A novel microscopic hyperspectral imaging detection method for quantitative powdered food adulteration assessment: A comparative study of CNN-RNN attention models.
- DOI
- 10.1016/j.talanta.2026.130490
- Published
- 2026 Sep 17
- Container
- Talanta
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.talanta.2026.130490,
title = {A novel microscopic hyperspectral imaging detection method for quantitative powdered food adulteration assessment: A comparative study of CNN-RNN attention models.},
author = {Zhang S and Hao Y and Liu T and Sang T and Zhang Z and Wang S},
year = {2026},
journal = {Talanta},
doi = {10.1016/j.talanta.2026.130490},
url = {https://doi.org/10.1016/j.talanta.2026.130490}
}RIS
TY - JOUR TI - A novel microscopic hyperspectral imaging detection method for quantitative powdered food adulteration assessment: A comparative study of CNN-RNN attention models. AU - Zhang S AU - Hao Y AU - Liu T AU - Sang T AU - Zhang Z AU - Wang S PY - 2026 JO - Talanta DO - 10.1016/j.talanta.2026.130490 UR - https://doi.org/10.1016/j.talanta.2026.130490 ER -
APA
S, Z., Y, H., T, L., T, S., Z, Z., & S, W. (2026). A novel microscopic hyperspectral imaging detection method for quantitative powdered food adulteration assessment: A comparative study of CNN-RNN attention models.. Talanta. https://doi.org/10.1016/j.talanta.2026.130490
Source records
- pubmed · retrieved 2026-09-27T09:30:19.518Z