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Article

Marker-Independent Food Identification Enabled by Combing Machine Learning Algorithms with Comprehensive GC × GC/TOF-MS

1
State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
2
Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China
3
National Engineering Research Center of Solid-State Brewing, Luzhou 646000, China
4
The Instrumental Analysis Center, Shanghai Jiao Tong University, Shanghai 200240, China
*
Author to whom correspondence should be addressed.
Academic Editors: Junhu Cheng, Shigeru Itoh and Young-Suk Kim
Molecules 2022, 27(19), 6237; https://doi.org/10.3390/molecules27196237
Received: 5 September 2022 / Revised: 18 September 2022 / Accepted: 19 September 2022 / Published: 22 September 2022
(This article belongs to the Special Issue Food Chemistry in Asia)
Reliable methods are always greatly desired for the practice of food inspection. Currently, most food inspection techniques are mainly dependent on the identification of special components, which neglect the combination effects of different components and often lead to biased results. By using Chinese liquors as an example, we developed a new food identification method based on the combination of machine learning with GC × GC/TOF-MS. The sample preparation methods SPME and LLE were compared and optimized for producing repeatable and high-quality data. Then, two machine learning algorithms were tried, and the support vector machine (SVM) algorithm was finally chosen for its better performance. It is shown that the method performs well in identifying both the geographical origins and flavor types of Chinese liquors, with high accuracies of 91.86% and 97.67%, respectively. It is also reasonable to propose that combining machine learning with advanced chromatography could be used for other foods with complex components. View Full-Text
Keywords: Chinese liquors; food inspection; GC × GC/TOF-MS; machine learning Chinese liquors; food inspection; GC × GC/TOF-MS; machine learning
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MDPI and ACS Style

Li, B.; Liu, M.; Lin, F.; Tai, C.; Xiong, Y.; Ao, L.; Liu, Y.; Lin, Z.; Tao, F.; Xu, P. Marker-Independent Food Identification Enabled by Combing Machine Learning Algorithms with Comprehensive GC × GC/TOF-MS. Molecules 2022, 27, 6237. https://doi.org/10.3390/molecules27196237

AMA Style

Li B, Liu M, Lin F, Tai C, Xiong Y, Ao L, Liu Y, Lin Z, Tao F, Xu P. Marker-Independent Food Identification Enabled by Combing Machine Learning Algorithms with Comprehensive GC × GC/TOF-MS. Molecules. 2022; 27(19):6237. https://doi.org/10.3390/molecules27196237

Chicago/Turabian Style

Li, Bei, Miao Liu, Feng Lin, Cui Tai, Yanfei Xiong, Ling Ao, Yumin Liu, Zhixin Lin, Fei Tao, and Ping Xu. 2022. "Marker-Independent Food Identification Enabled by Combing Machine Learning Algorithms with Comprehensive GC × GC/TOF-MS" Molecules 27, no. 19: 6237. https://doi.org/10.3390/molecules27196237

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