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  • Title: [Predicting the chemical composition of intact kernels in maize hybrids by near infrared reflectance spectroscopy].
    Author: Wei LM, Jiang HY, Li JH, Yan YL, Dai JR.
    Journal: Guang Pu Xue Yu Guang Pu Fen Xi; 2005 Sep; 25(9):1404-7. PubMed ID: 16379276.
    Abstract:
    Intact-kernel samples of normal maize inbred lines and hybrids were collected from field experiments of three locations. Calibration equations were developed by partial least square regression (PLS) of chemical values of near infrared reflectance spectroscopy (NIRS) data and tested through both cross and external validation. In addition, 40 progenies of F1 and F2 generation not included in calibration and validation sets were verified to further evaluate the reliability of three calibration equations. The authors found the coefficients of correlation (r) of 0.98, 0.93 and 0.97 between NIRS predicted and actual protein, starch and oil content in these materials, respectively. However, the greatest relative errors were 2.7% (protein), 2.46% (starch) and 7% (oil). Thus, the accuracy of prediction could be comparable to chemical methods. The feasibility of developing NIRS equations with samples of inbred lines to determine grain quality of hybrids was also examined. The analysis of principal components of spectrum of the inbred lines and hybrids supported a new theory that plant spectrum properties could be heritable.
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