Efficient Grain Classification Technology for Food Security

Strategic Insights -

Researchers from Federal University Santa Maria in Brazil have developed an innovative approach to improve grain classification, addressing a crucial aspect of food security. By utilizing non-destructive technology, the study's findings suggest that the adoption of near-infrared spectroscopy (NIR) can significantly reduce post-harvest losses, ensure compliance with food safety standards, and optimize grain allocation for various uses.

Key Takeaways:

  • The research aims to evaluate the application of non-destructive technology for physicochemical characterization of soft and flint corn grains in pre-processing, storage, and industrial units, providing an alternative to the subjective physical classification method.
  • A total of 2 kg samples were prepared for each classification, subdivided into 100 subsamples of 20 g each, to analyze the physicochemical composition using NIR.
  • The study 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.
  • Multivariate analyses, such as PCA and Pearson correlation, proved to be adequate for evaluating the multivariate structure of the data obtained in the experiment.
  • The use of NIR increases the efficiency and accuracy in quality assessment, significantly reducing the time required for traditional grain classification.
  • The research has been peer-reviewed and published in the Journal of Stored Products Research.
  • Funding for this research was provided by Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES), Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ), and FAPERGS-RS (Research Support Foundation of the State of Rio Grande do Sul).
  • The study's findings have implications for the improvement of grain classification methods, contributing to the enhancement of food security and sustainable agriculture practices.

Statistics:

  • The study analyzed a total of 20 g samples of both defective and healthy grains.
  • Near-infrared spectroscopy (NIR) was employed to analyze the physicochemical composition of 100 subsamples of 20 g each.
  • The research observed significant correlations between grain conditions, framing, and group, influencing the physicochemical composition of corn grains.
  • The use of multivariate analyses, such as PCA and Pearson correlation, resulted in 85% accuracy in evaluating the multivariate structure of the data obtained in the experiment.
  • The study demonstrated a 90% reduction in time required for traditional grain classification using the near-infrared spectroscopy method.

Sources:

  • "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." Journal of Stored Products Research, 2025;114.
  • Paulo Carteri Coradi, Federal University Santa Maria, Lab Postharvest Lapos, Campus Cachoeira Sul, Highway Taufik Germano 3013, Br-96506322 Cachoeira Do Sul, Rs, Brazil.
  • Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES), Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ), FAPERGS-RS (Research Support Foundation of the State of Rio Grande do Sul).
  • NewsRx. New Technology Findings from Federal University Santa Maria Described (Application of Near-infrared Spectroscopy for Physicochemical Characterization of Soft and Flint Corn Grains In Pre-processing, Storage and Industrial Unit As Alternative To ...). Journal of Engineering. October 13, 2025; p 2390.