Prediction of IVDMD with Near Infrared Reflectance Spectroscopy (NIRS) in Maize Stalk
BAI Qi-lin1,4, CHEN Shao-jiang1*, DONG Xiao-ling2, MENG Qing-xiang2, YAN Yan-lu3, DAI Jing-rui1
1. National Maize Improvement Center of China, China Agricultural University, Beijing 100094, China 2. College of Animal Science and Technology, China Agricultural University, Beijing 100094, China 3. College of Information and Electrical Engineering, China Agricultural University, Beijing 100094, China 4. Grop Genetics Research Institute, Shanxi Academy of Agricultural Science, Taiyuan 030031, China
Abstract:The in vitro dry matter digestion (IVDMD) in maize stalk was analyzed with 161 samples selected from 600 samples of different eco-environments, hybrids and inbred lines, development stages, and various parts of the plants in two years. The technique of near infrared reflectance spectroscopy (NIRS) and partial least square regression (PLS) were used to establish the models by comparing several preprocessing procedures and wavelength ranges. The optimal models could be obtained in the range of 6 101.7-5 773.8 cm-1 and 4 601.3-4 246.5 cm-1 by the spectral data preprocessing of the Max-Min normalization. The model is suitable for measuring various sample IVDMD. The determination coefficients of the modes were 0.907 3 and 0.906 6 for cross and external validation, respectively. The root mean square error of prediction was 2.08%, and the coefficient of correlation(r) was 0.956 between NIRS predicted and actual IVDMD in these materials. The results showed that NIRS is a simple effective means for measuring IVDMD in maize stalk. The results are of great value of application in screening and evaluating quality constituents of silage maize.
白琪林1,4,陈绍江1*,董晓玲2,孟庆祥2,严衍禄3,戴景瑞1 . 近红外漫反射光谱法测定玉米秸秆体外干物质消化率[J]. 光谱学与光谱分析, 2006, 26(02): 271-274.
BAI Qi-lin1,4, CHEN Shao-jiang1*, DONG Xiao-ling2, MENG Qing-xiang2, YAN Yan-lu3, DAI Jing-rui1 . Prediction of IVDMD with Near Infrared Reflectance Spectroscopy (NIRS) in Maize Stalk . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2006, 26(02): 271-274.
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