文章基本信息

标题：Rapid qualitative and quantitative detection of formaldehyde in squids based on colorimetric sensor array
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作者：Binbin Guan ; Hongmei Ding ; Bin Chen 等
期刊名称：E3S Web of Conferences
印刷版ISSN：2267-1242
电子版ISSN：2267-1242
出版年度：2021
卷号：233
页码：2021
DOI：10.1051/e3sconf/202123302021
出版社：EDP Sciences
摘要：The colorimetric sensor array was used to detect the volatile organic compounds (VOCs) in squids with different formaldehyde content. In order to distinguish whether the formaldehyde is artificially added in the squids, the linear discriminant analysis (LDA) and K-nearest neighbor (KNN) based on principal component analysis (PCA) were used to make qualitative judgments, the result shows that the recognition rates of the training set and prediction set of the LDA model were 95% and 85% respectively, and the recognition rates of the training set and prediction set of the KNN model were both 90%. Moreover, error back propagation artificial neural network (BP-ANN) was used to quantitatively predict the concentration of formaldehyde in squids. The result indicates that the BP-ANN model acquired a good recognition rate with the correlation coefficient (R p ) for prediction was 0.9887 when the PCs was 10. To verify accuracy and applicability of the model, paired sample t-test was used to verify the difference between the predicted value of formaldehyde in the BP-ANN model and the actual addition amount. Therefore, this approach showed well potentiality to provide a fast, accuracy, no need for a pretreatment, and low-cost technique for detecting the formaldehyde in squids.
其他摘要：The colorimetric sensor array was used to detect the volatile organic compounds (VOCs) in squids with different formaldehyde content. In order to distinguish whether the formaldehyde is artificially added in the squids, the linear discriminant analysis (LDA) and K-nearest neighbor (KNN) based on principal component analysis (PCA) were used to make qualitative judgments, the result shows that the recognition rates of the training set and prediction set of the LDA model were 95% and 85% respectively, and the recognition rates of the training set and prediction set of the KNN model were both 90%. Moreover, error back propagation artificial neural network (BP-ANN) was used to quantitatively predict the concentration of formaldehyde in squids. The result indicates that the BP-ANN model acquired a good recognition rate with the correlation coefficient (R p ) for prediction was 0.9887 when the PCs was 10. To verify accuracy and applicability of the model, paired sample t-test was used to verify the difference between the predicted value of formaldehyde in the BP-ANN model and the actual addition amount. Therefore, this approach showed well potentiality to provide a fast, accuracy, no need for a pretreatment, and low-cost technique for detecting the formaldehyde in squids.