High-throughput targeted quantification and interpretable machine learning for differentiating Baijiu aroma types
- DOI
- 10.1016/j.chroma.2026.467458
- Published
- 2026-10
- Container
- Journal of Chromatography A
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.chroma.2026.467458,
title = {High-throughput targeted quantification and interpretable machine learning for differentiating Baijiu aroma types},
author = {Sen Luo and Jiao Niu and Li Zhu and Kezan Xu and Jian Wang and Yaru Wang and Juxiu Li and Hongbo Gao and Lili Jiang and Xinguang Guo},
year = {2026},
journal = {Journal of Chromatography A},
doi = {10.1016/j.chroma.2026.467458},
url = {https://doi.org/10.1016/j.chroma.2026.467458}
}RIS
TY - JOUR TI - High-throughput targeted quantification and interpretable machine learning for differentiating Baijiu aroma types AU - Sen Luo AU - Jiao Niu AU - Li Zhu AU - Kezan Xu AU - Jian Wang AU - Yaru Wang AU - Juxiu Li AU - Hongbo Gao AU - Lili Jiang AU - Xinguang Guo PY - 2026 JO - Journal of Chromatography A DO - 10.1016/j.chroma.2026.467458 UR - https://doi.org/10.1016/j.chroma.2026.467458 ER -
APA
Luo, S., Niu, J., Zhu, L., Xu, K., Wang, J., Wang, Y., Li, J., Gao, H., Jiang, L., & Guo, X. (2026). High-throughput targeted quantification and interpretable machine learning for differentiating Baijiu aroma types. Journal of Chromatography A. https://doi.org/10.1016/j.chroma.2026.467458
Source records
- crossref · retrieved 2026-09-25T21:53:44.923Z