“…Data exploration allows finding sample groups, the relation between variables and management with outliers samples by means of a PCA or a parallel factor analysis (PARAFAC) (Bro, ; Rodrigues, Condino, Pinheiro, & Nunes, ). Data preprocessing can be handled with preprocessing algorithms, such as smoothing methods (Savitzky–Golay, Gaussian filter, median filter, moving average), normalization and scaling, detrending (Levasseur‐garcia, ), 1 st Derivate, 2 nd Derivate–Savitzky Golay (Savitzky & Golay, ), Standard Normal Variation (Teye, Uhomoibhi, & Wang, ), Orthogonal Signal Correction (Wold, Antti, Lindgren, & Öhman, ), and Multiple Scatter Correction to build and enhance calibration models (Su & Sun, ). The selected preprocessing method can be related to data features to, for example, rid up multiplicative and additive effects in spectra.…”