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Dichromate oxidation and dry combustion analysis were evaluated as reference methods to determine organic carbon in Brazilian soils using near infrared spectroscopy (NIRS) as an alternative. The main objective of this study was to evaluate which of the reference methods could provide a calibration model with higher predictive ability. A total of 161 soil samples obtained from horizons in full profiles (Parque Estadual da Mata Seca, Minas Gerais State, Brazil) were used. Models were mean centered and built from partial least squares. The dichromate oxidation method presented a lower accuracy when compared to dry combustion analysis as reference for NIRS. Figures of merit such as sensitivity, analytical sensitivity, detection and quantification limits, adjust and linearity presented results very similar. A paired t-test was applied to the figures of merit results and with 95% confidence did not show significant differences between the two methods used as reference for NIRS. The non-parametric Mann Whitney test showed that the samples provided with the partial least squares (PLS) model when the reference method was the dichromate oxidation or dry combustion analysis come from the same population, indicating that the reference methods employed for multivariate calibration from NIRS provide the same results practically.

This work shows an alternative methodology based on a portable near-infrared (NIR) spectroscopy coupled to independent components analysis (ICA) in a pseudo-univariate calibration way to determine total anthocyanins (TA) concentration and antioxidant activity (AA) in whole grape juice. To this, the scores proportions more related to TA and AA were plotted against TA and AA obtained by its respective references methodology to build pseudo-univariate calibration models with correlation coefficients of 0.9699 and 0.9814, respectively. From the results, it is possible the suggestion that NIR spectra coupled to ICA enable to overcome interferences using first-order data and work properly when there is enough selectivity for the analyte profile in the sample data.

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