Grade Identification of Raw Nongxiangxing Baijiu Based on Fused Data of Near Infrared Spectroscopy and Gas Chromatography-Mass Spectrometry
2024
ZHANG Wei, ZHANG Guiyu, TUO Xianguo, FU Ni, LI Xiaoping, PANG Tingting, LIU Kecai
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.
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