动物营养学报2019，Vol.31Issue(1)：452-458,7.DOI:10.3969/j.issn.1006-267x.2019.01.053
花生秧常规营养成分近红外反射光谱预测模型的建立
Prediction Model Establishment for Routine Nutrient of Peanut Vine by Near-Infrared Reflectance Spectroscopy
摘要
Abstract
In order to explore the application feasibility of prediction of routine nutrient of peanut vine by nearinfrared reflectance spectroscopy (NIRS) technology, a total of 107 peanut vine samples in Henan province were collected, the samples were divided into calibration set (n=83) and verification set (n=24) , the 8 kinds of routine nutrient contents in peanut vine were analyzed included:dry matter (DM) , ether extract (EE) , crude protein (CP) , acid detergent fiber (ADF) , neutral detergent fiber (NDF) , crude ash (Ash) , calcium (Ca) and phosphorus (P) , and calculated the correlation between predicted value and measured value of verification set, verified the accuracy of the model.The results showed that after pre-treatment of standard normal variate (SNV) + 1st derivative, the predictive effect of DM content in peanut vine was the best [calibration correlation coefficient (RSQcal) =0.989, coefficient of determination (RSQv) =0.968 2];after pre-treatment of SNV+detrended correction + 1st derivative, the predictive effect of NDF content was the best (RSQcal=0.966, RSQv=0.937 3) ;after pre-treatment of SNV + detrended correction + 2nd derivative, the predictive effect of CP content was the best (RSQcal=0.923, RSQv=0.903 6) ;after different pre-treatment, the RSQcal of ADF and EE contents were> 0.7, and 0.9>RSQv> 0.7;and the RSQcal of Ca, P and Ash contents were<0.7, RSQv<0.7, the predictive effect was not satisfactory.It is concluded that the contents of DM, NDF and CP in peanut vine can be accurately predicted by NIRS technology, the contents of EE and ADF can be predicted roughly, while the contents of Ca, P and Ash cannot be predicted.关键词
近红外反射光谱/花生秧/常规营养成分Key words
NIRS/peanut vine/routine nutrient分类
农业科技引用本文复制引用
冯豆,蔡阿敏,薛宵,栗敏杰,李改英,付彤,高腾云..花生秧常规营养成分近红外反射光谱预测模型的建立[J].动物营养学报,2019,31(1):452-458,7.基金项目
现代奶牛产业技术体系建设专项资金 (CARS-36) （CARS-36）
2016年河南省畜牧业专项补助资金 (奶牛业发展 (2015) 236号) （奶牛业发展 (2015）
