Abstract
Vitamin C and total acidity were determined in industrialized fruit nectar and soy juices through visible-near infrared (Vis-NIR) spectroscopy and multiproduct, multicomponent, and multivariate calibration, based on partial least squares (PLS2) regression. Since samples with different types, flavors, and sugar content (light or not) were together in the model construction, the samples present higher heterogeneity and it was necessary to optimize the calibration and validation sets by outlier elimination based on leverage and unmodelled residuals in spectral data. The model was developed and validated by the evaluation of the parameters of merit such as accuracy, analytical sensitivity, adjust, linearity, residual prediction deviation, limits of detection, and quantification. The results achieved indicates that the multiproduct, multicomponent, and multivariate calibration model developed from Vis-NIR spectroscopy and PLS2 regression can be used in the industrial routine analysis as an alternative to titration reference methods that are time- and reagent-consuming methods, making the methodology extremely attractive from the industrial point of view.
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