Aroma quality characterization for Pixian broad bean paste fermentation by electronic nose combined with machine learning methods

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