Stable Elemental Fingerprints for Multi-Attribute Classification of Ginseng Using Laser-Induced Breakdown Spectroscopy.
- 1College of Optical, Mechanical and Electrical Engineering (College of Intelligent Equipment Engineering), Zhejiang A&F University, Hangzhou 311300, China.
Abstract
Laser-induced breakdown spectroscopy (LIBS) was evaluated for qualitative within-batch discrimination of ginseng materials by plant category, geographical origin, and growth year. The datasets comprised 200 spectra from four plant categories, 100 spectra from two origins, and 100 spectra from two growth-year groups; each class contained 50 pellets prepared from one ground-and-sieved powder batch. Four chemometric pipelines were compared using five-fold nested cross-validation repeated five times, with preprocessing, wavelength filtering, and tuning restricted to the training folds. The best mean balanced accuracies were 1.000, 0.982, and 1.000 for plant category, origin, and growth year, respectively. An SNV-ANOVA50 filter pipeline retained balanced accuracies of 1.000, 0.980, and 1.000 with 50 of 12,990 detector channels. Conservative wavelength matching, limited to the nominal 0.09 nm instrumental resolution, yielded tentative Ba, Al, Ca, and Fe candidates; these matches do not establish elemental identity. A full-spectrum plus standard normal variate (SNV) control showed that resistance to multiplicative scaling and additive offsets arose primarily from SNV rather than from wavelength selection. Detector-channel shifts remained damaging, especially for the filtered pipeline. Because class identity is confounded with powder-batch identity, the reported cross-validation quantifies technical pellet-level separability within the measured materials and does not estimate performance across independent biological batches.
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BibTeX
@article{dai2026stable,
title = {{Stable Elemental Fingerprints for Multi-Attribute Classification of Ginseng Using Laser-Induced Breakdown Spectroscopy.}},
author = {Yujia Dai and Yilei Zhang and Huangwei Li and Shangyong Zhao and Ziyuan Liu},
journal = {Plants (Basel, Switzerland)},
year = {2026},
month = oct,
doi = {10.3390/plants15203124},
url = {https://europepmc.org/article/MED/42943077},
note = {CC BY},
}
RIS
TY - JOUR
TI - Stable Elemental Fingerprints for Multi-Attribute Classification of Ginseng Using Laser-Induced Breakdown Spectroscopy.
AU - Yujia Dai
AU - Yilei Zhang
AU - Huangwei Li
AU - Shangyong Zhao
AU - Ziyuan Liu
PY - 2026
DA - 2026/10/13
JO - Plants (Basel, Switzerland)
DO - 10.3390/plants15203124
UR - https://europepmc.org/article/MED/42943077
AB - Laser-induced breakdown spectroscopy (LIBS) was evaluated for qualitative within-batch discrimination of ginseng materials by plant category, geographical origin, and growth year. The datasets comprised 200 spectra from four plant categories, 100 spectra from two origins, and 100 spectra from two growth-year groups; each class contained 50 pellets prepared from one ground-and-sieved powder batch. Four chemometric pipelines were compared using five-fold nested cross-validation repeated five times, with preprocessing, wavelength filtering, and tuning restricted to the training folds. The best mean balanced accuracies were 1.000, 0.982, and 1.000 for plant category, origin, and growth year, respectively. An SNV-ANOVA50 filter pipeline retained balanced accuracies of 1.000, 0.980, and 1.000 with 50 of 12,990 detector channels. Conservative wavelength matching, limited to the nominal 0.09 nm instrumental resolution, yielded tentative Ba, Al, Ca, and Fe candidates; these matches do not establish elemental identity. A full-spectrum plus standard normal variate (SNV) control showed that resistance to multiplicative scaling and additive offsets arose primarily from SNV rather than from wavelength selection. Detector-channel shifts remained damaging, especially for the filtered pipeline. Because class identity is confounded with powder-batch identity, the reported cross-validation quantifies technical pellet-level separability within the measured materials and does not estimate performance across independent biological batches.
SN - 2223-7747
ER -