天津农学院学报 ›› 2020, Vol. 27 ›› Issue (1): 82-86.doi: 10.19640/j.cnki.jtau.2020.01.018

• 研究与简报 • 上一篇    下一篇

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

王鹏1, 赵润2, 孟祥辉1, 付学周1, 辛悦1, 宁天阳1, 赵文雅1, 杨仁杰1,通信作者   

  1. 1. 天津农学院 工程技术学院,天津300384;
    2. 农业农村部 环境保护科研监测所,天津300191
  • 收稿日期:2019-08-02 发布日期:2020-04-01
  • 通讯作者: 杨仁杰(1978-),男,教授,博士,主要从事光谱检测技术研究。E-mail:rjyang1978@163.com。
  • 作者简介:王鹏(1994-),男,硕士在读,研究方向为农业生物环境与能源工程。E-mail:wpppeng@163.com。
  • 基金资助:
    国家重点研发计划(2018YFD0800100); 天津市现代奶牛产业技术体系创新团队建设专项(ITTCRS2017006); 国家自然科学基金(41771357,21607114,81471698); 天津市自然科学基金(18JCYBJC96400,16JCQNJC08200)

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

WANG Peng1, ZHAO Run2, MENG Xiang-hui1, FU Xue-zhou1, XIN Yue1, NING Tian-yang1, ZHAO Wen-ya1, YANG Ren-jie1,Corresponding Author   

  1. 1. College of Engineering and Technology, Tianjin Agricultural University, Tianjin 300384, China;
    2. Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China
  • Received:2019-08-02 Published:2020-04-01

摘要: 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0.161 3%;iPLS变量选择后的相关系数为0.926,RMSEP为0.151 4%;siPLS变量选择后的相关系数为0.928,RMSEP为0.149 1%;CARS变量选择后的相关系数为0.981,RMSEP为0.084 1%。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。

关键词: 近红外漫反射光谱, 奶牛粪便, 总氮, 偏最小二乘, iPLS, siPLS, CARS

Abstract: 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 0.161 3%, respectively. For iPLS modeling, the R and RMSEP were 0.926 and 0.151 4%, respectively. For siPLS modeling, the R and RMSEP were 0.928 and 0.149 1%, respectively. For CARS model, the R and RMSEP were 0.981 and 0.084 1%, 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.

Key words: near infrared reflectance spectroscopy, dairy cow manure, total nitrogen(TN), partial least squares, interval partial least square, synergy interval partial least square, CARS

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