Fast spark discharge-laser-induced breakdown spectroscopy method for rice botanic origin determination

•Botanical origin of rice predicted by SD-LIBS and machine learning.•First time CCD was used to fit SVM classifier from rice LIBS profiling.•C, Ca, Fe, Mg, N and Na contributed for sample classification.•The best SVM model showed 96.4% of correct predictions in test set. A simple, fast, and efficient spark discharge-laser-induced breakdown spectroscopy (SD-LIBS) method was developed for determining rice botanic origin using predictive modeling based on support vector machine (SVM). Seventy-two samples from four rice varieties (Guri, Irga 424, Puitá, and Taim) were analyzed by SD-LIBS. Spectral lines of C, Ca, Fe, Mg, N and Na were selected as input variables for prediction model fitting. The SVM algorithm parameters were optimized using a central composite design (CCD) to find the better classification performance. The optimum model for discriminating rice samples according to their botanical variety was obtained using C = 5.25 and γ = 0.119. This model achieved 96.4% of correct predictions in test samples and showed sensitivities and specificities per class within the range of 92–100%. The developed method is robust and eco-friendly for rice botanic identification since its prediction results are consistent and reproducible and its application does not generate chemical waste.