Evaluation Quality of Chinese Baijiu Using GC–MS Based on SPCA and Neural Network

Abstract

Currently, evaluating the quality of strong-flavor Baijiu (SFB) heavily relies on subjective sensory analysis, resulting in large deviations in evaluation. However, as there are no existing evaluation criteria for SFB quality, this study aimed to extract trace components and design an evaluation model using gas chromatography–mass spectrometry (GC–MS). First, the key component data was analyzed using principal component analysis (PCA) and sparse principal component analysis (SPCA) to identify the most important principal components that represent the SFB samples. Second, KNN, DT, SVM, and BP analyses were then employed on the principal component data to determine the grade of the SFB samples. Finally, a price prediction model based on SPCA+BP was established to objectively evaluate the quality and price of SFB. The experimental results show that the proposed method can effectively realize the distinction and price prediction of SFB.