Identification of the Age of Luzhou-Flavor Base Baijiu by Gas Chromatography-Mass Spectrometry Fingerprinting and eXtreme Gradient Boosting Machine Learning

In order to identify the age of Luzhou-flavor base baijiu, headspace solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) was used to create a fingerprint of the volatile composition of Luzhou-flavor base baijiu, and the eXtreme Gradient Boosting (XGBoost) algorithm was used to establish a regression model. Feature selection was conducted via a combination of variable importance evaluation using the extremely randomized trees, and F_regression and mutual_info_regression in the sklearn feature selection module. The coefficient of determination (R²) of the proposed regression model was 0.987, demonstrating good predictive reliability. This study provides a new idea for the identification of baijiu age.