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Using 128 bulk-kernel samples of inbred lines and hybrids, a study was conducted toinvestigate the feasibility and method of measuring protein and starch contents inintact seeds of maize by near infrared reflectance spectroscopy (NIRS). The chemometricalgorithms of partial least square (PLS) regression was used. The results indicated thatthe calibration models developed by the spectral data pretreatment of firstderivative+multivariate scattering correction within the spectral region of 10000-4000cm-1, and first derivative + straight line subtraction in 9000-4000cm-1 were thebest for protein and starch, respectively. All these models yielded coefficients ofdetermination of calibration (R2cal) above 0.97, while R2cv and R2val of cross and externalvalidation ranged from 0.92 to 0.95, respectively; however, the root of mean squareerrors of calibration, cross and external validation (RMSEE, RMSECV and RMSEP) werebelow 1(ranged 0.3-0.7),respectively. This study demonstrated that it is feasible touse NIRS as a rapid, accurate, and none-destructive technique to predict protein andstarch contents of whole kernel in the maize quality improvement program.
Using 128 bulk-kernel samples of inbred lines and hybrids, a study was conducted to investigate the feasibility and method of measuring protein and starch contents in intact seeds of maize by near infrared reflectance spectroscopy (NIRS). The chemometrical algorithms of partial least square (PLS) regression was used. The results indicated that the calibration models developed by the spectral data pretreatment of first derivative + multivariate scattering correction within the spectral region of 10000-4000 cm-1, and first derivative + straight line subtraction in 9000-4000 cm- 1 were the same for protein and Both of these models yielded coefficients of determination of calibration (R2cal) above 0.97, while R2cv and R2val of cross and externalvalidation ranged from 0.92 to 0.95, respectively; however, the root of mean squareerrors of calibration, cross and external validation (RMSEE , RMSECV and RMSEP) werebelow 1 (ranged 0.3-0.7), respectively. This study demonstrates that it is feasib le touse NIRS as a rapid, accurate, and none-destructive technique to predict protein and starch contents of whole kernel in the maize quality improvement program.