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利用近红外反射光谱法(NIRS)预测中性洗涤纤维(NDF)和酸性洗涤纤维(ADF)浓度

[Prediction of NDF and ADF concentrations with near infrared reflectance spectroscopy (NIRS)].

作者信息

Bai Qi-lin, Chen Shao-jiang, Dong Xiao-ling, Meng Qing-xiang, Yan Yan-lu, Dai Jing-rui

机构信息

National Maize Improvement Center of China, China Agricultural University, Beijing 100094, China.

出版信息

Guang Pu Xue Yu Guang Pu Fen Xi. 2004 Nov;24(11):1345-7.

Abstract

The NDF (Neutral Detergent Fiber) and ADF (Acid Detergent Fiber) concentrations in maize stalk were analyzed with 147 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 (PLS) regression were used to establish the models. The results showed that the calibration models developed by the spectral data pretreatment of the first derivative + vector normalization, and the first derivative + multivariate scattering correction were the best for NDF and ADF with the same spectral regions (7501.7-5449.8 cm(-1) and 4601.3-4246.5 cm(-1)). All these models yielded coefficients of determination of calibration (R2(cal)) for NDF and ADF that are higher than 0.94, while R2(cv) and R2(val) ranged from 0.92 to 0.96 for cross and external validation. The root mean square error of estimation, root mean square error of cross validation, and root mean square error of prediction (RMSEE, RMSECV and RMSEP) for NDF and ADF ranged from 1.49% to 1.81%. The models can be used to measure various samples in screening and evaluating quality constituents of silage maize.

摘要

从600个来自不同生态环境、杂交种和自交系、发育阶段以及植株不同部位的样本中选取147个样本,对两年内玉米秸秆中的中性洗涤纤维(NDF)和酸性洗涤纤维(ADF)含量进行了分析。采用近红外反射光谱(NIRS)技术和偏最小二乘(PLS)回归建立模型。结果表明,在相同光谱区域(7501.7 - 5449.8 cm(-1)和4601.3 - 4246.5 cm(-1)),经一阶导数 + 向量归一化以及一阶导数 + 多元散射校正的光谱数据预处理所建立的校准模型对NDF和ADF效果最佳。所有这些模型的NDF和ADF校准决定系数(R2(cal))均高于0.94,而交叉验证和外部验证的R2(cv)和R2(val)在0.92至0.96之间。NDF和ADF的估计均方根误差、交叉验证均方根误差和预测均方根误差(RMSEE、RMSECV和RMSEP)在1.49%至1.81%之间。这些模型可用于测量青贮玉米筛选和评估质量成分中的各种样本。

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