Guo Tao, Dai Luming, Yan Baipeng, Lan Guisheng, Li Fadi, Li Fei, Pan Faming, Wang Fangbin
State Key Laboratory of Pastoral Agricultural Ecosystem, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China.
Institute of Animal & Pasture Science and Green Agriculture, Gansu Academy of Agricultural Science, Lanzhou 730070, China.
Animals (Basel). 2021 Nov 22;11(11):3328. doi: 10.3390/ani11113328.
Rapid, non-destructive methods for determining the biochemical composition of straw are crucial in ruminant diets. In this work, ground samples of corn stover ( = 156) and wheat straw ( = 135) were scanned using near-infrared spectroscopy (instrument NIRS DS2500). Samples were divided into two sets, with one set used for calibration (corn stover, = 126; wheat straw, = 108) and the remaining set used for validation (corn stover, = 30; wheat straw, = 27). Calibration models were developed utilizing modified partial least squares (MPLS) regression with internal cross validation. Concentrations of moisture, crude protein (CP), and neutral detergent fiber (NDF) were successfully predicted in corn stover, and CP and moisture were in wheat straw, but other nutritional components were not predicted accurately when using single-crop samples. All samples were then combined to form new calibration ( = 233) and validation ( = 58) sets comprised of both corn stover and wheat straw. For these combined samples, the CP, NDF, and ADF were predicted successfully; the coefficients of determination for calibration (RSQ) were 0.9625, 0.8349, and 0.8745, with ratios of prediction to deviation (RPD) of 6.872, 2.210, and 2.751, respectively. The acid detergent lignin (ADL) and moisture were classified as moderately useful, with RSQ values of 0.7939 (RPD = 2.259) and 0.8342 (RPD = 1.868), respectively. Although the prediction of hemicellulose was only useful for screening purposes (RSQ = 0.4388, RPD = 1.085), it was concluded that NIRS is a suitable technique to rapidly evaluate the nutritional value of forage crops.
快速、无损测定秸秆生化成分的方法对于反刍动物日粮至关重要。在本研究中，使用近红外光谱仪（NIRS DS2500）对玉米秸秆（n = 156）和小麦秸秆（n = 135）的粉碎样本进行扫描。样本分为两组，一组用于校准（玉米秸秆，n = 126；小麦秸秆，n = 108），其余一组用于验证（玉米秸秆，n = 30；小麦秸秆，n = 27）。利用改进的偏最小二乘法（MPLS）回归及内部交叉验证建立校准模型。成功预测了玉米秸秆中的水分、粗蛋白（CP）和中性洗涤纤维（NDF）含量，以及小麦秸秆中的CP和水分含量，但使用单一作物样本时，其他营养成分的预测并不准确。然后将所有样本合并，形成由玉米秸秆和小麦秸秆组成的新校准集（n = 233）和验证集（n = 58）。对于这些混合样本，成功预测了CP、NDF和酸性洗涤纤维（ADF）；校准决定系数（RSQ）分别为0.9625、0.8349和0.8745，预测偏差比（RPD）分别为6.872、2.210和2.751。酸性洗涤木质素（ADL）和水分被归类为中等有用，RSQ值分别为0.7939（RPD = 2.259）和0.8342（RPD = 1.868）。尽管半纤维素的预测仅适用于筛选目的（RSQ = 0.4388，RPD = 1.085），但得出结论，近红外光谱法是快速评估饲料作物营养价值的合适技术。
