Novel colorimetric sensor array for Chinese rice wine evaluation based on color reactions of flavor compounds

Abstract In this study, a 3 × 3 comprehensive colorimetric sensor array (CSA) was constructed for the quality evaluation of Chinese rice wine based on three different action principles (pH change principle, indicators displacement assay principle, and ethanol fading principle). First, China national standard was used to determine the total acid, reducing sugar, and alcoholic strength of six kinds of rice wine, which forms the basis for the selection of the CSA sensitive points. Then, CSA was combined with pattern recognition technologies to qualitatively distinguish rice wine. The results showed that the when PCs = 3, the linear discriminant analysis model can achieve a recognition rate of 100%, which shows strong prediction ability compared with K ‐nearest neighbor and back‐propagation artificial neural network models. Finally, CSA was combined with partial least squares (PLS) and support vector regression (SVR) to quantitatively analyze the three components in rice wine. The correlation coefficients of the training set and predictions are more than 85% in the SVR model, which is superior to the PLS model. Based on these findings, it is possible to conclude that the CSA can be used to rapidly evaluate the quality of rice wine. Practical Applications Established the 3 × 3 colorimetric sensor array (CSA) for the quality evaluation of Chinese rice wine based on three different action principles (pH change principle, indicators displacement assay principle, and ethanol fading principle). The CSA was combined with pattern recognition technologies to qualitatively distinguish different kinds of rice wine. The performance of linear discriminant analysis shows strong prediction ability compared with K ‐nearest neighbor and back‐propagation artificial neural network. The CSA was combined with partial least squares (PLS) and support vector regression (SVR) to quantitatively analyze the three components in rice wine. The correlation coefficients of the training set and predictions are more than 85% in the SVR model, which is superior to the PLS model.