What question did this study set out to answer?

The study aims to improve the classification of rice quality during storage and processing using modern techniques.

February 5, 2026Open Access

Classification of the Physicochemical Quality of White, Parboiled, Black and Red Rice in Storage and Processing Units Integrating Near-infrared Spectroscopy, Hyperspectral Sensing, and Machine Learning Models

Key Points

The study aims to improve the classification of rice quality during storage and processing using modern techniques.
Utilized near-infrared spectroscopy (NIRS) and hyperspectral sensing for data collection.
Analyzed spectral data in specific wavelength ranges linked to rice composition.
Classified samples according to regulatory standards and created sub-samples for analysis.
Employed machine learning algorithms, including J48, SL, RF, and SVM, for data interpretation.
Identified distinct nutritional and physicochemical profiles for white, parboiled, black, and red rice.
Achieved high accuracy in rice classification across multiple evaluation metrics.
Principal component analysis effectively differentiated compositional characteristics among rice types.
Pigmented rice types displayed higher nutritional value than non-pigmented types.

Abstract

Abstract Purpose Grain quality is typically assessed during the reception and shipment stages at storage and processing units through physical classification. However, traditional methods based on visual inspection are often subjective, imprecise, and time-consuming. This study aimed to evaluate the use of near-infrared spectroscopy (NIRS) and hyperspectral sensors combined with machine learning algorithms to determine the physicochemical properties and classify the quality of white, parboiled, black, and red rice. Methods and Results Spectral data were acquired using a FieldSpec 4 Jr spectroradiometer covering the 350–2500-nm range, with analysis focused on the 350–750-nm and 1400–2100-nm intervals. These spectral windows correspond to the most informative absorption features associated with O–H, C–H, and N–H bonds, directly related to the moisture, protein, starch, and lipid contents of rice grains. Samples were classified according to regulatory standards, and representative samples were subdivided into 100 sub-samples of 20 g each. Distinct nutritional and physicochemical profiles were observed among rice types. Principal component analysis (PCA) effectively discriminated compositional characteristics, emphasizing the higher nutritional value of pigmented rice. Hyperspectral signatures revealed distinct spectral differences among rice types based on their physicochemical composition. The combination of NIRS, hyperspectral sensors, and machine learning algorithms achieved high accuracy across all evaluation metrics, with the J48, SL, RF, and SVM models delivering the best performance in rice quality classification. Conclusion The integration of NIR spectroscopy, hyperspectral sensing, and machine learning models provides a rapid, non-destructive, and highly accurate approach for assessing the physicochemical quality of rice in storage and processing facilities. This methodology demonstrates strong potential for enhancing efficiency, reproducibility, and objectivity in grain quality monitoring, offering a data-driven alternative to traditional visual inspection methods. Graphical Abstract

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