Zhenbo Wei

Determination of the flavours and marked ages of rice wines using a taste sensing system combined with the Weber–Fechner law and chemometric methods

A taste sensing system was used to determine the marked ages and flavours of rice wines. The taste sensing system consisted of five different artificial lipid–polymer membrane electrodes that were highly sensitive to the five basic tastes (umami, astringency, bitterness, sourness and saltiness). This taste sensing system can be used to show the quality and intensity of tastes in samples and detects these tastes in a manner similar to that of the human gustatory system. Three types of rice wine with the same flavour, but different marked ages, and three types of rice wine with same marked age, but different flavours were analysed. A “taste map” analysis was performed to determine taste intensity based on which the tastes could be quantitatively analysed without chemometric methods. The differences in flavour among the rice wines were clearly shown. The responses (including the change in membrane potential caused by adsorption) obtained by the electrodes were analysed using principal components analysis and discriminant function analysis for classification and partial least-squares regression and a support vector machine for forecasting. Discriminant function analysis performed better than principal components analysis in classifying the rice wines with different marked ages and had similar results to principal components analysis in classifying rice wines with different flavours. The support vector machine based on the leave-one-out cross-validation was more stable than partial least-squares regression and the support vector machine based on the ten-fold cross-validation in predicting the marked ages and flavours of different types of rice wine; the prediction correlations were R2 = 0.9568 and R2 = 9620, respectively.

Nickel and copper foam electrodes modified with graphene or carbon nanotubes for electrochemical identification of Chinese rice wines

AbstractThe authors describe the application of two types of metallic foams modified with either graphene (GR) or carbon nanotubes (CNTs) as voltammetric electrodes in order to discriminate rice wines of different age and brand. Two types of bare metallic foams (bare Ni and Cu foam electrodes) were combined with GR or CNTs to give four types of modified metallic foams, referred to as GR/Ni, GR/Cu, CNT/Ni, and CNT/Cu foam electrodes. Cyclic voltammetry was applied to study the effects of GR and CNTs on the response of the electrodes. Multifrequency rectangle pulse voltammetry and multifrequency staircase pulse voltammetry were applied to generate potential waveforms, and chronoamperometric curves were recorded. Principal component analysis (PCA) allowed a classification of the rice wines, and characteristic regular distributions were identified in the PCA plots. Support vector machines (SVM) were found to perform better than partial least squares regression in predicting ages and brands of the rice wines in that all fit correlation coefficients were >0.9930. The SVM based leave-one-out cross-validation method proved to be the most powerful regression tool. The six types of foam electrodes perform very well in the classification and prediction of rice wines of different ages and brands. Graphical abstractModified Ni and Cu foams were modified by grapheme and carbon nanotubes respectively. Those modified electrodes worked well to classify and predict rice wines of different ages and brands with the help of multi-frequency potential waveforms and pattern recognition methods.

Application of novel nanocomposite-modified electrodes for identifying rice wines of different brands

In this paper, poly(acid chrome blue K) (PACBK)/AuNP/glassy carbon electrode (GCE), polysulfanilic acid (PABSA)/AuNP/GCE and polyglutamic acid (PGA)/CuNP/GCE were self-fabricated for the identification of rice wines of different brands. The physical and chemical characterization of the modified electrodes were obtained using scanning electron microscopy and cyclic voltammetry, respectively. The rice wine samples were detected by the modified electrodes based on multi-frequency large amplitude pulse voltammetry. Chronoamperometry was applied to record the response values, and the feature data correlating with wine brands were extracted from the original responses using the ‘area method’. Principal component analysis, locality preserving projections and linear discriminant analysis were applied for the classification of different wines, and all three methods presented similarly good results. Extreme learning machine (ELM), the library for support vector machines (LIB-SVM) and the backpropagation neural network (BPNN) were applied for predicting wine brands, and BPNN worked best for prediction based on the testing dataset (R2 = 0.9737 and MSE = 0.2673). The fabricated modified electrodes can therefore be applied to identify rice wines of different brands with pattern recognition methods, and the application also showed potential for the detection aspects of food quality analysis.