近红外漫反射光谱法测定玉米秸秆NDF与ADF含量

应用主成分空间和傅里叶变换近红外光谱技术,采用偏最小二乘回归法(PLS),在国内首次建立了适合不同品种类型、不同生长发育时期和不同部位且适配范围广的近红外漫反射光谱(NIRS)测定玉米秸秆中性洗涤纤维(NeutralDetergentFiber,NDF)和酸性洗涤纤维(AcidDetergentFiber,ADF)含量的稳定校正模型。结果表明,采用一阶导数 矢量归一化预处理和一阶导数 多元散射校正预处理,谱区均为7502～5450cm-1和4601～4247cm-1,所建立的NDF与ADF校正模型,其校正和预测效果最佳。其校正决定系数(R2cal)均大于094,交叉验证和外部验证决定系数(R2cv,R2val)为092～096,各项误差(RMSEE,RMSECV和RMSEP)为149%～181%。该结果对青贮玉米秸秆材料快速鉴定和筛选具有重要的意义。

Prediction of NDF and ADF Concentrations with Near Infrared Reflectance Spectroscopy (NIRS)

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 (7 501 7 5 449 8 cm -1 and 4 601 3 4 246 5 cm -1 ). All these models yielded coefficients of determination of calibration ( R 2 cal ) for NDF and ADF that are higher than 0 94, while R 2 cv and R 2 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.