Transforming food authenticity testing by the exploitation of a machine learning - Data fusion approach: a tea case study.
- DOI
- 10.1016/j.foodchem.2026.148302
- Published
- 2026 Apr 15
- Container
- Food chemistry
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.foodchem.2026.148302,
title = {Transforming food authenticity testing by the exploitation of a machine learning - Data fusion approach: a tea case study.},
author = {Li Y and Logan N and Petchkongkaew A and Hong Y and Liu X and Birse N and Haughey S and McGrath TF and Wu D and Elliott CT},
year = {2026},
journal = {Food chemistry},
doi = {10.1016/j.foodchem.2026.148302},
url = {https://doi.org/10.1016/j.foodchem.2026.148302}
}RIS
TY - JOUR TI - Transforming food authenticity testing by the exploitation of a machine learning - Data fusion approach: a tea case study. AU - Li Y AU - Logan N AU - Petchkongkaew A AU - Hong Y AU - Liu X AU - Birse N AU - Haughey S AU - McGrath TF AU - Wu D AU - Elliott CT PY - 2026 JO - Food chemistry DO - 10.1016/j.foodchem.2026.148302 UR - https://doi.org/10.1016/j.foodchem.2026.148302 ER -
APA
Y, L., N, L., A, P., Y, H., X, L., N, B., S, H., TF, M., D, W., & CT, E. (2026). Transforming food authenticity testing by the exploitation of a machine learning - Data fusion approach: a tea case study.. Food chemistry. https://doi.org/10.1016/j.foodchem.2026.148302
Source records
- pubmed · retrieved 2026-09-26T16:28:07.917Z