首页 | 本学科首页   官方微博 | 高级检索  
     

应用近红外光谱技术测试温室黄瓜叶片全氮含量
引用本文:Rui YK,Xin SZ,Li JH. 应用近红外光谱技术测试温室黄瓜叶片全氮含量[J]. 光谱学与光谱分析, 2011, 31(8): 2114-2116. DOI: 10.3964/j.issn.1000-0593(2011)08-2114-03
作者姓名:Rui YK  Xin SZ  Li JH
作者单位:1. 中国农业大学资源与环境学院,北京,100193
2. 中国农业大学信息与电气工程学院,北京,100193
基金项目:国家自然科学基金创新研究群体科学基金,农业公益性行业科研专项经费项目,国家“十一五”科技支撑计划重点项目
摘    要:近红外分析技术是一种新型、快速、无损的检测技术,是未来农业化学分析检测的发展方向.文章采用近红外分析技术对黄瓜叶片中全氮进行了无损测试.主要结论如下:应用黄瓜叶片的全氮含量测定值(凯氏定氮法)与近红外光谱建立模型,然后进行外部验证,验证结果为:模型的决定系数为0.406 6,相对标准差为0.155 9,校正标准差为0....

关 键 词:无损测试  黄瓜  叶片  氮素  近红外光谱

Application of NIRS to detecting total N of cucumber leaves growing in greenhouse
Rui Yu-Kui,Xin Shu-Zhen,Li Jun-Hui. Application of NIRS to detecting total N of cucumber leaves growing in greenhouse[J]. Spectroscopy and Spectral Analysis, 2011, 31(8): 2114-2116. DOI: 10.3964/j.issn.1000-0593(2011)08-2114-03
Authors:Rui Yu-Kui  Xin Shu-Zhen  Li Jun-Hui
Affiliation:College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China. ruiyukui@163.com
Abstract:Non-destructive testing, as a new, rapid, non-destructive technology, is the direction of agricultural produce testing in the future. In this study, the nitrogen content of cucumber leaves was predetermined using near infrared spectroscopy technology. The main results were as follows: The authors measured the nitrogen content in cucumber leaves with Kjeldahl method and near infrared spectroscopy, then established a model between them, and processed a external verification next. The verification results showed that the determination coefficient of the model was 0.4066, relative standard deviation is 0.1559, and calibration standard deviation is 0.72; Then the authors predicted the cucumber leaves nitrogen content with this model, and the results showed that the mean absolute percent error was 0.59, average relative error was 13.88, and correlation coefficient of the chemical values and predicted values was 0.6377. So it was proved that this model had a certain feasibility in vegetable leaves nitrogen testing.
Keywords:
本文献已被 万方数据 PubMed 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号