Non-Destructive Genotyping of Cultivars and Strains of Sesame through NIR Spectroscopy and Chemometrics

Abstract

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.

Classification of Rice Varieties Using SIMCA Applied to NIR Spectroscopic Data

“…SIMCA classification, also known as similarity and analogy model, is a supervised pattern recognition model based on a known class model. 22 Basically, it processes to create a PCA model for each class in the modeling set, thereby reducing the dimensions of the data. The aim of PCA is to obtain the score matrix T and loading matrix P of each rice variety in a known class.…”