Marker-Independent Food Identification Enabled by Combing Machine Learning Algorithms with Comprehensive GC × GC/TOF-MS
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 Chine...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Molecules |
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| Online Access: | https://www.mdpi.com/1420-3049/27/19/6237 |
| _version_ | 1797478082691465216 |
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| author | Bei Li Miao Liu Feng Lin Cui Tai Yanfei Xiong Ling Ao Yumin Liu Zhixin Lin Fei Tao Ping Xu |
| author_facet | Bei Li Miao Liu Feng Lin Cui Tai Yanfei Xiong Ling Ao Yumin Liu Zhixin Lin Fei Tao Ping Xu |
| author_sort | Bei Li |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T21:26:59Z |
| format | Article |
| id | doaj.art-d439620b10864f10bd7e4e7eca085e69 |
| institution | Directory Open Access Journal |
| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-03-09T21:26:59Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj.art-d439620b10864f10bd7e4e7eca085e692023-11-23T21:07:58ZengMDPI AGMolecules1420-30492022-09-012719623710.3390/molecules27196237Marker-Independent Food Identification Enabled by Combing Machine Learning Algorithms with Comprehensive GC × GC/TOF-MSBei Li0Miao Liu1Feng Lin2Cui Tai3Yanfei Xiong4Ling Ao5Yumin Liu6Zhixin Lin7Fei Tao8Ping Xu9State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, ChinaNational Engineering Research Center of Solid-State Brewing, Luzhou 646000, ChinaNational Engineering Research Center of Solid-State Brewing, Luzhou 646000, ChinaState Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, ChinaNational Engineering Research Center of Solid-State Brewing, Luzhou 646000, ChinaNational Engineering Research Center of Solid-State Brewing, Luzhou 646000, ChinaThe Instrumental Analysis Center, Shanghai Jiao Tong University, Shanghai 200240, ChinaState Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, ChinaState Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, ChinaState Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, ChinaReliable 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.https://www.mdpi.com/1420-3049/27/19/6237Chinese liquorsfood inspectionGC × GC/TOF-MSmachine learning |
| spellingShingle | Bei Li Miao Liu Feng Lin Cui Tai Yanfei Xiong Ling Ao Yumin Liu Zhixin Lin Fei Tao Ping Xu Marker-Independent Food Identification Enabled by Combing Machine Learning Algorithms with Comprehensive GC × GC/TOF-MS Molecules Chinese liquors food inspection GC × GC/TOF-MS machine learning |
| title | Marker-Independent Food Identification Enabled by Combing Machine Learning Algorithms with Comprehensive GC × GC/TOF-MS |
| title_full | Marker-Independent Food Identification Enabled by Combing Machine Learning Algorithms with Comprehensive GC × GC/TOF-MS |
| title_fullStr | Marker-Independent Food Identification Enabled by Combing Machine Learning Algorithms with Comprehensive GC × GC/TOF-MS |
| title_full_unstemmed | Marker-Independent Food Identification Enabled by Combing Machine Learning Algorithms with Comprehensive GC × GC/TOF-MS |
| title_short | Marker-Independent Food Identification Enabled by Combing Machine Learning Algorithms with Comprehensive GC × GC/TOF-MS |
| title_sort | marker independent food identification enabled by combing machine learning algorithms with comprehensive gc gc tof ms |
| topic | Chinese liquors food inspection GC × GC/TOF-MS machine learning |
| url | https://www.mdpi.com/1420-3049/27/19/6237 |
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