近红外漫反射光谱法预测紫花苜蓿草颗粒营养价值

研究旨在探讨利用紫花苜蓿草颗粒样品的近红外漫反射光谱信息,建立能够预测其营养价值的校正模型。采集22份全株草颗粒、19份茎颗粒、19份叶颗粒共60份紫花苜蓿草颗粒样品,其中建模样品45份,检验样品15份。利用傅里叶变换近红外漫反射光谱技术(FT-NIRS)采集各实验样品的近红外漫反射光谱,运用偏最小二乘法(PLS)建立了紫花苜蓿草颗粒粗蛋白(CP)、中性洗涤纤维(NDF)和酸性洗涤纤维(ADF)含量的预测模型。3个预测模型的校正模型建模效果均较好,其交叉检验相关系数(RCV)为0.96410~0.96887,交互验证的残差均方根(RMSECV)为0.80%~2.59%。用15个检验样品对模型进行外部检验,预测相关系数(r)为0.9669~0.9743,外部验证的残差均方根(RMSEP)为0.85%~2.07%。所建模型的交叉检验和外部检验RPD均大于3,表明近红外光谱分析技术可以准确地预测紫花苜蓿草颗粒的营养价值。

Research on Predicting the Nutrition Value of Pelletized Alfalfa by Near Infrared Reflectance Spectroscopy

The present research aimed to predict the qualities of pelletized alfalfa by near infrared reflectance spectroscopy.Sixty pelletized alfalfa samples were collected,including 22 whole plant alfalfa samples,19 stem samples and 19 leaf samples.They were divided into a calibration sample set(45 samples) and a validation sample set(15 samples).The Fourier transform-near infrared reflectance spectroscopy(FT-NIRS) and the partial least square(PLS) were used to calibrate models of the pelletized alfalfa nutrition value,involving crude protein(CP),neutral detergent fiber(NDF) and acid detergent fiber(ADF) contents.All models had great calibration performances.The correlation coefficients of cross-validation(RCV) were between 0.964 10 and 0.968 87,and the root mean square errors of cross-validation(RMSECV) were between 0.80% and 2.59%.Fifteen validation samples were used to predict the performances of these models,all the correlation coefficients of NIRS value and chemical value(r) were between 0.966 9 and 0.974 3,and the root mean square errors of prediction(RMSEP) were between 0.85% and 2.07%.The RPD values of cross-validation and prediction were all above 3.The results showed that pelletized alfalfa's CP,NDF,ADF contents were exactly predicted by near infrared reflectance spectroscopy.