Rapid Physicochemical Screening of Parboiled Rice Using VNIR-SWIR Spectroscopy and Chemometrics

Efficient quality assessment of parboiled rice is important for industrial processing, but conventional analytical procedures are often time-consuming, reagent-intensive, and destructive. This study evaluated VNIR-SWIR spectroscopy combined with chemometric modeling for the rapid prediction of moisture, starch, protein, lipids, fiber, and ash in parboiled rice representing commercial types from Type 1 to Type 5 and Off-Type. Partial least squares regression models were developed using six spectral preprocessing strategies and evaluated through internal cross-validation and a held-out test set. Model performance varied substantially among the physicochemical attributes. Starch showed the best test-set performance using baseline offset preprocessing (R² = 0.740; RMSEP = 0.902 percentage points), while moisture showed promising results using MSC, baseline offset, or untreated spectra (R² = 0.627; RMSEP < 0.37 percentage points). Ash showed moderate performance with SNV combined with baseline correction and Savitzky-Golay smoothing (R² = 0.492; RMSEP = 0.045 percentage points). In contrast, protein and fiber showed limited predictability, with maximum R² values of 0.112 and 0.248, respectively. SNV and MSC generally improved models affected by scattering, whereas baseline offset was most effective for starch. Overall, VNIR-SWIR spectroscopy showed potential for rapid screening, particularly for starch and moisture, although broader datasets and validation using independent production batches are required before routine industrial implementation.