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

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 (R² = 0.94), width (R² = 0.94), resilience (R² = 0.96), and springiness (R² = 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.

Plant phenotyping relevance

イネ品種の穀粒・品質形質をNIR分光と機械学習で高スループットに定量・分類し、PLSモデルの予測精度や外部予測を評価しているため、形質取得法が中心です。

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