| 基于GC-MS指纹图谱和XGBoost机器学习的泸型基酒贮存时间鉴别 |
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| 引用本文： | 刘青茹,孟连君,张晓娟,翟伟绩,柴丽娟,陆震鸣,许泓瑜,王松涛,张宿义,沈才洪,史劲松,许正宏. 基于GC-MS指纹图谱和XGBoost机器学习的泸型基酒贮存时间鉴别[J]. 食品科学, 2022, 43(24): 310-317. DOI: 10.7506/spkx1002-6630-20211129-354 |
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| 作者姓名： | 刘青茹 孟连君 张晓娟 翟伟绩 柴丽娟 陆震鸣 许泓瑜 王松涛 张宿义 沈才洪 史劲松 许正宏 |
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| 作者单位： | （1.江南大学生物工程学院,江苏 无锡 214122;2.江南大学 粮食发酵与食品生物制造国家工程研究中心,江苏 无锡 214122;3.江南大学生命科学与健康工程学院,江苏 无锡 214122;4.国家固态酿造工程技术研究中心,四川 泸州 646000） |
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| 基金项目： | “十三五”国家重点研发计划重点专项（2018YFC1604104）;四川省固态酿造技术创新中心建设项目（2021ZYD0102） |
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| 摘 要： | 通过顶空固相微萃取结合气相色谱-质谱联用采集挥发性成分指纹图谱,采用极端梯度提升算法建立回归模型,运用极端随机森林的变量重要性评估、sklearn特征选择模块中的单变量线性回归测试（F_regression）以及连续目标变量的互信息（mutual_info_regression）确定有效建模变量,对白酒的贮存时间进行鉴别。模型的R2评估结果为0.987,预测模型可靠性较好,为白酒酒龄的判断提供了新思路。
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| 关 键 词： | 白酒年份 挥发性化合物 特征筛选 机器学习 鉴别, |
Identification of the Age of Luzhou-Flavor Base Baijiu by Gas Chromatography-Mass Spectrometry Fingerprinting and eXtreme Gradient Boosting Machine Learning |
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| LIU Qingru,MENG Lianjun,ZHANG Xiaojuan,ZHAI Weiji,CHAI Lijuan,LU Zhenming,XU Hongyu,WANG Songtao,ZHANG Suyi,SHEN Caihong,SHI Jingsong,XU Zhenghong. Identification of the Age of Luzhou-Flavor Base Baijiu by Gas Chromatography-Mass Spectrometry Fingerprinting and eXtreme Gradient Boosting Machine Learning[J]. Food Science, 2022, 43(24): 310-317. DOI: 10.7506/spkx1002-6630-20211129-354 |
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| Authors: | LIU Qingru MENG Lianjun ZHANG Xiaojuan ZHAI Weiji CHAI Lijuan LU Zhenming XU Hongyu WANG Songtao ZHANG Suyi SHEN Caihong SHI Jingsong XU Zhenghong |
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| Affiliation: | (1. School of Biotechnology, Jiangnan University, Wuxi 214122, China; 2. National Engineering Research Center of Cereal Fermentation and Food Biomanufacturing, Jiangnan University, Wuxi 214122, China; 3. School of Life Science and Health Engineering, Jiangnan University, Wuxi 214122, China; 4. National Engineering Research Center of Solid-State Brewing, Luzhou 646000, China) |
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| 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. |
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| Keywords: | baijiu age volatile compounds feature selection machine learning discrimination, |
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