A hybrid electronic tongue

Winquist, Fredrik; Holmin, Susanne; Krantz‐Rülcker, Christina; Wide, Peter; Lundström, Ingemar

doi:10.1016/s0003-2670(99)00767-9

Cited by 199 publications

(89 citation statements)

References 14 publications

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“…All PCA were performed with the statistical package SPSS (Statistical Programme for Social Scientist) 9.0 for Windows. PCA is a mathematical transform, which can be to explain variation in experimental data [5]. PCA reduces the immense data set to plots, which can be easily used to classify or group the observation.…”

Section: Discussionmentioning

confidence: 99%

Monitoring of Milk Quality With Disposable Taste Sensor

Sim

Shya

Ahmad

et al. 2003

Sensors

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A disposable screen-printed multi channel taste sensor composed of several types of lipid as transducers and a computer as data analyzer could detect taste in a manner similar to human gustatory sensation. The disposable taste sensor was used to measure the electrical potential resulted from the interaction between lipid membranes and taste substances. In the present study, two types of packaged commercial milk, the ultra high temperature (UHT) and the pasteurized milk were tested. It was found that the disposable taste sensor is capable to discriminate reliably between fresh and spoiled milk and to follow the deterioration of the milk quality when it is stored at room temperature based on a pattern recognition principle namely Principle Component Analysis (PCA). This research could provide a new monitoring method ideally for simple and cheap decentralized testing for controlling the quality of milk, which may be of great use in the dairy industries.

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Section: Discussionmentioning

confidence: 99%

Monitoring of Milk Quality With Disposable Taste Sensor

Sim

Shya

Ahmad

et al. 2003

Sensors

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“…Electronic tongues (e-tongue) are a combination of sensors with complementary selectivity coupled to a pattern recognition software [23]. E-tongues based on different types of sensors have been used to analyze the quality of milks [24], to monitor changes in quality during storage [25,26], to classify fermented milks [27], or to detect adulterations [28] or off-flavors in the raw milk [29]. Usually etongue systems provide global information about the samples and are used for classification purposes.…”

Section: Introductionmentioning

confidence: 99%

Multisensor System Based on Layer by Layer Films. Discrimination of Milks

Salvo-Comino¹,

García-Hernández²,

Garcı́a³

et al. 2018

Preprint

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Abstract:A nanostructured electrochemical bi-sensor system for analysis of milks has been developed using the Layer by Layer technique. The non-enzymatic sensor [CHI+IL/CuPc S ]2, is a layered material containing a negative film of the anionic sulfonated copper phthalocyanine (CuPc S ) acting as electrocatalytic material, and a cationic layer containing a mixture of an ionic liquid (IL) (1-butyl-3-methylimidazolium tetrafluoroborate) that enhances the conductivity and chitosan (CHI) that facilitates the enzyme immobilization. The biosensor ([CHI+IL/CuPc S ]2-GAO) results from the immobilization of galactose oxidase on the top of the LbL layers. FTIR, UV-vis and AFM have confirmed the proposed structure and cyclic voltammetry has demonstrated the amplification caused by the combination of materials in the film. Sensors have been combined to form an electronic tongue for milk analysis. Principal Component Analysis has revealed the ability of the sensor system to discriminate between milk samples with different lactose content. Using PLS-1 calibration models, correlations have been found between the voltammetric signals and chemical parameters measured by classical methods. PLS-1 models provide excellent correlations with lactose content. Additional information about other components such as fats, proteins and acidity can also be obtained. The method developed is simple and the short response time permits its use in assaying milk samples on-line.

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“…Winquist's team (Winquist et al 1997) were pioneers in the development of voltammetric electronic tongues, and the system had also been used to evaluate several food samples (Ivarsson et al 2001;Winquist et al 2005) under the combination of PCA. Also, with the data analysis methods of PCA and ANN, a hybrid taste sensor based on voltammetric metallic electrodes and potentiometric ion-selective electrodes was employed to classify six types of fermented milk (Winquist et al 2000). In general, the electronic tongue has been widely used year by year, and it has played a significant role in many aspects (del Valle 2012; Escuder-Gilabert and Peris 2010; Laura EscuderGilabert a 2010; Riul et al 2010).…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, considering that data analysis is a fundamental part of the electronic tongue system, as we have mentioned above, several pattern recognition techniques such as PCA, LDA, SIMCA, ANN (Lvova et al 2003;Scampicchio et al 2006;Vlasov et al 1997;Winquist et al 2000) have been explored for the data analysis of the sensors. In the last two decades, a classification algorithm called support vector machine (SVM) (Burges 1998;Shawe-Taylor and Cristianini 2000), which is on the basis of statistical learning theory (Smola and Schölkopf 1998;Vapnik 1998), has been proposed in the broad learning field (Brereton and Lloyd 2010;.…”

Section: Introductionmentioning

confidence: 99%

Classification of Green and Black Teas by PCA and SVM Analysis of Cyclic Voltammetric Signals from Metallic Oxide-Modified Electrode

et al. 2013

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Forty-three samples of green and black teas were analyzed by an electronic tongue technique. A class of metallic oxide-modified nickel foam electrodes (SnO 2 , ZnO, TiO 2 , Bi 2 O 3 ) was compared in their sensitivity in this system. The signals obtained by cyclic voltammetry were submitted to multivariate data analysis. In the explorative analysis based on principal component analysis (PCA), the score plots showed that two of these sensors were able to distinguish varieties of teas. The resulting PCA scores were modeled with a support vector machine (SVM) that accomplished final prediction with the qualitative classification of teas. The optimal SVM model was achieved after grid search optimization of some parameters and the conduction of the three commonly used kernel functions. With a comparison of classification accuracies, Bi 2 O 3 -modified nickel foam electrode performed the best among the four electrodes and SVM model using the polynomial kernel attained the highest within the three used kernels. This work demonstrated that cyclic voltammetry combined with the SVM pattern recognition method could be successfully applied in the classification of green and black teas.

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A hybrid electronic tongue

Cited by 199 publications

References 14 publications

Monitoring of Milk Quality With Disposable Taste Sensor

Monitoring of Milk Quality With Disposable Taste Sensor

Multisensor System Based on Layer by Layer Films. Discrimination of Milks

Classification of Green and Black Teas by PCA and SVM Analysis of Cyclic Voltammetric Signals from Metallic Oxide-Modified Electrode

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