Work

Spectral markers and machine learning: Revolutionizing Rice evaluation with near infrared spectroscopy

Year: 2025

Type: article

Abstract: The evaluation of rice varieties is a complex, time-consuming process requiring advanced equipment. This study aimed to discriminate 22 commercial rice varieties from six types by analyzing biochemical, physicochemical, and cooking properties. Near-infrared (NIR) spectroscopy, combined with machine learning, linked molecular properties with quality traits, offering a high-throughput solution. Partial Least Squares (PLS) models accurately predicted parameters such as whiteness (R 2 = 0.94), width (R 2 = 0.94), resilience (R 2 = 0.96), and springiness (R 2 = 0.98), highlighting key wavelength regions. Principal Component Analysis (PCA) revealed distinct clustering patterns, while Partial Least Squares Discriminant Analysis (PLS-DA) achieved a 17 % error rate in external predictions. Spectral markers at A6032/4457 cm −1 , A7004/5241 cm −1 , and A7004/4749 cm −1 reflected biomolecular differences among varieties. This innovative approach enables precise quantification, classification, and differentiation of rice types, enhancing quality control, improving consumer satisfaction, and optimizing breeding selection processes efficiently.

Source: Food Chemistry

DOI: 10.1016/j.foodchem.2025.145569

Authors: Pedro Sousa Sampaio, Bruna Carbas, Andreia Soares, Inês Sousa, Carla Brites

Institutions: Instituto Nacional de Investigação Agrária e Veterinária, Universidade Lusófona, Universidade Politécnica de Bragança, Centro de Investigação de Montanha, Instituto de Tecnologia Química e Biológica António Xavier

Language: English

Topic: Spectroscopy and Chemometric Analyses

Subfield: Analytical Chemistry

Field: Chemistry

Domain: Physical Sciences

SDG: Zero hunger

Open Access status: hybrid

Estimated APC paid: $4,930