Abstract: The chemical composition of base baijiu is complex and diverse. A classification model for base baijiu of different sensory grades was established based on the gas chromatography-mass spectrometric (GC-MS) data for their volatile composition. In order to improve the accuracy and generalization capacity of the classification model, a method combining genetic algorithm (GA) and bootstrap aggregating (Bagging) was proposed to optimize the support vector machine (SVM) classifier. Using Spearman’s correlation analysis, 36 key substances were selected, and 12 kernel principal components were extracted as input to the model by kernel principal component analysis, which together accounted for 96.06% of the total variance. The radial basis kernel function support vector machine with the best performance was selected, and the parallel computing Bagging ensemble algorithm with strong adaptability to data diversity was used to construct a Bagging-SVM classifier for base baijiu classification. Finally, GA was used to optimize the parameters (C, γ, and N) of the Bagging-SVM classifier to construct a GA-Bagging-SVM model. The results showed that the accuracy, precision, recall rate, and F1-Score of the GA-Bagging-SVM model were 96.77%, 96.90%, 96.77%, and 96.78%, respectively, which were 6.45%, 5.61%, 6.45%, and 6.42% higher than those of the SVM model, and 3.22%, 2.29%, 3.22%, and 3.15% higher than those of the Bagging-SVM model, respectively. This method can be used as an optimization method for the quality evaluation model for base baijiu.

Key words: base baijiu; support vector machine; Bootstrap aggregating; genetic algorithm; classification prediction