Abstract
Reliable ginseng authentication requires analytical signatures that distinguish plant category, production region, and growth duration while remaining interpretable and operationally compact. This study evaluated laser-induced breakdown spectroscopy (LIBS) as a proximal elemental-sensing method for these three classification tasks. The datasets comprised 200 spectra from four plant categories, 100 spectra from two geographical origins, and 100 spectra from two growth years; each class contained 50 pressed pellets prepared from one homogenized powder batch. Four chemometric pipelines were compared using five-fold nested cross-validation repeated five times, with all preprocessing and model selection confined to training folds. The best mean balanced accuracies were 1.000, 0.982, and 1.000 for plant category, origin, and growth year, respectively. A sparse model using only 50 of 12,990 wavelengths retained balanced accuracies of 1.000, 0.980, and 1.000. Stable regions were provisionally associated with Ba, Sr, and K for plant category, Al for origin, and Ca and Fe for growth year. Standard normal variate preprocessing suppressed sensitivity to uniform intensity scaling and offsets, whereas detector-channel shifts caused marked deterioration. The results show that compact LIBS fingerprints can encode multiple ginseng attributes and identify wavelength registration, rather than intensity variation alone, as a central requirement for future transfer across batches and measurement settings.