. 3). Two main classes were modelled: onions and shallots. Grey shallot Griselle and hybrid samples Ambition and Matador were not considered for calibration. Two parameters are used in SIMCA to define class boundaries: sample to model distance and leverage. Classification was performed to a level of probability of 0.95. Shallot class boundaries are denoted with a dashed line in Fig. 3. The samples of Ambition and Matador were significantly heterogeneous: some of them belong to the shallot class (H), some of them are close to the class border (S) and some of them are onions rather than shallots (Q, R).

In general, the numeric classification confirms the results of the PCA classifica-tion, giving new proof of a substantial difference between onions and shallots as detected by electronic tongue. Third Series

The results of the third set of samples are shown in Fig. 4 and 5 in the form of a PCA score plot. The first two principal components PC1 and PC2 described 38 and 26% of the total variance, respectively. The next component, PC3 (12%), did not add significantly to the recognition picture topology. The other components were noisy and less meaningful. Samples in Fig. 4 can be divided into several groups. Sample V – onion with a very high dry matter content, was clearly separate from all the other samples. In the next group, shallot samples A, C and U were relatively close; A and U being practically the same and sample C being easily distinguishable. Furthermore, the next group, B, G and W, was formed by common onions with a low dry matter content. Here, samples B and G were again close, whereas sample W, although close was clearly separate. All the Ambition and Matador samples (H, Q, S, R, X, Y) formed “a cloud” and displayed poorer reproducibility in comparison with both onions and shallots.

Only onion and shallot samples are shown in Fig. 5. Here, all the onions were easily distinguished from all the shallots. Onions were distinguished from shallots even when the dry matter content was close (as with samples U and V). The vector of onion/shallot separation did not coincide with PC1 and PC2 and should be plotted from the upper left-hand corner to the lower right-hand corner of the figure.

For onions, there is a clear correlation of picture topology with dry matter content. The direction vector of dry matter content roughly coincides with the first PC. It is hard to make a final conclusion about the influence of dry matter content in shallots, the amounts are rather too close for different samples. However, sample U with the highest dry matter content is lying to the left of samples I, C and A. The onion sample with the highest content of dry matter, V, is lying far from the three other onion samples with lower dry matter contents.

The separation of onions and shallots obtained with the help of the electronic tongue is not solely related to dry matter content alone, but to intrinsic features of their chemical composition, which e-tongue is able to detect.

So, electronic tongue proved to be a rapid and simple to use instrument for onion and shallot classification. It appears as a very effective taxonomic tool for plant material, when in solution. Results obtained concurs with biological data. It can reliably distinguish shallots from onions and from heterogeneous hybrid cultivars. Data obtained with electronic tongue bring a new proof of the heterogeneous status of these hybrid cultivars.

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The electronic tongue can be used to detect heterogeneous material. So, it can be applied for product quality assessment and would be very effective in detecting cases when onions or heterogeneous material are sold in the market as shallots. ACKNOWLEDGEMENTS

The authors wish to acknowledge Dr. Patrick Mielle from INRA-UMRA (Laboratoire de Recherches sur les Arômes, Dijon, France) for inspiration and kind help in organization of the present work. Literature Cited Block, E., Putman, D. and Zhao, S.H. 1992. GC-MS analysis of thiosulfinates and related

compounds from onion, leek, scallion, chive and chinese chive. J. Agric. Food Chem. 40:2431-2438.

Cohat, J. and Le Nard, M. 1998. L’échalote en France: variétés et amélioration génétique. P.H.M. 392:53-56.

Friesen, N. and Klaas, M. 1998. Origin of some minor vegetatively propagated Allium crops studied with RAPD and GISH. Gen. Res. Crop Evol. 45:511-523.

Hanelt, P., Schulze-Motel, J., Fritsch, R., Kruse, J., Maass, H.I., Ohle, H. and Pistrick, K. 1992. Infrageneric group of Allium - the Gatersleben approach. p.107-123. In: P. Hanelt, K. Hammer and H. Knupffer (eds.), The genus Allium - Taxonomic Problems and Resources, IPK Gatersleben.

Jones, H. and Mann, L. 1963. Onions and their allies. Interscience Publ., New York. Klaas, M. 1998. Applications and impact of molecular markers on evolutionary and

diversity studies in the genus Allium. Plant Breed. 117:297-308. Legin, A.V., Rudnitskaya, A.M. and Vlasov, Y.G. 2002a. Electronic tongues: sensors,

systems, applications. p.143-188. In: G.K. Fedder and J.G. Korvink (eds.), Sensor Update 10, Wiley-VCH Verlag GmbH, Weinheim.

Legin, A.V., Rudnitskaya, A.M. and Vlasov, Y.G. 2002b. Recognition of liquid and flesh food using an “electronic tongue”. Intl. J. Food Sci. Technol. 37:375-385.

Maass, H. 1996. About the origin of the French Grey shallot. Gen. Res. Crop Evol. 43:291-292.

Messiaen, C.M., Cohat, J., Leroux, J.-P., Pichon, M. and Beyries A. 1993. Les Allium alimentaires reproduits par voie végétative. INRA Editions, Paris.

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Tables Table 1. List of grey shallot (▲), pink shallot (■ ), onion (!) and hybrid (◆ ) samples

used in the experiments. Codes Cultivar Plant and bulb shape Dry matter contents ■ A Jermor long shallot 20.2%, 17.0% ! B Rosé de Roscoff onion 11.0% ■ C Mikor half-long shallot 17.1%, 16.0% ■ D Delvad half-long shallot 18.8% ■ E Gouelor long shallot 18.2% ■ F Bretor long shallot 19.6% ! G Jaune des Cévennes onion 10.5% ◆ H Ambition hybrid (shallot type) ■ I Longor long shallot 19.3%, 17.0% ■ J Pikant half-long shallot 22.1% ■ K Golden Gourmet half-long shallot 16.7% ▲ L Griselle grey shallot 27.9% ■ M Arvro half-long shallot 16.7% ■ N Ploumor long shallot 19.0% ■ O Santé half-long shallot 15.1% ■ P Trégor long shallot 17.6% ◆ Q Matador hybrid (shallot type) ◆ R Matador hybrid (onion type) ◆ S Ambition hybrid (onion type) ◆ T Echalion onion-échalion 12.0% ■ U Vigarmor long shallot 20.0% ! V Oignon blanc onion 21.0% ! W Oignon jaune onion 9.0% ◆ X Ambition hybrid (intermediate) ◆ Y Matador hybrid (intermediate)

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Fig. 2. Recognition of a subset of samples using increased concentration of homogenate.

Fig. 3. SIMCA classification (p=0.95) of onions and shallots. All pink shallots are

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Fig. 4. PCA score plot of all samples from the third series.

Fig. 5. Recognition of onions and shallots only, taken from the third series. All onions (B, G, W and V) are clearly distinguished from all shallots (A, U, I and C).

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