Discrimination between domestic and imported red chili powders using laser-induced breakdown spectroscopy: variable selection and comparative evaluation of machine learning algorithms

-NN), Naive Bayes (NB), support vector machine (SVM), and random forest (RF). The highest leave-one-out cross-validation (LOOCV) accuracy was 97.5% and it was achieved with only two or three variables (Mg + Li, Mg + Li + Rb, or Mg + Li + Ca), showing that a compact feature set suffices for reliable discrimination. Bootstrapped random-split validation (100 independent 70 : 30 partitions) further confirmed robustness: mean accuracies were consistent with LOOCV within one standard deviation, and variable-selection frequencies were strongly concentrated on Mg, Li, and Rb (with Ca occasionally replacing Rb). Across models, Mg, Li, Rb, and Ca consistently exhibited the strongest discriminative power, reflecting agro-environmental and geological contrasts between production regions. Overall, LIBS with principled variable selection provides a rapid, cost-effective, and reproducible framework for differentiating domestic and imported red chili powders.