Molecular Recognition and Threshold Prediction Model of Bitterness in Natural Compounds
It is important to identify the bitter substances in natural compounds and determine their bitterness threshold for finding out the bitter molecules that affect the flavor of food and developing some foods with unique flavors. Identifying bitter molecules and predicting the threshold of bitter molec...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
The editorial department of Science and Technology of Food Industry
2022-02-01
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| Series: | Shipin gongye ke-ji |
| Subjects: | |
| Online Access: | http://www.spgykj.com/cn/article/doi/10.13386/j.issn1002-0306.2021080020 |
| _version_ | 1798017481545089024 |
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| author | Baolong FENG Haibin REN Jiahui DUAN Housen ZHANG Chunhui WEN Xiaosen BAI Fei GAO Yutang WANG |
| author_facet | Baolong FENG Haibin REN Jiahui DUAN Housen ZHANG Chunhui WEN Xiaosen BAI Fei GAO Yutang WANG |
| author_sort | Baolong FENG |
| collection | DOAJ |
| description | It is important to identify the bitter substances in natural compounds and determine their bitterness threshold for finding out the bitter molecules that affect the flavor of food and developing some foods with unique flavors. Identifying bitter molecules and predicting the threshold of bitter molecules based on the quantitative structure-activity relationship is a low-cost and rapid method. This research used Molecular Operating Environment (MOE), Chemopy and Mordred to generate 2D molecular descriptor to establish bitterness molecular recognition models with Support Vector Machine (SVM) and Random Forests (RF) algorithms. This study used above descriptors to establish bitterness threshold prediction models with Partial Least Squares Regression (PLSR), Random Forests Regression (RFR), k-Nearest Neighbor Regression (kNNR), and Principle Component Regression (PCR) algorithms. The results showed that the MOE-RF model had the highest accuracy of 0.982, the ChemoPy-PLSR model had the best bitterness prediction effect with a coefficient of determination of 0.85 and a root mean square error of 0.43. The two models would be combined to predict whether the molecule has bitterness and the threshold of bitterness or not. |
| first_indexed | 2024-04-11T16:07:56Z |
| format | Article |
| id | doaj.art-ec08eaf7c81240f3a1a86d7a5912d7f0 |
| institution | Directory Open Access Journal |
| issn | 1002-0306 |
| language | zho |
| last_indexed | 2024-04-11T16:07:56Z |
| publishDate | 2022-02-01 |
| publisher | The editorial department of Science and Technology of Food Industry |
| record_format | Article |
| series | Shipin gongye ke-ji |
| spelling | doaj.art-ec08eaf7c81240f3a1a86d7a5912d7f02022-12-22T04:14:46ZzhoThe editorial department of Science and Technology of Food IndustryShipin gongye ke-ji1002-03062022-02-01434243210.13386/j.issn1002-0306.20210800202021080020-4Molecular Recognition and Threshold Prediction Model of Bitterness in Natural CompoundsBaolong FENG0Haibin REN1Jiahui DUAN2Housen ZHANG3Chunhui WEN4Xiaosen BAI5Fei GAO6Yutang WANG7Center for Education Technology Northeast Agricultural University, Harbin 150030, ChinaKey Laboratory of Dairy Science, Ministry of Education, Northeast Agricultural University, Harbin 150030, ChinaCollege of Food Science, Northeast Agricultural University, Harbin 150030, ChinaCollege of Food Science, Northeast Agricultural University, Harbin 150030, ChinaCollege of Food Science, Northeast Agricultural University, Harbin 150030, ChinaCollege of Food Science, Northeast Agricultural University, Harbin 150030, ChinaCenter for Education Technology Northeast Agricultural University, Harbin 150030, ChinaKey Laboratory of Dairy Science, Ministry of Education, Northeast Agricultural University, Harbin 150030, ChinaIt is important to identify the bitter substances in natural compounds and determine their bitterness threshold for finding out the bitter molecules that affect the flavor of food and developing some foods with unique flavors. Identifying bitter molecules and predicting the threshold of bitter molecules based on the quantitative structure-activity relationship is a low-cost and rapid method. This research used Molecular Operating Environment (MOE), Chemopy and Mordred to generate 2D molecular descriptor to establish bitterness molecular recognition models with Support Vector Machine (SVM) and Random Forests (RF) algorithms. This study used above descriptors to establish bitterness threshold prediction models with Partial Least Squares Regression (PLSR), Random Forests Regression (RFR), k-Nearest Neighbor Regression (kNNR), and Principle Component Regression (PCR) algorithms. The results showed that the MOE-RF model had the highest accuracy of 0.982, the ChemoPy-PLSR model had the best bitterness prediction effect with a coefficient of determination of 0.85 and a root mean square error of 0.43. The two models would be combined to predict whether the molecule has bitterness and the threshold of bitterness or not.http://www.spgykj.com/cn/article/doi/10.13386/j.issn1002-0306.2021080020bitter compoundsrecognitionthresholdpredictionverification |
| spellingShingle | Baolong FENG Haibin REN Jiahui DUAN Housen ZHANG Chunhui WEN Xiaosen BAI Fei GAO Yutang WANG Molecular Recognition and Threshold Prediction Model of Bitterness in Natural Compounds Shipin gongye ke-ji bitter compounds recognition threshold prediction verification |
| title | Molecular Recognition and Threshold Prediction Model of Bitterness in Natural Compounds |
| title_full | Molecular Recognition and Threshold Prediction Model of Bitterness in Natural Compounds |
| title_fullStr | Molecular Recognition and Threshold Prediction Model of Bitterness in Natural Compounds |
| title_full_unstemmed | Molecular Recognition and Threshold Prediction Model of Bitterness in Natural Compounds |
| title_short | Molecular Recognition and Threshold Prediction Model of Bitterness in Natural Compounds |
| title_sort | molecular recognition and threshold prediction model of bitterness in natural compounds |
| topic | bitter compounds recognition threshold prediction verification |
| url | http://www.spgykj.com/cn/article/doi/10.13386/j.issn1002-0306.2021080020 |
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