Publicação
Spectral markers and machine learning: Revolutionizing Rice evaluation with near infrared spectroscopy
| Resumo: | 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. |
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| Autores principais: | Sampaio, Pedro Sousa |
| Outros Autores: | Carbas, Bruna; Soares, Andreia; Sousa, Inês; Brites, Carla |
| Assunto: | Classification models Machine learning techniques NIR spectroscopy PCA PLS-DA Rice Spectral markers |
| Ano: | 2025 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Instituto Politécnico de Bragança |
| Idioma: | inglês |
| Origem: | Biblioteca Digital do IPB |
| Resumo: | 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. |
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