近红外漫反射光谱结合CARS-PLS规模化奶牛场粪便总氮定量分析模型的建立

Establishment of quantitative analysis model for total nitrogen in feces of large-scale dairy farm using near infrared diffuse reflectance spectroscopy combined with CARS-PLS

将近红外漫反射光谱技术与CARS-PLS相结合,建立一种规模化奶牛场粪污治理过程中全环节粪便中总氮含量的快速检测方法。采集111个粪污治理全过程环节粪便样品的近红外漫反射光谱,利用间隔偏最小二乘法(iPLS)、联合偏最小二乘法(siPLS)和竞争自适应重加权抽样法(CARS)进行建模变量选择。全波长建模的相关系数(R)为0.928,预测均方根误差(RMSEP)为0.1613%;iPLS变量选择后的相关系数为0.926,RMSEP为0.1514%;siPLS变量选择后的相关系数为0.928,RMSEP为0.1491%;CARS变量选择后的相关系数为0.981,RMSEP为0.0841%。上述结果表明:通过变量选择可以良好地提升模型的预测精度,而3种变量选择方法中通过CARS法进行建模变量的选择可以更大地降低预测误差,提高预测精度。该研究为开发现场便捷式近红外光谱仪器提供了理论基础。

A rapid method for the determination of total nitrogen in feces of large-scale dairy farms was established combining near infrared diffuse reflectance spectroscopy with CARS-PLS.Near infrared diffuse reflectance spectra of 111 feces samples were collected from the whole process of wastewater treatment.Modeling variables were selected by using interval partial least squares(iPLS),synergy intervalpartial least squares(siPLS)and competitive adaptive weighted sampling(CARS).For full-wavelength modeling,the correlation coefficient(R)and root mean square error of prediction(RMSEP)were 0.928 and 0.1613%,respectively.For iPLS modeling,the R and RMSEP were 0.926 and 0.1514%,respectively.For siPLS modeling,the R and RMSEP were 0.928 and 0.1491%,respectively.For CARS model,the R and RMSEP were 0.981 and 0.0841%,respectively.The results showed that the prediction accuracy of the model couldbe improved by variable selection,and the prediction error could be reduced and the prediction accuracy could be improved by using CARS method.This study provides a theoretical basis for the development of field convenient near infrared spectrometer.