Multicriteria decision making-based approach to classify loose-leaf teas
Near infrared spectra of 75 different loose-leaf teas were analyzed based on their oxidational state: white, green, matcha, oolong, black, dark and pu-erh. Different spectral transformations (MSC, SNV and derivatives) and seven supervised linear and non-linear chemometric methods were performed. Cla...
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| Language: | English |
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Elsevier
2025-03-01
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| Series: | NFS Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352364625000070 |
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| author | Eszter Benes Attila Gere |
| author_facet | Eszter Benes Attila Gere |
| author_sort | Eszter Benes |
| collection | DOAJ |
| description | Near infrared spectra of 75 different loose-leaf teas were analyzed based on their oxidational state: white, green, matcha, oolong, black, dark and pu-erh. Different spectral transformations (MSC, SNV and derivatives) and seven supervised linear and non-linear chemometric methods were performed. Classification methods were ranked based on their model performance metrics. In the ranking of the models, multicriteria decision making (MCDM) methods have crucial role, of which sum of ranking differences (SRD) method was used. SNV preprocessing showed better performance compared to MSC and FD + SNV. Among the models, linear support vector machine (lSVM) gave satisfactory performance regardless of the preprocessing. lSVM used on SNV preprocessed data proved to be the far best model, with 83.3 % accuracy. However, it is important to note that there are no general rules regarding model performances and proper testing is always advised. For such, multicriteria decision making models (and especially SRD) is strongly advised. |
| format | Article |
| id | doaj-art-59b1ee7b356e496e960825b5766c00c4 |
| institution | Kabale University |
| issn | 2352-3646 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NFS Journal |
| spelling | doaj-art-59b1ee7b356e496e960825b5766c00c42025-02-06T05:12:00ZengElsevierNFS Journal2352-36462025-03-0138100218Multicriteria decision making-based approach to classify loose-leaf teasEszter Benes0Attila Gere1Hungarian University of Agriculture and Life Sciences, Institute of Food Science and Technology, Department of Food and Analytical Chemistry, H-1118, Budapest, Villányi út, 29-43, HungaryHungarian University of Agriculture and Life Sciences, Institute of Food Science and Technology, Department of Postharvest, Supply Chain, Commerce and Sensory Science, H-1118, Budapest, Villányi út, 29-43, Hungary; Corresponding author.Near infrared spectra of 75 different loose-leaf teas were analyzed based on their oxidational state: white, green, matcha, oolong, black, dark and pu-erh. Different spectral transformations (MSC, SNV and derivatives) and seven supervised linear and non-linear chemometric methods were performed. Classification methods were ranked based on their model performance metrics. In the ranking of the models, multicriteria decision making (MCDM) methods have crucial role, of which sum of ranking differences (SRD) method was used. SNV preprocessing showed better performance compared to MSC and FD + SNV. Among the models, linear support vector machine (lSVM) gave satisfactory performance regardless of the preprocessing. lSVM used on SNV preprocessed data proved to be the far best model, with 83.3 % accuracy. However, it is important to note that there are no general rules regarding model performances and proper testing is always advised. For such, multicriteria decision making models (and especially SRD) is strongly advised.http://www.sciencedirect.com/science/article/pii/S2352364625000070Sum of ranking differencesTeaQualityNIRMachine learning |
| spellingShingle | Eszter Benes Attila Gere Multicriteria decision making-based approach to classify loose-leaf teas NFS Journal Sum of ranking differences Tea Quality NIR Machine learning |
| title | Multicriteria decision making-based approach to classify loose-leaf teas |
| title_full | Multicriteria decision making-based approach to classify loose-leaf teas |
| title_fullStr | Multicriteria decision making-based approach to classify loose-leaf teas |
| title_full_unstemmed | Multicriteria decision making-based approach to classify loose-leaf teas |
| title_short | Multicriteria decision making-based approach to classify loose-leaf teas |
| title_sort | multicriteria decision making based approach to classify loose leaf teas |
| topic | Sum of ranking differences Tea Quality NIR Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2352364625000070 |
| work_keys_str_mv | AT eszterbenes multicriteriadecisionmakingbasedapproachtoclassifylooseleafteas AT attilagere multicriteriadecisionmakingbasedapproachtoclassifylooseleafteas |