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In this work, the application of an electronic tongue (ET) based on a specific ion-selective sensor array for discrimination of different white wine types is presented. The electronic tongue equipped with specific sensor array containing seven IFSET sensors was used to analyze wine samples. The obtained ET responses were evaluated using different pattern recognition methods. Principal component analysis (PCA) provides the possibility to identify some initial patterns. Linear discriminant analysis (LDA) was used to build models to separate white wine samples based on wine regions and grape cultivars. The results showed that every group was distinguished from each other with no misclassification error. Furthermore, the sequence of the wine sample groups was similar to the increasing total acidity content. Partial least square (PLS) regression was used to build models for the prediction of the main chemical compositions of the wine samples based on the electronic tongue results. The closest correlation (R2=0.93) was found in case of ‘total acidity’, and the prediction error (RMSEP) was 6.9%. The pH of the wine samples was predicted with good correlation (R2=0.89) but higher prediction error (RMSEP=10.71%) from the electronic tongue results. The ET combining these statistical methods can be applied to determine the origin and variety of the wine samples in easy and quick way.

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Acta Alimentaria
Authors: E. Várvölgyi, T. Werum, L. Dénes, J. Soós, G. Szabó, J. Felföldi, G. Esper, and Z. Kovács

Time consuming and expensive methods have been applied for detection of coffee adulteration based on the literature. In the present work, an optical method (vision system) and the application of an electronic tongue is proposed to reveal the addition of barley in different proportion to coffee in ground and brewed forms. In a range of 1 to 80% (w/w) Robusta coffee was blended with roasted barley. Principal Component Analysis (PCA) accomplished on vision system image data showed a good discrimination of the adulterated samples. The results of Polar Qualification System (PQS) data reduction method revealed even small differences in the right barley content order by point method approach. With Partial Least Squares (PLS) regression the amount of barley in Robusta was predicted with high R2 (0.996) and relatively low RMSEP (∼2%) values in case of vision system data processing. Considering electronic tongue measurements, PCA results showed a good discrimination of samples with higher barley concentration. Misclassification was found in the low concentrated area by Lienar Discriminant Analgsis (LDA). To obtain an accurate model for barley content prediction in coffee, the most sensitive sensor signals were used to apply PLS regression successfully (R2=0.97, RMSEP=3.99% (w/w)).

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Acta Alimentaria
Authors: J. Soós, Sz. Kozits, Z. Kovács, E. Várvölgyi, D. SzöllőSi, and A. Fekete

Nowadays quality measurement is an important topic in food quality control. The electronic tongue (ET) can be a useful tool in this feld. The objective of our work is to demonstrate the application potential of ET for the evaluation of different coffee, wine and carrot juice samples and to compare the results with sensory attributes. ET was able to distinguish the different coffee samples. The Arabica concentration of the samples were predicted with close correlation (R2=0.98) and low error (RMSEP=3.16). The Arabica content of commercial samples were also determined. The ET measurement results of different wine samples showed a tendency similar to the increasing ‘acidic content’ determined by sensory evaluation. The closest correlation between ET and sensory evaluation was found with the ‘acidic taste’ (R2=0.87) and the lowest prediction error was observed with the prediction of ’fruit taste’ (RMSEP=6.11). Carrot juice samples were also distinguished by ET. Sensor SRS, developed for sour taste, gave the highest correlation (r=−0.99) with the sour taste of the carrot juice samples. The conclusion is that ET is a useful instrument in the feld of food quality control when appropriate statistical methods are applied.

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Differential Display RT-PCR was developed before the genomic era to serve as a tool in hunting for genes. Nowadays, applications using state-of-the-art techniques to obtain more information about the whole transcriptome or the genome have rapidly overtaken DD-RT-PCR. This paper will discuss a few of the major drawbacks and limitations of using this once highly valued method.

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During the ageing in barrels, the contact with the fine lees triggers several processes in wine. Lees has a reductive effect by absorbing dissolved oxygen and reducing the amount, which will remain in the wine. At present, minimizing the addition of sulphur dioxide is the trend in all viticultural areas. In this study, the effect of various sulphur dioxide levels was monitored in presence of the lees to determine which dose is appropriate to provide the protection of susceptible white wine against oxidation.

Without SO2 protection, the rH and redox potential changed slightly, so the level of dissolved oxygen seemed to be controlled during the ageing period by the lees, though the antioxidant effect of lees in itself was not appropriate to protect the polyphenol content from chemical oxidation, which led to considerable browning. With the addition of a lower amount of SO2 — 40 mg l2, the lees is already able to protect the white wine samples in all aspects.

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Efforts have been made to predict the sensory profile of coffee samples by instrumental measurement results. The objective of the work was to evaluate the most important sensory attributes of coffee samples prepared from ground roasted coffee by electronic tongue and by sensory panel. Further aim was to predict the Arabica concentration and the main sensory attributes of the different coffee blends by electronic tongue and to analyze the sensitivity of the electronic tongue to the detection of poor quality coffee samples. Five coffee blends with known Arabica and Robusta concentration ratio, five commercially available coffee blends and a poor quality coffee were analyzed. The electronic tongue distinguished the coffee samples according to the Arabica and Robusta content. The sensory panel was able to discriminate the samples based on global aroma, bitterness and coffee aroma intensity (p < 0.01). The Arabica concentration was predicted from the electronic tongue results by PLS with close correlation and low prediction error. Models were developed to predict sensory attributes of the tested coffee samples from the results obtained by the electronic instrument.

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