基于近红外光谱技术检测除草剂胁迫下油菜叶片中脯氨酸含量的方法

孙光明; 刘飞; 张帆; 金宗来; 何勇; 周伟军; 冯雷

doi:doi:10.3788/aos20103004.1192

光学学报, 2010, 30 (4): 1192, 网络出版: 2010-04-20

基于近红外光谱技术检测除草剂胁迫下油菜叶片中脯氨酸含量的方法

Determination of Proline in Herbicide-Stressed Oilseed Rape Leaves Based on Near Infrared Spectroscopy

论文大纲

孙光明 ^1,*刘飞 ¹张帆 ²金宗来 ²何勇 ¹周伟军 ²冯雷 ¹

作者单位

¹ 浙江大学生物系统工程与食品科学学院,浙江杭州 310029

² 浙江大学农业与生物技术学院,浙江杭州 310029

摘要

应用近红外光谱技术实现除草剂胁迫下油菜叶片中脯氨酸含量的检测。对248个经过除草剂丙酯草醚处理后的油菜叶片,经过烘干、磨碎后进行光谱扫描。经过Savitzky-Golay平滑、变量标准化(SNV)、二阶求导预处理后,应用偏最小二乘法(PLS)建立脯氨酸含量的预测模型,同时提取有效特征变量作为神经网络(BPNN)和最小二乘支持向量机(LS-SVM)的输入值,并建立相应的模型。用186个样本建模,62个样本预测。结果表明,最小二乘支持向量机能够获得最优的预测效果,预测的相关系数(r)、预测标准差(RMSEP)和偏差分别为0.995,0.041和0.000。说明应用近红外光谱技术结合最小二乘支持向量机能够定量获得油菜叶片中脯氨酸的含量。

Abstract

Near infrared (NIR) spectroscopy was applied for the fast determination of proline in oilseed rape leavers. The oilseed rape leaves were treated by herbicide,248 samples were collected for NIR spectral scanning within the wavelength region of 1100-2500 nm. Smoothing way of Savitzky-Golay with 7 segments,standard normal variate (SNV) and second derivative were used as preprocessing methods of spectral data before the calibration stage. Partial least squares (PLS) analysis was applied as calibration method as well as a way to extract the new eigenvectors which could be used to represent the most useful information of original spectra and compress the spectral dimensionality. The selected new eigenvectors were used as the input data matrix of back propagation neural network (BPNN) and least squares-support vector machine (LS-SVM) to develop the BPNN and LS-SVM models. The calibration set was composed of 186 samples,whereas 62 samples in the validation set. The results indicated that LS-SVM model achieved the best prediction performance,and LS-SVM model outperformed PLS and BPNN models. The correlation coefficients (r),root mean square error of prediction (RMSEP) and bias by LS-SVM model were 0.995,0.041 and 0.000,respectively. The overall results demonstrated that NIR spectroscopy combined with LS-SVM model could be successfully applied for the determination of proline in oilseed rape leaves treated by herbicide.

参考文献

[1] 张国平,周伟军. 作物栽培学[M]. 杭州:浙江大学出版社,2001

Zhang Guoping,Zhou Weijun. Crop Production[M]. Hangzhou:Zhejiang University Press,2001

[2] . Nakashima,R. Satoh,T. Kiyosue et al.. A gene encoding proline dehydrogenase is not only induced by proline and hypoosmolarity,but is also developmentally regulated in the reproductive organs of arabidopsis[J]. Plant Physiology, 2008, 118(4): 1233-1241.

[3] . TroteI-Aziz,M. F. Niogret,C. Deleu et al.. The control of proline consumption by abscisic acid during osmotic stress recovery of canola leaf discs[J]. Physiologia Plantarum, 2003, 117(2): 213.

[4] . D. Hare,W. A. Cress et al.. A regulatory role for proline metabolism in stimulating arabidopsis thaliana seed germination[J]. Plant Growth Regulation, 2003, 39(1): 41-50.

[5] . 油菜叶片保护酶活性、丙二醛及脯氨酸对铝胁迫的响应[J]. 中国粮油学报, 2008, 23(5): 107-111.

. . Response of protectiase activity,MDA and proline contends to alunimium-stress in leaves in leaves of rape seedlings[J]. J. the Chinese Cereals and Oils Association, 2008, 23(5): 107-111.

[6] . 以烟叶脯氨酸含量判断田间成熟度的研究[J]. 作物研究, 2008, 22(1): 31-33.

. Study on judgement of maturity degree by free proline content in flue-cured tobacco leaves[J]. Crop Reseach, 2008, 22(1): 31-33.

[7] . 脯氨酸测定方法的改进[J]. 植物生理学通讯, 2005, 41(3): 355-357.

. Improvement on the method for measuring proline content[J]. Plant Physiology, 2005, 41(3): 355-357.

[8] 严衍禄,赵龙莲,韩东海等. 近红外光谱分析基础与应用[M]. 北京:中国轻工业出版社,2005

Yan Yanlu,Zhao Longlian,Han Donghai et al.. The Foundation and Application of Near Infrared Spectroscopy Analysis[M]. Beijing:China Light Industry Press,2005

[9] 刘飞,何勇,王莉. 黄酒糖度预测的可见-近红外光谱方法研究[J]. 光学学报,2007,27(11):2054-2058

Liu Fei,He Yong,Wang Li. Methods for the prediction of sugar content of rice wine using visible-near infrared spectroscopy [J]. Acta Optica Sinica,2007,27(11):2054-2058

[10] 赵杰文,郭志明,陈全胜. 近红外光谱法检测绿茶中儿茶素的含量[J]. 光学学报,2008,28(12):2302-2307

Zhao Jiewen,Guo Zhiming,Chen Quansheng et al.. Feasibility study on use of near-infrared spectroscopy in quantitative analysis of catechins in green tea [J]. Acta Optica Sinica,2008,28(12):2302-2307

[11] . 应用光谱技术无损检测油菜叶片中乙酰乳酸合成酶[J]. 分析化学, 2009, 37(1): 67-71.

. . Non-destructive determination of acetolacta synthase in oilseed Rrape leaves using visible and near infrared spectroscopy[J]. Chinese J. Analytical Chemistry, 2009, 37(1): 67-71.

[12] F. Liu,F. Zhang,Z. L. Jin et al.. Determination of acetolactate synthase activity and protein content of oilseed rape (brassica napus L.) leaves using visible/near-infrared spectroscopy[J]. Analytica Chimica Acta,2008,629(1,2):56-65

[13] 芮玉奎,黄昆仑,王为民等. 近红外光谱技术在检测转基因油菜籽中芥酸和硫甙上的应用研究[J]. 光谱学与光谱分析,2006,26(12):2190-2192

Rui Yukui,Huang Kunlun,Wang Weimin et al.. Spectroscopy and Spectral Analysis,2006,26(12):2190-2192

[14] . M. Montes,C. Paul,A. E. Melchinger. Quality assessment of rapeseed accessions by means of near-infrared spectroscopy on combine harvesters[J]. Plant Breeding, 2007, 126: 329-330.

[15] 冯雷,方慧,周伟军等. 基于多光谱视觉传感技术的油菜氮含量诊断方法研究[J]. 光谱学与光谱分析,2006,26(9):1749-1752

Feng Lei,Fang Hui,Zhou Weijun et al.. Nitrogen stress measurement of canola based on multi-spectral charged coupled device imaging sensor[J]. Spectroscopy and Spectral Analysis,2006,26(9):1749-1752

[16] 方慧,宋海燕,曹芳等. 油菜叶片的光谱特征与叶绿素含量之间的关系研究[J]. 光谱学与光谱分析,2007,27(9):1731-1734

Fang Hui,Song Haiyan,Cao Fang et al.. Study on the relationship between spetral properties of oilseed rape leaves and their chlorophyll content[J]. Spectroscopy and Spectral Analysis,2007,27(9):1731-1734

[17] 吴建国,石春海,张海珍等. 构建整粒油菜籽脂肪酸成分近红外反射光谱分析模型的研究[J]. 光谱学与光谱分析,2006,26(2):259-262

Wu Jianguo,Shi Chunhai,Zhang Haizhen et al.. Study on developing calibration models of fat acid composition in intact rapeseed by near infrared reflectance spectroscopy[J]. Spectroscopy and Spectral Analysis,2006,26(2):259-262

[18] . 应用SPAD和光谱技术研究油菜生长期间的氮素变化规律[J]. 农业工程学报, 2007, 23(7): 150-154.

. . Variation rules of the nitrogen content of the oilseed rape at growth stage using SPAD and visible-NIR[J]. Transaction of the CSAE, 2007, 23(7): 150-154.

[19] V. N. Vapnik. The Nature of Statistical Learning Theory[M]. New York:Springer-Verlag,1995

[20] . 最小二乘支持向量机参数选择方法及其应用研究[J]. 系统仿真学报, 2006, 18(7): 2033-2037.

. Method for selecting parameters of least squares support vector machines and application[J]. J. System Simulation, 2006, 18(7): 2033-2037.

[21] . A. K. Suykens,J. Vanderwalle. Least squares support vector machine classifiers[J]. Neural Processing Letters, 1999, 9(3): 293-300.

孙光明, 刘飞, 张帆, 金宗来, 何勇, 周伟军, 冯雷. 基于近红外光谱技术检测除草剂胁迫下油菜叶片中脯氨酸含量的方法[J]. 光学学报, 2010, 30(4): 1192. Sun Guangming, Liu Fei, Zhang Fan, Jin Zonglai, He Yong, Zhou Weijun, Feng Lei. Determination of Proline in Herbicide-Stressed Oilseed Rape Leaves Based on Near Infrared Spectroscopy[J]. Acta Optica Sinica, 2010, 30(4): 1192.

基于近红外光谱技术检测除草剂胁迫下油菜叶片中脯氨酸含量的方法

关于本站 Cookie 的使用提示

全站搜索

基于近红外光谱技术检测除草剂胁迫下油菜叶片中脯氨酸含量的方法

相关论文

相关资讯

关于本站 Cookie 的使用提示

全站搜索