Near Infrared (NIR) Spectroscopy technique combined with chemometrics methods were used to group and identify samples of different soy cultivars. Spectral data, collected in the range of 714 to 2500 nm (14000 to 4000 cm -1 ), were obtained from whole grains of four different soybean cultivars and were submitted to different types of pre-treatments. Chemometrics algorithms were applied to extract relevant information from the spectral data, to remove the anomalous samples and to group the samples. The best results were obtained considering the spectral range from 1900.6 to 2187.7 nm (5261.4 cm -1 to 4570.9 cm -1) and with spectral treatment using Multiplicative Signal Correction (MSC) + Baseline Correct (linear fit), what made it possible to the exploratory techniques Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) to separate the cultivars. Thus, the results demonstrate that NIR spectroscopy allied with de chemometrics techniques can provide a rapid, nondestructive and reliable method to distinguish different cultivars of soybeans.

Rice is one of the most consumed cereals in the world. Currently, techniques for the authentication and geographical origin of rice is known not to be objective because to depend on the naked eye of a well-trained inspector. DNA fingerprint methods have been shown to be inappropriate for on-site application because the method needs a lot of labor and skilled expertise. Rice consumers want to confirm cultivation origin because they believe price or eating score has a high correlation according to them. Considering rice as a raw material of economic and social value and the recent use of NIR spectroscopy coupled with chemometric methods to authentication and discrimination of geographical origin as an alternative to classical methods in the search for a methodology in line with Green Chemistry, this work investigates the potential of NIR spectroscopy combined with multivariate analysis: PCA (Principal Component Analysis) and HCA (Hierarchical Cluster Analysis) for rapid and non-destructive forensic authentication of rice grains from Brazil and Venezuela. This study investigated the potential of near-infrared spectroscopy, combined with PCA and HCA chemometric technique to the authenticity of rice. It was verified that is feasible and advantageous to implement authenticity detection of different brands, typology and geographical discrimination (Brazil and Venezuela) rice.

The potential of Fourier transform mid-infrared spectroscopy with attenuated total reflection (FTIR-ATR) for the quantification of iron (Fe) in vegetable oil extracted from the fruit of the moriche palm (buriti) [Mauritia flexuosa] was evaluated. This green method enables direct measurements without previous sample handling. Twenty-five buriti samples were collected in Roraima (Brazil). The statistical models were developed using the technique of partial least squares (PLS) analysis and the data set was divided into two parts: one used for calibration (n = 20) and one used for testing (n = 5). First, the model was calibrated and cross-validated with the calibration data set so that the model was validated with the test data set to verify its prediction ability. To obtain reference data, the samples were analyzed by X-ray fluorescence (EDXRF). The coefficient of determination (R 2 ) was 0.9965 and the mean square error of prediction (RMSEP) obtained for iron (Fe) was 0.8067 (in ppm). The results showed that the prediction ability can be considered good for large quantification of iron intervals in vegetable oil, and the mean relative errors were less than ±7%. This indicated that the green method for the determination of iron (Fe) in vegetable oil by Fourier transform mid-infrared spectroscopy with attenuated total reflection can be used as an alternative method to the classic methods of analysis, because it does not use reagents harmful to the environment or operator, does not generate harmful waste, uses a fast technique, and there is minimal manipulation of the sample.