采用近红外漫反射光谱技术,结合偏最小二乘法(PLS),以152个来源不同的紫花苜蓿样品建立了粗蛋白和粗纤维含量的近红外定量分析校正模型.在近红外光谱范围内(950～1 650 nm)对紫花苜蓿样品采集光谱数据时,分别设置了粗磨样、细磨样两种样品的状态和1,2,5 nm三种光谱扫描间隔,对建立的模型进行准确性和重复件的验证,比较其优劣.结果显示:光谱扫描时样品为细磨样,光谱扣描间隔为2 nm时所建立的粗蛋白和粗纤维含量的校正模型最佳,其相关系数(R_(cal)~2)分别是0.97和0.94,最佳因素数时的定标标准差(SECV)分别是0.42和0.78.所建近红外定量分析模型对独立检验集样品粗蛋白和粗纤维含最的预测值与化学值的相关系数(R_(val)~2)分别为0.96和0.92,预测标准差(SEP)分别为0.43和0.79.该研究结果表明:利用近红外漫反射光谱法测定紫花苜蓿内在主要品质性状是可行的,为紫花苜蓿粗蛋白和粗纤维含量的检验提供了新的方法模式.%Near-infrared reflectance spectroscopy (NIRS) calibrations of crude protein and crude fiber in 152 alfalfa samples were developed by means of partial least-squares (PLS) regression. Different interval wavelengths of 1, 2 and S nm and two kinds of samples (crude and fine) were set to collect spectral data and establish calibration respectively. The accuracy and repeatability of calibrations were verified so as to compare superiority and inferiority. Results showed that the best validation result is the cali-bration by 2 nm interval wavelength and fine sample, and the correlation coefficients of calibration (R~2 ) were 0. 97 and 0. 94 for crude protein and crude fiber, and the SECV of these parameters were 0. 42 and 0. 78 respectively. The coefficients of determina-tion for validation (R_(val)~2) were 0. 96 and 0. 92, and the SEP of these parameters were 0. 43 and 0. 79. The experiment showed that it is feasible to evaluate the major quality traits of alfalfa by NIRS. All together, it provided a new model to verify the crude protein and fiber compositions of alfalfa.
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