Physical and Physicochemical Classification of Parboiled Rice Using VNIR-SWIR Spectroscopy and Machine Learning

doi:10.1016/j.rsci.2025.08.007

摘要/Abstract

. [J]. Rice Science, 2025, 32(6): 857-867.

图/表 7

Fig. 1. Hyperspectral curve from Types 1-5 and Off-type of parboiled rice. A, Visible region (350-750 nm). B, Near-infrared region (750-1300 nm). C, Shortwave-infrared region (1300-1500 nm).

Fig. 2. Reflectance spectra of rice samples after preprocessing steps. A, Raw reflectance spectra; B, Spectra after baseline correction; C, Standard normal variate (SNV) transformation; D, Multiplicative scatter correction (MSC); E, Combined preprocessing (SNV and MSC) using Savitzky-Golay smoothing; F, First derivative spectra after Savitzky-Golay smoothing. These transformations progressively reduce noise and scattering effects, enhancing spectral differences among rice types.

Fig. 3. Principal component analysis (PCA) for parboiled rice samples. A, Score plots for the first three principal components of all parboiled rice sample; B, PCA loading plot of spectral data without preprocessing.

Fig. 4. Boxplots showing distribution of performance metrics across different machine learning algorithms. A, Correlation coefficient (CC). B, F-score. C, Kappa between machine learning algorithms. DT, Decision Tree; J48, J48 Decision Tree algorithm; RF, Random Forest; ANN, Artificial Neural Network; LR, Logistic Regression; SVM, Support Vector Machine. Different lowercase letters above bars represent significant differences at the 0.05 level by the Scott-Knott test.

Fig. 5. Confusion matrices showing classification accuracy for parboiled rice types using Support Vector Machine (SVM, A) and Logistic Regression algorithms (B). Data show the percentage (%) of correct classifications obtained for each type.

Table 1. Correct classification rates among kernel functions and preprocessing methods obtained for training set (450 samples) and internal validation using Support Vector Machine algorithm.

Preprocessing	Linear kernel		Radial kernel		Polynomial kernel
Preprocessing	Training	Internal validation	Training	Internal validation	Training	Internal validation
No preprocessing	1.000	0.971	1.000	0.895	1.000	0.964
Baseline offset correction (BL)	1.000	0.980	1.000	0.915	0.997	0.973
Standard normal variate (SNV)	1.000	0.982	1.000	0.933	1.000	0.982
Multiplicative scatter correction	1.000	0.977	1.000	0.931	1.000	0.975
SNV + BL + Savitzky-Golay smoothing	1.000	0.975	1.000	0.928	1.000	0.977
First derivative of Savitzky-Golay	1.000	0.993	1.000	0.960	1.000	0.991

Table 2. Correct classification rate among kernel functions and preprocessing methods obtained for external validation set (149 samples) using Support Vector Machine algorithm.

Preprocessing	Kernel function
Preprocessing	Linear	Radial	Polynomial
No preprocessing	0.993	0.959	0.986
Baseline offset correction (BL)	0.986	0.953	0.986
Standard normal variate (SNV)	0.986	0.973	0.979
Multiplicative scatter correction	0.986	0.979	0.986
SNV + BL + Savitzky-Golay smoothing	0.986	0.986	0.979
First derivative of Savitzky-Golay	0.228	0.174	0.174

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