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Near-Infrared Spectroscopy for Determination of Protein and Amylose in Rice Flour Through Use of Derivatives

¹USDA-Agricultural Research Service, Richard B. Russell Agricultural Research Center, Athens, GA 30605. ²Corresponding author. E-mail: msohn@qaru.ars.usda.gov ³USDA-Agricultural Research Service, Rice Research Unit, Beaumont, TX 77713. ⁴USDA-Agricultural Research Service, Southern Regional Research Center, New Orleans, LA 70179.

The use of the derivative method for near-infrared (NIR) calibration was investigated to determine protein and amylose content in rice flour. Samples for two years, 1996 and 1999, were combined to give a wide range of the constituents for development of the calibration model. The NIR spectral data were transformed with Savitzky-Golay derivative with multiplicative scatter correction. To develop the best derivative models, the polynomial fits (quadratic, cubic, and quartic), convolution intervals (3–11 points for protein, 3–17 points for amylose), and derivative orders (1st derivative D1; 2nd derivative D2) were investigated. For the protein analysis, all polynomial fits with 3–11 points were acceptable to develop both the D1 and D2 models. However, the three-point quadratic and five-point quartic fits were not acceptable for the D1 model, and the three-point quadratic fit was not acceptable for D2. For the amylose analysis, the D1 model produced generally better results than D2. Higher convolution intervals were required for the D2 model, whereas the D1 model was not affected by convolution intervals. A quadratic (or cubic) fit with 17-point convolution interval was acceptable for the amylose D2 model, and the quadratic fit with 5–11 points and cubic (or quartic) fit with 7–17 points were suitable for the D1 model. Based on the standard error of cross-validation (SECV), the calibration models developed using data for two years resulted in good precision with an SECV of 0.23% for protein using four factors and an SECV of 1.0% for amylose using 10 factors.