Decoding orthogonal geochemical fingerprints in complex soil matrices via multi-model consensus machine learning for high-fidelity forensic provenance.
- DOI
- 10.1039/d6ay01029f
- Published
- 2026 Aug 13
- Container
- Analytical methods : advancing methods and applications
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1039/d6ay01029f,
title = {Decoding orthogonal geochemical fingerprints in complex soil matrices via multi-model consensus machine learning for high-fidelity forensic provenance.},
author = {Wang P and Jie Z and Guo H and Mei H and Hu C and Yang R and Zhu J and Quan Y},
year = {2026},
journal = {Analytical methods : advancing methods and applications},
doi = {10.1039/d6ay01029f},
url = {https://doi.org/10.1039/d6ay01029f}
}RIS
TY - JOUR TI - Decoding orthogonal geochemical fingerprints in complex soil matrices via multi-model consensus machine learning for high-fidelity forensic provenance. AU - Wang P AU - Jie Z AU - Guo H AU - Mei H AU - Hu C AU - Yang R AU - Zhu J AU - Quan Y PY - 2026 JO - Analytical methods : advancing methods and applications DO - 10.1039/d6ay01029f UR - https://doi.org/10.1039/d6ay01029f ER -
APA
P, W., Z, J., H, G., H, M., C, H., R, Y., J, Z., & Y, Q. (2026). Decoding orthogonal geochemical fingerprints in complex soil matrices via multi-model consensus machine learning for high-fidelity forensic provenance.. Analytical methods : advancing methods and applications. https://doi.org/10.1039/d6ay01029f
Source records
- pubmed · retrieved 2026-09-25T01:48:34.490Z