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A compilation of papers published between 2014 and 2018 was evaluated. Many papers related to multivariate calibration and classification have been reported, as well as, design of experiments applications and artificial intelligence methods. Some applications were highlighted, as medical and pharmaceutical, food analysis, fuels, biological and forensic for the chemometric techniques on this review. Most studies are related to developing methods for practical solutions in industry or routine analysis. A promising scenario is shown considering the number of published papers: a total of 832 for this period using the keywords, multivariate classification, multivariate calibration, analysis, chemometrics, prediction, analytical chemistry, artificial neural networks (ANN), design of experiments (DoE) and factorial design. An useful overview for Analytical Chemistry researchers´ combined with Chemometrics is presented in this review.

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.

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