摘要
利用近红外光纤分析技术检测饲料中粗纤维的含量,采用偏最小二乘回归(PLS)方法,分别对光谱进行附加散射校正、变量标准化、一阶导数和平滑处理,建立了饲料中粗纤维含量的预测模型。其中附加散射校正、一阶导数和9点平滑处理定标效果最优。定标集化学分析值与预测值之间的决定系数(Rc2)和标准分析误差(SEC)分别为0.9698和0.1131%,相对分析误差为6.01;验证集化学分析值与预测值之间的决定系数(Rv2)和标准分析误差(SEP)分别为0.9402和0.1536%,相对分析误差为4.04。结果表明,利用近红外光纤分析技术可以比较准确地定量检测饲料中粗纤维的含量。
A prediction model was built using the technology of Near Infrared Spectroscopy （NIRS） with fiber optic probe and method of partial least squares regression （PLS ）. Multiplicative Signal Correction （MSC ） , Standard Normal Variate,First Derivative and Smoothing were used to pretreat the spectra. The best effect,with a correlation coefficient of 0.9698 were achieved by the petreatments of MSC full,First Derivative BCAP and smooth average 9 points,a standard analytical error of calibration（SEC） of 0.1131% and a RPD of 6.01 for the calibration sample set and a correlation coefficient of 0.9402,a standard analytical error of prediction（SEP） of 0.1536% and a RPD of 4.04 for the validation sample set. The result showed that the technology of Near Infrared Spectroscopy（NIRS） with fiber optic probe could be used to protect crud fiber content in forage.
出处
《食品工业科技》
CAS
CSCD
北大核心
2013年第2期75-77,82,共4页
Science and Technology of Food Industry
基金
国家质检总局科技计划项目(2011IK213)
关键词
近红外光纤
粗纤维
检测
饲料
Near Infrared Spectroscopy（NIRS）
crud fiber
detection
forage
