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.

Green Chemistry is a vital and crucial instrument in achieving pollution control, and it plays an important role in helping society reach the Sustainable Development Goals (SDGs). NIR (near-infrared spectroscopy) has been utilized as an alternate technique for molecular identification, making the process faster and less expensive. Near-infrared diffuse reflectance spectroscopy and Machine Learning (ML) algorithms were utilized in this study to construct identification and classification models of bacteria such as Escherichia coli, Salmonella enteritidis, Enterococcus faecalis and Listeria monocytogenes. Furthermore, divide these bacteria into Gram-negative and Gram-positive groups. The green and quick approach was created by combining NIR spectroscopy with a diffuse reflectance accessory. Using infrared spectral data and ML techniques such as principal component analysis (PCA), hierarchical cluster analysis (HCA) and K-Nearest Neighbor (KNN), It was feasible to accomplish the identification and classification of four bacteria and classify these bacteria into two groups: Gram-positive and Gram-negative, with 100% accuracy. We may conclude that our study has a high potential for bacterial identification and classification, as well as being consistent with global policies of sustainable development and green analytical chemistry.