外文摘要：Pixian broad bean paste (PBP) is a popular fermentation condiment known in home and abroad. Aroma is a significant index for evaluating PBP quality during fermentation process. Hence, in this study, electronic nose (E-nose) system combined machine learning methods were applied for PBP quality characterization. The machine learning methods including partial least squares discriminant analysis (PLS-DA), partial least squares regression (PLSR), support vector machine (SVM), random forest (RF), and artificial neural networks (ANN) were introduced for qualitatively discriminating fermentation time and quantitatively analyzing the contents of key aromas of PBP samples. The PLS-DA result indicated that it is feasible to identify the fermentation stages of PBP samples by E-nose and a classification accuracy of 99% could be achieved. As for the quantitative prediction modelling, ANN exhibited preferable performance than PLSR, SVM and RF for analyzing the contents of phenethyl alcohol (R2 = 0.846, RMSE = 10.270), isoamyl alcohol (R2 = 0.940, RMSE = 6.857), 3-methylthiopropanol (R2 = 0.910, RMSE = 2.205), benzaldehyde (R2 = 0.824, RMSE = 4.172), furfural (R2 = 0.902, RMSE = 2.066), 4-ethyl guaiacol (R2 = 0.877, RMSE = 11.249) and 4-ethylphenol (R2 = 0.913, RMSE = 12.754).
外文关键词：machine learning;FERMENTATION;Electronic nose;Pixian broad bean paste;Aroma
作者：Xu, Min;Wang, Xingbin;Xu, Zedong;Wang, Yao;Jia, Pengfei;Ding, Wenwu;Dong, Shirong;Liu, Ping
作者单位：Guangxi Univ;Xihua Univ;Sichuan Fansaoguang Food Grp Co Ltd;Chongqing Key Lab Special Food Cobuilt Sichuan & C
期刊名称：JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION
期刊影响因子：0.0
出版年份：2024
出版刊次：18（5）
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台