Counterfeit detection of bulk Baijiu based on fluorescence hyperspectral technology and machine learning

Baijiu is a unique distilled spirit in China. The bulk Baijiu market has been experiencing issues related to counterfeit and substandard products, raising concerns about food safety. Detecting liquor adulteration is crucial for eliminating fraud in the bulk Baijiu market. In this study, we proposed using fluorescence hyperspectral Technology (FH) combined with machine learning (ML) to detect Baijiu adulteration quickly and non-destructive. Due to the similarity of fluorescence spectral features between adulterated Baijiu and real Baijiu, it was difficult to distinguish them based on the fluorescence feature parameters alone. The data preprocessing methods were used and then principal component analysis (PCA) was adapted. The principal components were used as inputs to ML models to establish the qualitative and quantitative detection models. In the qualitative detection models, the Adaptive Boosting (AdaBoost) model demonstrated the best performance with 98.08