Near Infrared Spectroscopy and Machine Learning for Non-Destructive Estimation of Ageing of Komal Chaul

Abstract

Komal Chaul is a traditional form of a parboiled rice product from rice varieties indigenous to the state of Assam and it constitutes a culturally significant dish served during the auspicious occasions. Ageing impacts its rehydration properties diminishing its value as a no-cooking rice product. To empower the consumers in selecting Komal Chaul of desired rehydration qualities, this study focused on developing a non-destructive tool based on near-infrared spectral data coupled with machine learning (ML) algorithm for distinguishing the aged Komal Chaul. An NIR spectral library of KomalChaul samples was created, covering the spectral range of 740–1050 nm for samples stored for a period up to one year under ambient conditions. The methodology involved spectral preprocessing to enhance data quality, followed by partial least squares (PLS) regression modeling to predict storage time. Statistical metrics, including regression coefficient (R²), relative error percentage (REP), and root mean squared error (RMSE), were used to validate the model. Feature selection based on coefficient weightage was performed to identify key wavelengths contributing to time prediction. Classification models, including LDA, KNN, CART, Naïve Bayes, SVM, and Random Forest, were employed to categorize samples into aging periods of 1, 3, and 6 months. Partial Least Squares (PLS) regression models predicted the ageing time with a validation score R² of 0.897 and RMSE of 19.41 days. Optimized with wavelength selection, the PLS regression model achieved significant accuracy in estimating the ageing time, with a prediction score R² of 0.89 and RMSE of 2.01 days. Similarly, using the same approach for cooking quality prediction resulted in satisfactory performance, achieving a validation R² of 0.79. Classification models further enhanced prediction accuracy, with the Random Forest model attaining the highest accuracy of 92% for six-month interval classifications. These results underscore the potential of integrating NIR spectroscopy and machine learning for efficient, non-destructive quality assessment of Komal Chaul, supporting its commercialization as a value-added traditional food product