光学精密工程
 
     首 页 |  期刊介绍  |  编委会  |  投稿指南  |  期刊订阅  |  联系我们  |  留言板 |  English
光学精密工程 2007, 15(12) 1952-1958  ISSN: 1004-924X CN: 22-1198/TH

本期目录 | 下期目录 | 过刊浏览 | 高级检索                                                            [打印本页]   [关闭]
论文  
小型近红外玉米蛋白质成分分析 仪器设计的波段选择
曹 璞1;潘 涛1;陈星旦1,2
1. 暨南大学 理工学院 应用光谱实验室,广东 广州 510632;
2. 中国科学院 长春光学精密机械与物理研究所 应用光学国家重点实验室,吉林 长春 130033
摘要: 采用傅里叶变换近红外漫反射光谱技术和偏最小二乘法(PLS)建立了玉米蛋白质含量的定标模型。按照预测效果优选光谱波段,为设计小型近红外玉米蛋白质成分分析仪器提供依据。采用多元散射校正方法对光谱进行预处理,然后利用Savitzky-Golay平滑法对原谱、一阶导数谱和二阶导数谱进行平滑处理。选取全谱、合频、一倍频、二倍频和蛋白质基团等5个波段,每个波段分别采用原光谱、一阶导数谱、二阶导数谱,共建立15个定标模型。同时调整Savitzky-Golay平滑点数和PLS因子数,通过多次PLS数值实验比较,按照预测效果确定每个模型的最优平滑点数、因子数,再从中选优。结果表明,采用一阶导数谱的一倍频波段(7 000~5 500 cm-1)的定标效果最好,模型的预测相关系数、预测均方根偏差、相对预测均方根偏差分别为0.945,0.357,3.340%。一倍频波段可以代替全谱波段并得到更好的定标效果。
关键词 玉米   蛋白质   近红外光谱分析   偏最小二乘法   波段选择  
Choice of wave band in design of minitype near-infrared corn protein content analyzer
CAO Pu1;PAN Tao1;CHEN Xing-dan1,2
1. Laboratory of Applied Spectroscopy, College of Science and Engineering, Jinan University, Guangzhou 510632, China;
2. State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
Abstract: The calibration models for protein concentration in corn samples were established by Fourier transform near-infrared diffuse reflection spectroscopy and Partial Least Square (PLS) regression. According to the prediction effect the best wave band was chosen to provide a basis for designing a minitype near-infrared corn protein content analyzer. The spectra were processed by Multiplicative Scatter Correction(MSC) method firstly, then the original spectra, the first derivative spectra and the second derivative spectra were processed by Savitzky-Golay smoothing method. The following 5 wave bands, the whole region, combination region, the first overtone region, the second overtone region and the protein functional group bands were selected for establishing 15 calibration models adopting the original spectra, the first derivative spectra and the second derivative spectra respectively in each band. By adjusting the number of Savitzky-Golay smoothing points and number of PLS factors simultaneously and comparing with PLS computational experiments several times, the optimal number of smoothing points and number of factors for each models were obtained based on the prediction effect, and then the best one was selected. The research results show that the prediction effect using the first derivative spectra in the first overtone region is best one, and the correlation coefficient, the Root Mean Square Error of Prediction(RMSER) and the Relative Root Mean Square Error of Prediction(RRMSEP) for the corresponding model are 0.945, 0.357, and 3.340%, respectively. It shows that the first overtone band (7 000~5 500 cm-1) can replace the whole band, and get better calibration effect.
Keywords: corn   protein   near infrared spectroscopic analysis   partial least square regression   wave band choice  
收稿日期 2007-09-24 修回日期 2007-10-09 网络版发布日期 2007-12-30 
基金项目:

通讯作者: 潘 涛
作者简介:
作者Email:

参考文献:
本刊中的类似文章
1.刘燕德 周延睿 彭彦颖.基于近红外漫反射光谱检测鸡蛋品质[J]. 光学精密工程, 2013,21(1): 40-45
2.刘振尧, 潘涛.可见-近红外光谱测定血红蛋白的等效波段选择[J]. 光学精密工程, 2012,20(10): 2170-2175
3.张伟,曹移明,丛明煜,鲍文卓,孟祥龙.基于杂波模型的天基目标红外探测波段选择[J]. 光学精密工程, 2010,18(2): 341-348
4.郭志明,赵杰文,陈全胜.特征谱区筛选在近红外光谱检测茶叶游离氨基酸含量中的应用研究[J]. 光学精密工程, 2009,17(8): 1839-1844
5.万志.探测海洋目标的光学遥感器工作波段选择[J]. 光学精密工程, 2008,16(10): 1864-1868
6.张俊雄;荀 一;李 伟.基于形态特征的玉米种子表面裂纹检测方法[J]. 光学精密工程, 2007,15(6): 951-956
7.张玲.PLS定标法在近红外光谱分析仪中的应用研究[J]. 光学精密工程, 2000,8(3): 238-241

Copyright by 光学精密工程