Integrating multivariate statistics and interpretable machine learning for the quantitative profiling of tea polyphenols and catechins in six tea categories.
- DOI
- 10.1016/j.foodres.2026.120295
- Published
- 2026 Nov 1
- Container
- Food research international (Ottawa, Ont.)
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.foodres.2026.120295,
title = {Integrating multivariate statistics and interpretable machine learning for the quantitative profiling of tea polyphenols and catechins in six tea categories.},
author = {Li W and Zhou S and Xu J and Wen Q and Jing Y and Chen L and Hong Y and Chai Y and Ma G and Zhang Y and Zhang X and Chen H},
year = {2026},
journal = {Food research international (Ottawa, Ont.)},
doi = {10.1016/j.foodres.2026.120295},
url = {https://doi.org/10.1016/j.foodres.2026.120295}
}RIS
TY - JOUR TI - Integrating multivariate statistics and interpretable machine learning for the quantitative profiling of tea polyphenols and catechins in six tea categories. AU - Li W AU - Zhou S AU - Xu J AU - Wen Q AU - Jing Y AU - Chen L AU - Hong Y AU - Chai Y AU - Ma G AU - Zhang Y AU - Zhang X AU - Chen H PY - 2026 JO - Food research international (Ottawa, Ont.) DO - 10.1016/j.foodres.2026.120295 UR - https://doi.org/10.1016/j.foodres.2026.120295 ER -
APA
W, L., S, Z., J, X., Q, W., Y, J., L, C., Y, H., Y, C., G, M., Y, Z., X, Z., & H, C. (2026). Integrating multivariate statistics and interpretable machine learning for the quantitative profiling of tea polyphenols and catechins in six tea categories.. Food research international (Ottawa, Ont.). https://doi.org/10.1016/j.foodres.2026.120295
Source records
- pubmed · retrieved 2026-09-26T05:54:38.143Z