近红外光谱法测定玉米秸秆饲用品质

为了对玉米秸秆的饲用品质进行可靠、便捷、快速的分析和评价，该研究以不同品种、密度、氮肥和水分处理的不同发育时期和不同部位玉米秸秆为试验材料，应用近红外光谱（NIRS）技术和偏最小二乘法（PLS），采用一阶导数+中心化+多元散射校正的光谱数据预处理方法，构建了玉米秸秆体外干物质消化率（IVDMD）、酸性洗涤纤维（ADF）、中性洗涤纤维（NDF） 和可溶性糖（WSC）含量的NIRS分析模型。所建立的IVDMD、ADF、NDF和WSC含量的NIRS校正模型决定系数（R2cal）分别为0.9906、0.9870、0.9931和0.9802，交叉验证决定系数（R2cv）分别为0.9593、0.9413 、0.9678和0.9342，外部验证决定系数（R2val）分别为0.9549、0.9353、0.9519和0.9191，各项标准差（SEC、SECV和SEP）为0.935～1.904，相对分析误差（RPD）均大于3。结果表明，各参数的NIRS分析模型可用于玉米秸秆饲用品质的分析和品种选育的快速鉴定。

Prediction of forage quality of maize stover by near infrared spectroscopy

In order to reliably, conveniently and rapidly analyze and evaluate forage quality of maize stover, the samples of maize stover from different varieties and treatments of density, nitrogenous fertilizer and water were used to establish near infrared reflectance spectroscopy (NIRS) calibration models of in vitro dry matter digestion (IVDMD), acid detergent fiber (ADF), neutral detergent fiber (NDF) and water soluble carbohydrate (WSC) of maize stover with near infrared reflectance spectroscopy (NIRS) technique, partial least square regression (PLS) and data pretreatment of 1st derivative+mean center+Multiple scatter correction. The results showed that determination coefficients of calibration (R2cal) about those models were 0.9906, 0.9870, 0.9931 and 0.9802 and those of cross validation (R2cv) and validation (R2val) were 0.9593(0.9549), 0.9413(0.9353), 0.9678(0.9519) and 0.9342(0.9191) for IVDMD, ADF, NDF and WSC, respectively. Standard error of calibration, cross validation and prediction (SEC, SECV and SEP) ranged from 0.935 to 1.904. All values of relative percent differences (RPD) were greater than three. It demonstrated that these calibration models could be used to rapidly and accurately predict forage quality of maize stover and screen various samples in maize breeding.