Characterization of defective coffee beans and blends differentiation based on 1H qNMR technique
Defective coffee beans (DCB) are one of the main reasons for poor coffee quality. In the current research, chemical difference of three common DCB including sour beans (SCB), black beans (BCB), and mold beans (MCB) were clarified using 1H qNMR method and compared with that of non-defective beans (ND...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-01-01
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| Series: | Current Research in Food Science |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665927124001965 |
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| author | Gui-Lin Hu Chen-Xi Quan Hao-Peng Dai Ming-Hua Qiu |
| author_facet | Gui-Lin Hu Chen-Xi Quan Hao-Peng Dai Ming-Hua Qiu |
| author_sort | Gui-Lin Hu |
| collection | DOAJ |
| description | Defective coffee beans (DCB) are one of the main reasons for poor coffee quality. In the current research, chemical difference of three common DCB including sour beans (SCB), black beans (BCB), and mold beans (MCB) were clarified using 1H qNMR method and compared with that of non-defective beans (NDCB). The results indicated that DCB has lower sugar and lipid content compared to NDCB, yet it boasts a higher acetate concentration. The 1H NMR from water-soluble content was shown to be more effective than that of oil fraction for qualitative of DCB blends, regardless of whether partial least squares discriminant analysis (PLS-DA) or machine learning (ML) algorithms were used. Support vector machine (SVM) was proved to be excellent for distinguishing DCB blends. Finally, a partial least squares regression (PLS) model was built for quantitative analysis of DCB blends. In summary, current research will not only help to reveal the material basis of DCB and their impact on coffee flavor, but also provide feasible strategies for the identification of DCB. |
| format | Article |
| id | doaj-art-1eb866b9b8b949b9bd2e3046405a490e |
| institution | DOAJ |
| issn | 2665-9271 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Current Research in Food Science |
| spelling | doaj-art-1eb866b9b8b949b9bd2e3046405a490e2025-08-20T02:49:49ZengElsevierCurrent Research in Food Science2665-92712024-01-01910087010.1016/j.crfs.2024.100870Characterization of defective coffee beans and blends differentiation based on 1H qNMR techniqueGui-Lin Hu0Chen-Xi Quan1Hao-Peng Dai2Ming-Hua Qiu3State Key Laboratory of Phytochemistry and Plant Resources in West China, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, 650201, Yunnan, ChinaState Key Laboratory of Phytochemistry and Plant Resources in West China, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, 650201, Yunnan, ChinaState Key Laboratory of Phytochemistry and Plant Resources in West China, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, 650201, Yunnan, ChinaCorresponding author.; State Key Laboratory of Phytochemistry and Plant Resources in West China, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, 650201, Yunnan, ChinaDefective coffee beans (DCB) are one of the main reasons for poor coffee quality. In the current research, chemical difference of three common DCB including sour beans (SCB), black beans (BCB), and mold beans (MCB) were clarified using 1H qNMR method and compared with that of non-defective beans (NDCB). The results indicated that DCB has lower sugar and lipid content compared to NDCB, yet it boasts a higher acetate concentration. The 1H NMR from water-soluble content was shown to be more effective than that of oil fraction for qualitative of DCB blends, regardless of whether partial least squares discriminant analysis (PLS-DA) or machine learning (ML) algorithms were used. Support vector machine (SVM) was proved to be excellent for distinguishing DCB blends. Finally, a partial least squares regression (PLS) model was built for quantitative analysis of DCB blends. In summary, current research will not only help to reveal the material basis of DCB and their impact on coffee flavor, but also provide feasible strategies for the identification of DCB.http://www.sciencedirect.com/science/article/pii/S2665927124001965Arabica coffeeDefective coffee beansChemometricsMachine learningSupport vector machine |
| spellingShingle | Gui-Lin Hu Chen-Xi Quan Hao-Peng Dai Ming-Hua Qiu Characterization of defective coffee beans and blends differentiation based on 1H qNMR technique Current Research in Food Science Arabica coffee Defective coffee beans Chemometrics Machine learning Support vector machine |
| title | Characterization of defective coffee beans and blends differentiation based on 1H qNMR technique |
| title_full | Characterization of defective coffee beans and blends differentiation based on 1H qNMR technique |
| title_fullStr | Characterization of defective coffee beans and blends differentiation based on 1H qNMR technique |
| title_full_unstemmed | Characterization of defective coffee beans and blends differentiation based on 1H qNMR technique |
| title_short | Characterization of defective coffee beans and blends differentiation based on 1H qNMR technique |
| title_sort | characterization of defective coffee beans and blends differentiation based on 1h qnmr technique |
| topic | Arabica coffee Defective coffee beans Chemometrics Machine learning Support vector machine |
| url | http://www.sciencedirect.com/science/article/pii/S2665927124001965 |
| work_keys_str_mv | AT guilinhu characterizationofdefectivecoffeebeansandblendsdifferentiationbasedon1hqnmrtechnique AT chenxiquan characterizationofdefectivecoffeebeansandblendsdifferentiationbasedon1hqnmrtechnique AT haopengdai characterizationofdefectivecoffeebeansandblendsdifferentiationbasedon1hqnmrtechnique AT minghuaqiu characterizationofdefectivecoffeebeansandblendsdifferentiationbasedon1hqnmrtechnique |