Classifying impurity ranges in raw sugarcane using laser-induced breakdown spectroscopy (LIBS) and sum fusion across a tuning parameter window

Abstract

The presence in raw sugarcane of low levels of solid impurities from soil particles and green and dry/brown sugarcane leaves is relevant to improving sugar mill production performance. Two ranges of impurities for raw sugar manufacturing processes need to be characterized from 0 to 5 wt% (desired material) and 8 to 10 wt% (undesired material); these ranges are denoted as 1 and 2, respectively. Laser-induced breakdown spectroscopy (LIBS) combined with chemometrics is used to detect chemical elements and different impurity ranges in leached raw sugarcane solutions. The potential use of LIBS based on leached solutions immobilized in a polyvinyl alcohol (PVA) polymer requires approximately 2 h sample preparation time. LIBS data are assigned to the above two impurity ranges using fusion of multiple classifiers. Most classifiers require a training set and optimization of a tuning parameter to select the best model; however, the sum fusion across a tuning parameter window used for classifying the samples in this study is a process that does not require either. The classification results are 97% accuracy for both ranges; 94% and 100% specificity for ranges 1 and 2, respectively; and 100% and 94% sensitivity for ranges 1 and 2, respectively. The classification results indicate potential for future applications in sugarcane refineries.