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 ( R ² = 0.740; RMSEP = 0.902 percentage points), while moisture showed promising results using MSC, baseline offset, or untreated spectra ( R ² = 0.627; RMSEP < 0.37 percentage points). Ash showed moderate performance with SNV combined with baseline correction and Savitzky–Golay smoothing ( R ² = 0.492; RMSEP = 0.045 percentage points). In contrast, protein and fiber showed limited predictability, with maximum R ² 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.