基于显微拉曼检测蛋白核小球藻鉴别丁草胺及草甘膦

doi:10.7503/cjcu20140938

高等学校化学学报 ›› 2015, Vol. 36 ›› Issue (6): 1082.doi: 10.7503/cjcu20140938

基于显微拉曼检测蛋白核小球藻鉴别丁草胺及草甘膦

邵咏妮, 蒋林军, 潘健, 何勇()

浙江大学生物系统工程与食品科学学院, 杭州 310058

收稿日期:2014-10-23 出版日期:2015-06-10 发布日期:2015-05-22
作者简介:联系人简介: 何勇, 男, 博士, 教授, 博士生导师, 主要从事数字农业与农业物联网等方面的研究. E-mail:yhe@zju.edu.cn
基金资助:
国家自然科学基金(批准号: 31402318)、高等学校博士学科点专项科研基金(批准号: 20130101120149)和浙江省教育厅科研项目(批准号: Y201327409)资助

dentification of Glyphosate and Butachlor by Detecting Chlorella Pyrenoidosa with Raman Microspectroscopy^†

SHAO Yongni, JIANG Linjun, PAN Jian, HE Yong^*()

College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China

Received:2014-10-23 Online:2015-06-10 Published:2015-05-22
Contact: HE Yong E-mail:yhe@zju.edu.cn
Supported by:
† Supported by the National Natural Science Foundation of China(No.31402318), the Higher Education Research Fund for the Doctoral Program of the New Teacher Project, China(No.20130101120149) and the Research Project of Education Department of Zhejiang Province, China(No.Y201327409)

摘要/Abstract

摘要：

以蛋白核小球藻(Chlorella pyrenoidosa)作为鉴别载体, 利用共聚焦显微拉曼光谱仪分别获取生长在除草剂草甘膦、丁草胺污染水体以及正常水体的蛋白核小球藻β-胡萝卜素的拉曼光谱信息, 对2种除草剂进行了鉴别. 利用预处理后的光谱信号, 建立偏最小二乘回归(PLS)预测模型及线性判别分析(LDA)分类模型. 当阈值为±0.3时, 全波段建立的PLS模型对草甘膦和丁草胺的预测正确率高达83.33%, 特征峰建立的LDA分类模型对2种除草剂的分类正确率均达到了100%. 结果表明, 利用蛋白核小球藻为载体对丁草胺和草甘膦2种除草剂进行鉴别是可行的, 且LDA分类模型更适合除草剂的分类研究.

关键词: 蛋白核小球藻, 共聚焦显微拉曼光谱仪, 偏最小二乘回归, 线性判别分析, 除草剂鉴别

Abstract:

This study investigate the method to identify two herbicides using Chlorella pyrenoidosa as a carrier and obtained the Raman spectral information of β-carotene of Chlorella pyrenoidosa grown in glyphosate-polluted water, butachlor-polluted water and normal water, respectively. The partial least-squares(PLS) model and linear discriminant analysis(LDA) classification model were established by the spectral signal after preprocessing. When the threshold is ±0.3, the correct prediction rate to glyhosate and butachlor reached 83.33% by using the PLS model based on the whole band, and correct classification rate of the two herbicides can reach 100% by using LDA classification model based on characteristic peaks. The results show that using Chlorella pyrenoidosa as a carrier to identify heribicides glyphosate and butachlor is feasible and LDA classification model is more suitable for classification of the two herbicides.

Key words: Chlorella pyrenoidosa, Confocal Raman microspectroscopy, Partial least square, Linear discriminant analysis, Herbicides identification

中图分类号:

O657

TrendMD:

邵咏妮, 蒋林军, 潘健, 何勇. 基于显微拉曼检测蛋白核小球藻鉴别丁草胺及草甘膦. 高等学校化学学报, 2015, 36(6): 1082.

SHAO Yongni, JIANG Linjun, PAN Jian, HE Yong. dentification of Glyphosate and Butachlor by Detecting Chlorella Pyrenoidosa with Raman Microspectroscopy^†. Chem. J. Chinese Universities, 2015, 36(6): 1082.

图/表 4

Fig.1 Original Raman spectra of single cell of Chlorella pyrenoidosa samples cultivated in different watera. Glyphosate water; b. normal water; c. butachlor water.

Fig.2 Preprocessing of Raman spectra(A) The origin spectrum; (B) bior2.6 wavelet transform; (C) bior2.6+Savitzky-Golay+Baseline; (D) Max-normalization.

Fig.3 Predicted and actual results of three kinds of water samplesGW: glyphosate-polluted water; NW: normal water; BW: butachlor-polluted water.

Table 1 Correct classification rate(CCR) of different types of water based on LDA modeling

Type of water	Calibration			Prediction
Type of water	Number	Missed	CCR(%)	Number	Missed	CCR(%)
Glyphosate-polluted water	20	0	100	10	0	100
Normal water	20	0	100	10	1	90
Butachlor-polluted water	20	0	100	10	0	100

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基于显微拉曼检测蛋白核小球藻鉴别丁草胺及草甘膦

dentification of Glyphosate and Butachlor by Detecting Chlorella Pyrenoidosa with Raman Microspectroscopy^†

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基于显微拉曼检测蛋白核小球藻鉴别丁草胺及草甘膦

dentification of Glyphosate and Butachlor by Detecting Chlorella Pyrenoidosa with Raman Microspectroscopy†

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