Short-wavelength near infrared spectroscopy (SW-NIR) is a very rapid, versatile and precise technique, which can be used in many different situations and for very types of products and chemical compounds. Extended multiplicative signal correction (EMSC) is a modification of the standard MSC pre-processing method that allows the separation of physical light scattering effects from chemical (vibrational) light absorbance effects in spectra. In this paper, the EMSC is applied and compared with first derivate, second derivate, MSC and SNV in combination of PLSR to obtain robust models in terms of accuracy and predict ability with a reduced calibration data set using SW-NIR spectra of moisture in marzipan. The Extended Multiplicative Signal Correction-EMSC and combination methods provide the best results in terms of prediction ability and calibration SW-NIR spectra of moisture in marzipan. The best classification results were obtained by Extended Multiplicative Signal Correction followed by second derivates.

The differentiation of cultivars is carried out by means of morphological descriptors, in addition to molecular markers. In this work, near-infrared spectroscopy (NIR) and chemometric techniques were used to develop classification models for two different commercial sesame cultivars (Sesamum indicum) and 3 different strains. The diffuse reflectance spectra were recorded in the region of 700 to 2500 nm. Based on the application of chemometric techniques: principal component analysis—PCA, hierarchical cluster analysis—HCA, k-nearest neighbor—KNN and the flexible independent modeling of class analogy—SIMCA, from the infrared spectra in the near region, it was possible to perform the genotyping of two sesame cultivars (BRS Seda and BRS Anahí), and to classify these cultivars with 3 different sesame strains, obtaining 100% accurate results. Due to the good results obtained with the implemented models, the potential of the methods for a possible realization of forensic, fast and non-destructive authentication, in intact sesame seeds was evident.

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This work presents an analytical technique, using chemometrics in data obtained by Nearinfrared, which has the potential to assist Forensic Science in the geographical

Pulp oils from buriti fruit (Mauritia Fleuxosa L.), from the Amazon region, were obtained from different locations of the state of Roraima - Brazil, for the application of Near Infrared Spectrometry with Fourier Transform (FTNIR) (PCA) and Hierarchical Cluster Analysis (HCA) as a methodological alternative for geographical discrimination of buriti collection points, which has been the focus of research related to bioactive compound compositions and presented potential for oil extraction for use in the fuel, pharmaceutical, cosmetic and food industries. In total, twenty samples of these oils were analyzed in the 700 to 2000 nm range, and processed through application PCA and HCA . In both cases the potentialities of the techniques were confirmed, obtaining satisfactory results for the geographic identification of the oils collected in the places of Boa Vista, Mucajaí, São João da Baliza and Caroebe, with a data description of up to 99.77% by PCA. Which demonstrates the effectiveness of the methods for discriminations without destruction of the samples and in real-time, useful in the efficient monitoring of healthy and safe products to consumers.