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• W2308982823 abstract "When determining the protein content of brown rice (unpolished grain of rice), one usually grinds the rice grains into flour and then adopts the near infrared reflectance spectroscopy (NIRS) to construct the calibration model. However, the NIRS is easily influenced by interference among different constituents. To effectively eliminate interference in NIRS on brown rice, this study employed several data pre-treatment methods, including normalization, standard normal variate (SNV), multiplicative signal correction (MSC), and two orthogonal signal correction (OSC) approaches, which were OSC-CV and JSosc. Then, the partial least squares regression (PLSR) was used to construct a calibration model to determine the protein content of brown rice. Results showed that the PLSR model constructed by using OSC-CV to correct the NIRS of brown rice had the better statistical indicators in terms of the root mean squared error and coefficient of determination (R2) than the other pre-treatment methods. The performance of the PLSR model was similar to those constructed on the original NIRS of the brown rice flour. Moreover, the number of latent variables required in the PLSR model was less than that with the original NIRS of brown rice flour. In conclusion, the OSC-CV method was a useful tool for correction and interpretation when constructing the PLSR model for determining the protein content of brown rice with NIRS data." @default.
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• W2308982823 date "2015-03-01" @default.
• W2308982823 modified "2023-09-23" @default.
• W2308982823 title "Using Orthogonal Signal Correction to Correct the Near Infrared Reflectance Spectroscopy for Determination of Protein Content in Brown Rice" @default.
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