Abstract: Raw Nongxiangxin Baijiu of different grades were collected during the distillation process, and their near infrared spectroscopy (NIR) data and gas chromatography-mass spectrometry (GC-MS) data were acquired. After preprocessing the NIR data through 5-point 2-fold convolutional smoothing, spectral feature wavelengths were selected using the competitive adaptive reweighted sampling (CARS) algorithm; combining Spearman’s rank correlation coefficient, maximum information coefficient (MIC) and random forest (RF) variable importance, the key flavor components (KC) identified by GC-MS affecting the grading of raw Baijiu were determined. Then, extreme gradient boosting tree (XGBoost) was applied to establish three grade identification models for raw Baijui based on NIR, GC-MS and their fused data. The results showed that the prediction accuracy of the model based on the spectral feature variables selected by CARS was 89.66%, the prediction accuracy of the model based on KC after feature selection was 94.83%, and the classification accuracy of the model based on the fused data of CARS + KC reached as high as 98.28%. This study shows that the fusion of effective feature information from GC-MS and NIR data can enable more accurate and stable grade identification of raw Nongxiangxin Baijiu than either analytical technique alone, which provides a new idea and theoretical basis for the grade identification and quality control of raw Baijiu.

Key words: raw Nongxiangxin Baijiu; near infrared spectroscopy; gas chromatography-mass spectrometry; data fusion; extreme gradient boosting tree