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西班牙西北部青贮牧草的近红外反射光谱分析

Analysis of grass silage from Northwestern Spain by near-infrared reflectance spectroscopy.

作者信息

Villamarín Begoña, Fernández Esperanza, Mendéz Jests

机构信息

Coren, S.C.L., Juan XXIII, Ourense, Spain.

出版信息

J AOAC Int. 2002 May-Jun;85(3):541-5.

Abstract

Near-infrared reflectance spectroscopy (NIRS) was evaluated for the determination of protein, crude fiber (CF), acid detergent fiber (ADF), and neutral detergent fiber (NDF) in grass silage. Calibration equations were based on analyses of 366 samples of grass silage produced in Northwestern Spain over 4 consecutive years (1992-1995) and validated by analyses of a set of 72 silage samples harvested during 1996. Dried and ground samples were analyzed by chemical and NIRS procedures. The spectral data were analyzed by regression against a range of chemical parameters, using modified partial least-squares (MPLS) multivariate analysis in conjunction with different mathematical treatments of the spectra. For each parameter, the optimum calibration was evaluated on the basis of the coefficient of multiple determination (R2), the coefficient of simple correlation (r2), the standard error of calibration (SEC), the standard error of cross-validation (SECV), and the standard error of validation (SEV). R2 and r2 were >0.90; SEC values were 0.58, 1.04, 1.40, and 1.75; SECV values were 0.64,1.15,1.50, and 2.04; and SEV values were 0.56,1.02, 1.42, and 1.80 for protein, CF, ADF, and NDF, respectively. The ratio of the standard deviation of the reference data to the SEV was >3.0 for each of the 4 parameters, which indicates that the equations can be used in routine analysis.

摘要

对近红外反射光谱法(NIRS)进行了评估,以测定青贮牧草中的蛋白质、粗纤维(CF)、酸性洗涤纤维(ADF)和中性洗涤纤维(NDF)。校准方程基于对西班牙西北部连续4年(1992 - 1995年)生产的366份青贮牧草样品的分析,并通过对1996年收获的一组72份青贮饲料样品的分析进行验证。对干燥和研磨后的样品采用化学和NIRS程序进行分析。利用改进的偏最小二乘法(MPLS)多变量分析结合光谱的不同数学处理方法,针对一系列化学参数对光谱数据进行回归分析。对于每个参数,基于多重决定系数(R2)、简单相关系数(r2)、校准标准误差(SEC)、交叉验证标准误差(SECV)和验证标准误差(SEV)评估最佳校准。蛋白质、CF、ADF和NDF的R2和r2均>0.90;SEC值分别为0.58、1.04、1.40和1.75;SECV值分别为0.64、1.15、1.50和2.04;SEV值分别为0.56、1.02、1.42和1.80。这4个参数各自的参考数据标准偏差与SEV之比均>3.0,这表明这些方程可用于常规分析。

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