Application of electronic tongue to beverages

Soós, J.; Kozits, Sz.; Kovács, Zoltán; Várvölgyi, E.; Szöllősi, Dániel; Fekete, Andrea

doi:10.1556/aalim.42.2013.suppl.11

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…These systems have also been expended for multicomponent quantitative analysis of different chemical attributes of interest for the wine industry, such as pH, alcoholic content, organic acids, sulphur dioxide, glycerol, different polyphenolic compounds (LEGIN et al, 2003;DI NATALE et al, 2004). Even the correlation of ET data with sensory descriptors provided by an expert human panel (BURATTI et al, 2007;SOÓS et al, 2013) has been also evaluated. However, until now the effect of origin, grape cultivars and chemical components in Hungarian white wines have not been reported.…”

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confidence: 99%

Application of electronic tongue to discriminate white wines originated from different regions of Hungary

Soós¹,

Várvölgyi²,

Dénes³

et al. 2014

Acta Alimentaria

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In this work, the application of an electronic tongue (ET) based on a specifi c ion-selective sensor array for discrimination of different white wine types is presented. The electronic tongue equipped with specifi c 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 misclassifi cation 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 (R 2 =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 (R 2 =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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confidence: 99%

Application of electronic tongue to discriminate white wines originated from different regions of Hungary

Soós¹,

Várvölgyi²,

Dénes³

et al. 2014

Acta Alimentaria

Self Cite

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“…Considering the authentication and thus especially the analysis of coffee as well as the usage of potentiometric sensors, most of the analysis are accomplished to classify different beverages (LEGIN et al, 1997) or to obtain sensory profi les of coffee samples (TOKO, 1998). The possibility to detect adulteration by electronic tongue was tested on wine (PARRA et al, 2006;STÓJ, 2011) and on cherry tomato juices (HONG et al, 2014) However, a lack of analysis to confi rm authentication of coffee is observed using ISFET sensors although the ability to discriminate coffee blends with different Arabica-Robusta ratios was tested successfully (VÁRVÖLGYI et al, 2012;SOÓS et al, 2013). Hence, the objective of this investigation is to establish an optical method and to extend the application of the electronic tongue for the detection of fraudulent barley in coffee.…”

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Vision system and electronic tongue application to detect coffee adulteration with barley

Várvölgyi¹,

Werum²,

Dénes³

et al. 2014

Acta Alimentaria

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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 Qualifi cation 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 R 2 (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. Misclassifi cation 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 (R 2 =0.97, RMSEP=3.99% (w/w)).

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Application of electronic senses to characterize espresso coffees brewed with different thermal profiles

Buratti

Benedetti

Giovanelli

2016

Eur Food Res Technol

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Application of electronic tongue to beverages

Cited by 4 publications

References 12 publications

Application of electronic tongue to discriminate white wines originated from different regions of Hungary

Application of electronic tongue to discriminate white wines originated from different regions of Hungary

Vision system and electronic tongue application to detect coffee adulteration with barley

Application of electronic senses to characterize espresso coffees brewed with different thermal profiles

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