Submitted to: Cereal Chemistry
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/14/2003
Publication Date: 5/1/2004
Citation: SOHN, M., BARTON II, F.E., MCCLUNG, A.M., CHAMPAGNE, E.T. NEAR INFRARED SPECTROSCOPY FOR DETERMINATION OF PROTEIN AND AMYLOSE IN RICE FLOUR THROUGH USE OF DERIVATIVES. CEREAL CHEMISTRY. VOL. 81. ISS. 3. P. 341-344. 2004. Interpretive Summary: Protein and amylose are important factors in the estimation of rice functional quality. Our goal is to develop a spectral database using rice cultivars which can be used to predict "rice quality". A sample set having a narrow range in amylose and protein content has been used for calibration in previous rice studies. The focus of this study is to develop robust calibration model that can be applied to a wider range of samples. We also investigated the effects of derivative condition as a chemomethric technique. Using a sample set with a wider range of variables, we developed robust calibration models using near infrared spectroscopy having precision of 0.23% for protein and 1.0% for amylose. These results will be used to fine suitable derivative conditions for best calibration models.
Technical Abstract: 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 3-point quadratic and 5-point quartic fits were not acceptable for the D1 model, and the 3-point quadratic fit was not acceptable for D2. For the amylose analysis, the D1 model produced generally better result 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 4 factors and an SECV of 1.0% for amylose using 10 factors.