This study proposed a novel strategy for the rapid identification of Nongxiangxing base Baijiu from different regions in Sichuan (Luzhou, Yibin, Chengdu, and Deyang) based on the fused data of gas chromatography (GC) and near infrared spectroscopy (NIR). A total of 225 base Baijiu samples were collected, and two classification models with accuracies of 75.1% and 79.2% were developed using random forest (RF) based on the flavor and spectral data, respectively. The models were found to have limitations in distinguishing complex geographical origins. Subsequently, a fusion classification model with an accuracy of 91.0% was constructed using the XGBoost algorithm based on the feature-level fusion of flavor and spectral information. The fusion model exhibited excellent robustness. The area under the receiver operating characteristic curve for Chengdu, Luzhou, and Yibin was above 0.95 each. Furthermore, by comparing the feature selection results of the fusion model with those of the flavor-based model, eight flavor markers to discriminate base Baijiu samples from different regions were identified through model consensus: ethyl acetate, ethyl lactate, ethyl heptanoate, ethyl formate, isobutanol, acetic acid, butyric acid, and propionic acid. This study not only provides insights for the development of geographical origin discrimination methods for Nongxiangxing base Baijiu, but also offers theoretical support for elucidating the flavor chemistry of Nongxiangxing base Baijiu from different regions of Sichuan.

In this study, high-throughput sequencing technology, linear discriminant analysis effect size (LEfSe) and redundancy analysis (RDA) were used to investigate the structure of prokaryotic and eukaryotic communities, the differential microbial communities, and the relationship between microorganisms and physicochemical factors in pit mud (PM) from cellars of different ages (5, 10 and 20 years) and from different spatial locations of cellars. The results showed that the diversity of both prokaryotic and eukaryotic microorganisms was higher in the five-year-old PM than in the 10- and 20-year-old PM. Among all samples, a total of 37 phyla, 83 classes, 176 orders, 306 families and 629 genera of prokaryotic microorganisms were detected, with five phyla being the dominant ones. Totally 12 phyla, 37 orders, 74 families, 160 families and 282 genera of eukaryotic microorganisms were detected, including three phyla and 15 genera being dominant. LEfSe identified 12 differential prokaryotic genera and 15 differential eukaryotic species, and the differential microbial communities of each sample played an important role in the fermentation process of baijiu. The results of physicochemical analysis showed that the pH of the PM samples varied from 3.66 to 5.20, and the effective phosphorus content of PM was significantly different among locations. RDA analysis showed that Lactobacillus, unclassified_f__Aspergillaceae, and Thermomyces were negatively correlated with moisture, ammonia nitrogen, and effective phosphorus contents, and Methanobacterium, Clostridium_sensu_stricto_12, Caproiciproducens, Methanobrevibacter, Cryptococcus_f__Tremellaceae and Apiotrichum were positively correlated with moisture, pH, ammonia nitrogen, and effective phosphorus. This study provides theoretical support for the establishment of daily PM maintenance as well as high-quality PM domestication and production.