Collaborative Analysis on the Marked Ages of Rice Wines by Electronic Tongue and Nose based on Different Feature Data Sets

Abstract

Aroma and taste are the most important attributes of alcoholic beverages. In the study, the self-developed electronic tongue (e-tongue) and electronic nose (e-nose) were used for evaluating the marked ages of rice wines. Six types of feature data sets (e-tongue data set, e-nose data set, direct-fusion data set, weighted-fusion data set, optimized direct-fusion data set, and optimized weighted-fusion data set) were used for identifying rice wines with different wine ages. Pearson coefficient analysis and variance inflation factor (VIF) analysis were used to optimize the fusion matrixes by removing the multicollinear information. Two types of discrimination methods (principal component analysis (PCA) and locality preserving projections (LPP)) were used for classifying rice wines, and LPP performed better than PCA in the discrimination work. The best result was obtained by LPP based on the weighted-fusion data set, and all the samples could be classified clearly in the LPP plot. Therefore, the weighted-fusion data were used as independent variables of partial least squares regression, extreme learning machine, and support vector machines (LIBSVM) for evaluating wine ages, respectively. All the methods performed well with good prediction results, and LIBSVM presented the best correlation coefficient (R2 ≥ 0.9998).

Rapid Non-Destructive Quantification of Eugenol in Curdlan Biofilms by Electronic Nose Combined with Gas Chromatography-Mass Spectrometry

“…Pattern recognition can provide direct and easily understood qualitative and semi-quantitative data. PCA is a projection approach used for reducing the dimensionality of data, calculating a number of variables that best describe the differences between the samples and are arranged according to the contribution rates (called principal components (PCs)) [ 32 , 37 ]. In order to determine the effectiveness of the E-nose technique to detect and differentiate biofilms of different eugenol concentrations, PCA was used to process the data of 10 different sensors in order to reduce the complexity of the data.…”

Electrochemical Sensors Coupled with Multivariate Statistical Analysis as Screening Tools for Wine Authentication Issues: A Review

“…Some studies indicate that SWV has some advantages versus CV such as the lower time per analysis and simplicity of the data treatment by providing single curves instead of bi-valuated curves [53]. Generally, in practical applications, multiparametric systems known as hybrid ETs consisting of an array of modified electrochemical sensors are preferred for wine discrimination [39,57,58]. Often, the electrochemical responses of ETs is associated with electronic nose (EN) and an e-eye (based on CIE Lab coordinates-CIE is the Commission Internationale de l'Eclairage, L is the luminance, a is the red-green axis, and b is the blue-yellow axis.)…”

“…The specificity toward phenols can be assured by enzymes such as tyrosinase or laccase, while glucose oxidase or fructose dehydrogenase are specific for sugars [47]. The electroactive modifiers can be immobilized in graphite-epoxy electrodes [39], carbon paste electrodes (CPEs) [57,62], glassy carbon (GC) [58,65], thin films [64], or screen-printed carbon electrodes (SPCE) [47,51].…”

Geographical origin determination of Red Huajiao in China using the electronic nose combined with ensemble recognition algorithm

“…Electronic nose, based on the gas sensor array and ensemble recognition algorithm, was a novel ensemble sensory technology during the recent years. Based on the advantages of rapid and nondestructive detection, the Electronic nose had been widely used in many kinds of detection application areas, including wine analysis (H. H. Zhang et al, 2020), tea classification (Gao et al, 2019;B. H. Yang et al, 2020), agricultural products quality detection (Cui et al, 2019), and air quality evaluation (Arroyo et al, 2020).…”

“…Electronic nose, based on the gas sensor array and ensemble recognition algorithm, was a novel ensemble sensory technology during the recent years. Based on the advantages of rapid and nondestructive detection, the Electronic nose had been widely used in many kinds of detection application areas, including wine analysis (H. H. Zhang et al., 2020), tea classification (Gao et al., 2019; B. H. Yang et al., 2020), agricultural products quality detection (Cui et al., 2019), and air quality evaluation (Arroyo et al., 2020). Combined with the principal component analysis and discriminant factor analysis method, our lab also had established a detection method to evaluate the freshness of pork, beef, and mutton by using the self‐established electronic nose system and obtained well prediction results (Chen et al., 2019).…”