Application of near-infrared spectroscopy for physicochemical characterization of soft and flint corn grains in pre-processing, storage and industrial unit as alternative to the subjective physical classification method

Abstract

Corn is a vital cereal crop for food security, which relies heavily on efficient practices and technologies. The adoption of non-destructive technologies for grain classification minimizes post-harvest losses, ensures compliance with food safety standards, and optimizes grain allocation for appropriate uses. This study aimed to evaluate the physical classification and physicochemical quality of corn grains using indirect methods, as well as the impact of defect levels on physicochemical quality. Thus, it was evaluating the application of non-destructive technology for physicochemical characterization of soft and flint corn grains in pre-processing, storage and industrial unit as alternative to the subjective physical classification method. Corn grains were initially classified using traditional methods. For individual characterization, 20 g samples of both defective and healthy grains were analyzed. Subsequently, 2 kg samples were prepared for each classification (Type I, II, III, and off-type), subdivided into 100 subsamples of 20 g each. Near-infrared spectroscopy (NIR) was employed to analyze physicochemical composition. Data were evaluated using descriptive statistics and multivariate tests. It was observed that the physicochemical composition of corn grains is influenced by grain conditions, framing and group, as well as by the interaction between these factors. The application of PCA and Pearson correlation proved to be adequate to evaluate the multivariate structure of the data obtained in the experiment. The identified correlations contributed to the understanding of the patterns observed in the analysis of variance and in the PCA. It is concluded that NIR increases the efficiency and accuracy in quality assessment, significantly reducing the time required for traditional grain classification. Multivariate analyses also serve as effective tools for data interpretation.