Amperometric Electronic Tongue for the Evaluation of the Tea Astringency

Abstract

A new multi-flow-through amperometric detectors design was employed as a sensors array system for the detection of flavor related phenols. Relevant parameters of the amperometric detection were examined and optimized. The multivariate analytical signal was processed with chemometric analysis for exploring and classifying the tea beverages. Multivariate regression was used to correlate the astringency value of several tea beverages, obtained by the UNI (Italian Organization for Standardization) sensory profile test, with the signals collected by this amperometric electronic tongue.

Impedance e-tongue instrument for rapid liquid assessment

“…Additionally, there is a user-friendly software interface for instrument control, allowing the statistical correlation of samples using principal component analysis. The artificial tongue concept is based on the application of an array of nonspecific sensors with wide sensitivity toward several media components ͑broad cross sensitivity͒, largely employed in the analysis of tastants, 1 medicines, 2,3 foodstuffs, [4][5][6] and pollutants. 7,8 The impedance-sensing units made from ultrathin films physically interact with analytes in solution according to their electrical nature, providing a fingerprint of the solution similar to the global selectivity concept in the human tongue.…”

Artificial neural networks (ANN): prediction of sensory measurements from instrumental data

“…Instrumental measurements may be preferred by the food industry, particularly due to routine quality control since they usually have a lower cost, can perform analyses faster, and are more easily controlled. They also present high data reproducibility, an important characteristic when the measurements must be performed in different industries (WILKINSON; YUKSEL, 1997;SCAMPICCHIO et al, 2006). However, in order for instrumental measurements to replace sensory attributes, it is essential that they provide accurate predictions.…”

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

“…A disposable all-solid-state potentiometric taste sensor coupled with PCA and Soft Independent Modeling of Class Analogy (SIMCA) was investigated in the identification of Korean green tea (Lvova et al 2003). A combination of the amperometric electronic tongue and linear discriminant analysis (LDA) was utilized to evaluate five classes of tea (Scampicchio et al 2006). In recent years, several varieties of the Longjing tea were discriminated by the electronic tongue based on multifrequency large amplitude pulse voltammetry (Tian et al 2007), and a single batch production of Kangra orthodox black tea was classified successfully by a novel impedance tongue (Bhondekar et al 2011).…”

“…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;.…”