@article{LIU2022, 
author = {Qingru LIU and Lianjun MENG and Xiaojuan ZHANG and Weiji ZHAI and Lijuan CHAI and Zhenming LU and Hongyu XU and Songtao WANG and Suyi ZHANG and Caihong SHEN and Jingsong SHI and Zhenghong XU},
title = {Identification of the Age of Luzhou-Flavor Base Baijiu by Gas Chromatography-Mass Spectrometry Fingerprinting and eXtreme Gradient Boosting Machine Learning},
year = {2022},
journal = {Food Science},
volume = {43},
number = {24},
pages = {310-317},
keywords = {baijiu age, volatile compounds, feature selection, machine learning, discrimination},
url = {https://www.sciopen.com/article/10.7506/spkx1002-6630-20211129-354},
doi = {10.7506/spkx1002-6630-20211129-354},
abstract = {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.}
}