Comparison between voltammetric detection methods for abalone-flavoring liquid

Open Life Sciences

Lv Yan¹  Ma Qinyi¹  Tao Xueheng¹  Zhang Xu¹  Zhang Peng¹  Wang Huihui¹ 

[1] Mechanical Engineering Department of Dalian Polytechnic University, Dalian 116034, China;

关键词: abalone-flavoring liquid;    voltammetric detection methods;    principal component analysis;    probabilistic neural network;    support vector machine;   

DOI  :  10.1515/biol-2021-0035

来源: DOAJ

【 摘 要 】

This article attempts to determine the most accurate classification method for different abalone-flavoring liquids. Three common voltammetric detection methods, namely, linear sweep voltammetry (LSV), cyclic voltammetry (CV), and square-wave voltammetry (SWV), were considered. To compare their classification accuracies of abalone-flavoring liquids, three methods were separately adopted to classify five different abalone-flavoring liquids, using a four-electrode (Au, Pt, Pd, and W) sensor array. Then the data acquired by each method were subject to the principal component analysis (PCA): the first three principal components whose eigenvalues were greater than 1 were extracted from each set of data; the cumulative variance contribution rate and the principal component scores of each method were obtained. The PCA results show that the first three principal components obtained by the CV had the highest cumulative variance contribution rate (91.307%), indicating that the CV can more comprehensively characterize the information of abalone-flavoring liquid samples than the other two methods. According to the principal component scores, compared with those of LSV and SWV, the same kind of samples detected by the CV were highly clustered and the different kinds of samples detected by the CV were greatly dispersed. This indicates that the CV can effectively distinguish between the five abalone-flavoring liquids. Finally, the detection data were further verified through probabilistic neural network and a support vector machine algorithm optimized by genetic algorithm. The results further confirm that the CV is more accurate than the other two methods in the classification of abalone-flavoring liquids. Therefore, the CV was recommended for the classification of abalone-flavoring liquids.

【 授权许可】

Unknown