Near infrared transmittance spectrophotometer: predictor of rice physicochemical properties
2005
Garcia, G.DG. | Corpuz, H.M. | Bulatao, R.M. | Aquino, D.V. | Mamucod, H.F. | Escubio, S.S.P. | Manaois, R.V. | Valdez, R.E. | Tibayan, P.A. | Alvaran, S.E. | Bandonill, E.H. (Philippine Rice Research Inst. Maligaya, Science City of Munoz, Nueva Ecija (Philippines). Rice Chemistry and Food Science Div.)
A fast, non-destructive and accurate method of quantifying physicochemical properties of rice was developed. Foss Infratec 1241 Near Infrared Transmittance (NIRT) Spectrophotometer was utilized in the development of calibration model for moisture, crude protein and amylose content of brown and milled rice. The spectral data of 2,988 milled rice and 2298 brown rice sample of NCT and non-NCT trials from 2002 to 2004 dry and wet-season harvest were collected. The reference value for moisture, protein and amylose of the samples were analyzed using conventional methods. The calibration models were established using partial least squares calculation. The computed coefficient of determination (r square) and standard error of prediction (SEP) of the validation set for milled rice were r square = 0.341 and SEP = 0.346 for moisture content, r square = 0.853 and SEP = 1.196 for amylose content and r square = 0.969 and SEP = 0.159 for protein content. SEP obtained for brown rice were, r square = 0.574 and SEP = 0.384 for moisture content and r square = 0.957 and SEP = 0.548 for amylose content and r = 0.966 and SEP = 0.167 for protein content. The results of the validation showed that NIRT could be employed to rapidly measure the protein, moisture and amylose content of brown and milled rice, while reducing the time of physicochemical evaluation and eliminating the laborious conventional method. However, repeatability and reproductibility-tests (pilot testing) should be performed to further assess the calibration function and performance of the developed models. Continuous updating of models is also recommended to expand the range of constituents values and enhance their prediction accuracy.
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