基于特征波段的高光谱技术检测水体中毒死蜱浓度的实验研究
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摘要
为了探究反射光谱检测水体中毒死蜱农药的可行性,使用由ASD公司的FieldSpecPro地物波谱仪构成的高光谱采集系统在室内、室外环境获取两种不同浓度区间的毒死蜱样品的光谱数据。基于偏最小二乘(PLS)和主成分分析(PCA)算法分别对毒死蜱样品光谱数据建立全波段定量模型,结果两种模型的预测能力均较高。通过相关性分析(CA)计算相关系数来选择毒死蜱样品光谱的特征波长,其中浓度区间为5～75 mg·L~(-1)的室内、室外实验光谱的特征波长为388, 1 080, 1 276 nm和356, 1 322, 1 693 nm,浓度区间为0.1～100 mg·L~(-1)的室内外实验样品光谱的特征波长为367, 1 070, 1 276, 1 708 nm和383, 1 081, 1 250, 1 663 nm。结合PLS算法建立样品特征波长光谱数据的定量模型,结果与全波段模型相比,浓度区间为5～75 mg·L~(-1)的室内外实验光谱PLS特征波长模型的校正集决定系数R■分别提高至0.987 5和0.999 2,预测集决定系数R■分别提高至0.989 4和0.994 4,校正集均方根误差RMSEC分别降低为2.841和0.714,预测集均方根误差RMSEP分别降低为1.715和1.244;浓度区间为0.1～100 mg·L~(-1)的室内外实验光谱特征波长PLS模型的校正集决定系数R■分别提高至0.998 3和0.998 8,预测集决定系数R■分别提高至0.998 4和0.999 0,校正集均方根误差RMSEC分别降低为1.383和1.186,预测集均方根误差RMSEP分别降低为1.510和1.229,验证集标准差与预测均方根误差的比值(RPD)有所增加,尤其是针对浓度区间为0.1～100 mg·L~(-1)的实验, RPD值显著增加至21.7,说明基于特征波长建立的毒死蜱样品定量模型具有较高精度的预测能力,但是通过不同浓度区间范围的对比实验发现, ASD地物光谱仪对低浓度的毒死蜱溶液预测的相对误差偏大,存在客观上的检测下限。为了保证不同试验条件下的毒死蜱农药的特征波长都得到分析,增强模型使用的普适性与鲁棒性,根据特征波长选择出4个波段,即351～393, 1 065～1 086, 1 245～1 281和1 658～1 713 nm作为特征波段。特征波段模型的波长变量个数共38个,相比于全波段模型的432个波长变量,模型变量精简了91.2%,其中浓度区间为5～75 mg·L~(-1)的室内外实验光谱PLS特征波段模型的R■分别为0.993 7和0.987 8,R■分别为0.979 8和0.998 2, RMSEC分别为1.690和2.516, RMSEP分别为1.987和0.659;浓度区间为0.1～100 mg·L~(-1)的室内外实验光谱特征波段PLS模型的R■分别为0.9882和0.9807,R■分别为0.9391和0.9936, RMSEC分别为3.345和3.942, RMSEP分别为8.996和2.663,且四种实验情况下的模型RPD值均大于2.5,满足定量分析条件。因此采用高光谱采集系统对室内和室外环境中毒死蜱农药的快速检测具有一定的可行性,此研究结果对有机磷农药等面源污染物快速检测有实际的应用价值,可为农田水体有机磷农药快速检测仪器的开发提供理论基础。
In order to investigate the feasibility of reflectance spectroscopy for the detection of chlorpyrifos pesticides in water, indoor and outdoor spectral data of chlorpyrifos samples in two different concentrations were obtained using a hyperspectral acquisition system composed of ASD's FieldSpecPro Spectrometer. The partial least squares(PLS) and principal component analysis(PCA) algorithms were used to establish quantitative models for spectral data of chlorpyrifos samples. The results showed that the predictable ability of the model is significantly reliable. Correlation analysis(CA) was used to calculate the correlation coefficient to select the characteristic wavelength of the spectrum of chlorpyrifos samples. The characteristic wavelengths of indoor and outdoor experimental spectra with concentration ranges of 5～75 mg·L~(-1) were 388, 1 080, 1 276 and 356, 1 322, 1 693 nm, respectively. And the characteristic wavelengths were 367, 1 070, 1 276, 1 708, and 383, 1 081, 1 250, 1 663 nm in the range of 0.1～100 mg·L~(-1) experiments. The PLS algorithm was used to establish a quantitative model of the sample characteristic wavelength spectral data. Compared with the full-band model, the calibration set determination coefficient(R■) of the PLS characteristic wavelength model with concentration range of 5～75 mg·L~(-1) was increased to 0.987 5 and 0.999 2 in the indoor and outdoor experiment, respectively. And the prediction set determination coefficient(R■) was increased to 0.989 4 and 0.994 4, respectively. The root mean square error of the calibration set(RMSEC) was reduced to 2.841 and 0.714, respectively. The root mean square error of the prediction set(RMSEP) was reduced to 1.715 and 1.244, respectively. The R■ of the characteristic wavelength PLS model with concentration range of 0.1～100 mg·L~(-1) in the indoor and outdoor experiment was increased to 0.998 3 and 0.998 8, respectively. The R■ was increased to 0.998 4 and 0.999 0, respectively, and the RMSEC of the correction set was reduced to 1.383 and 1.186, respectively, and the RMSEP of the prediction set was reduced to 1.510 and 1.229, respectively. The ratio of standard deviation of the validation set to standard error of prediction(RPD) were increased, especially for experiments with a concentration range of 0.1～100 mg·L~(-1). The RPD value increased to 21.7 significantly, indicating that the quantitative model based on the characteristic wavelength has higher accuracy of prediction ability. However, comparative experiments with different concentration ranges show that the relative error of the low-concentration chlorpyrifos solution prediction by the ASD spectrograph is large and there is an objective detection limit. In order to ensure that the characteristic wavelengths of chlorpyrifos pesticides under different experimental conditions are analyzed and the universality and robustness of the model are enhanced, four bands are selected according to the characteristic wavelengths, that is, 351～393, 1 065～1 086, 1 245～1 281 and 1 658～1 713 nm used as characteristic bands. The characteristic band model has a total of 38 wavelength variables. Compared with the 432 wavelength variables of the full-band model, the model variable was reduced by 91.2%. The R■ of indoor and outdoor experimental PLS models with concentration range of 5～75 mg·L~(-1) were 0.993 7 and 0.987 8, and R■ were 0.979 8 and 0.998 2, and RMSEC were 1.69 and 2.516, and RMSEP were 1.987 and 0.659, respectively. The R~2_C values of the experimental PLS model with concentration range of 0.1～100 mg·L~(-1) were 0.988 2 and 0.980 7 for the indoor and outdoor experiments, and the R■ were 0.939 1 and 0.993 6, and the RMSEC were 3.345 and 3.942, and the RMSEP were 8.996 and 2.663, respectively. All of the model RPD values were more than 2.5 and met the quantitative analysis conditions. Therefore, the hyperspectral system of the paper for the rapid detection of chlorpyrifos pesticides in indoor and outdoor environments has a certain feasibility. The results of this study have practical application value for the rapid detection of non-point source pollutants such as organic phosphorus pesticides, which can provide a theoretical basis for the development of an instrument for the rapid detection of organophosphorus pesticides in farmland water.
In order to investigate the feasibility of reflectance spectroscopy for the detection of chlorpyrifos pesticides in water, indoor and outdoor spectral data of chlorpyrifos samples in two different concentrations were obtained using a hyperspectral acquisition system composed of ASD's FieldSpecPro Spectrometer. The partial least squares(PLS) and principal component analysis(PCA) algorithms were used to establish quantitative models for spectral data of chlorpyrifos samples. The results showed that the predictable ability of the model is significantly reliable. Correlation analysis(CA) was used to calculate the correlation coefficient to select the characteristic wavelength of the spectrum of chlorpyrifos samples. The characteristic wavelengths of indoor and outdoor experimental spectra with concentration ranges of 5～75 mg·L~(-1) were 388, 1 080, 1 276 and 356, 1 322, 1 693 nm, respectively. And the characteristic wavelengths were 367, 1 070, 1 276, 1 708, and 383, 1 081, 1 250, 1 663 nm in the range of 0.1～100 mg·L~(-1) experiments. The PLS algorithm was used to establish a quantitative model of the sample characteristic wavelength spectral data. Compared with the full-band model, the calibration set determination coefficient(R■) of the PLS characteristic wavelength model with concentration range of 5～75 mg·L~(-1) was increased to 0.987 5 and 0.999 2 in the indoor and outdoor experiment, respectively. And the prediction set determination coefficient(R■) was increased to 0.989 4 and 0.994 4, respectively. The root mean square error of the calibration set(RMSEC) was reduced to 2.841 and 0.714, respectively. The root mean square error of the prediction set(RMSEP) was reduced to 1.715 and 1.244, respectively. The R■ of the characteristic wavelength PLS model with concentration range of 0.1～100 mg·L~(-1) in the indoor and outdoor experiment was increased to 0.998 3 and 0.998 8, respectively. The R■ was increased to 0.998 4 and 0.999 0, respectively, and the RMSEC of the correction set was reduced to 1.383 and 1.186, respectively, and the RMSEP of the prediction set was reduced to 1.510 and 1.229, respectively. The ratio of standard deviation of the validation set to standard error of prediction(RPD) were increased, especially for experiments with a concentration range of 0.1～100 mg·L~(-1). The RPD value increased to 21.7 significantly, indicating that the quantitative model based on the characteristic wavelength has higher accuracy of prediction ability. However, comparative experiments with different concentration ranges show that the relative error of the low-concentration chlorpyrifos solution prediction by the ASD spectrograph is large and there is an objective detection limit. In order to ensure that the characteristic wavelengths of chlorpyrifos pesticides under different experimental conditions are analyzed and the universality and robustness of the model are enhanced, four bands are selected according to the characteristic wavelengths, that is, 351～393, 1 065～1 086, 1 245～1 281 and 1 658～1 713 nm used as characteristic bands. The characteristic band model has a total of 38 wavelength variables. Compared with the 432 wavelength variables of the full-band model, the model variable was reduced by 91.2%. The R■ of indoor and outdoor experimental PLS models with concentration range of 5～75 mg·L~(-1) were 0.993 7 and 0.987 8, and R■ were 0.979 8 and 0.998 2, and RMSEC were 1.69 and 2.516, and RMSEP were 1.987 and 0.659, respectively. The R~2_C values of the experimental PLS model with concentration range of 0.1～100 mg·L~(-1) were 0.988 2 and 0.980 7 for the indoor and outdoor experiments, and the R■ were 0.939 1 and 0.993 6, and the RMSEC were 3.345 and 3.942, and the RMSEP were 8.996 and 2.663, respectively. All of the model RPD values were more than 2.5 and met the quantitative analysis conditions. Therefore, the hyperspectral system of the paper for the rapid detection of chlorpyrifos pesticides in indoor and outdoor environments has a certain feasibility. The results of this study have practical application value for the rapid detection of non-point source pollutants such as organic phosphorus pesticides, which can provide a theoretical basis for the development of an instrument for the rapid detection of organophosphorus pesticides in farmland water.
引文
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[2]Liang X,Xu L,Li H,et al.Physics and Chemistry of the Earth,Parts A/B/C,2011,36(9-11):395.
[3]LIU Hong-bin,ZOU Guo-yuan,FAN Xian-peng,et al(刘洪斌,邹国元,范先鹏,等).Establishment and Application of Monitoring Technology on Nonpoint Pollution from Arable Land(农田面源污染监测方法与实践).Beijing:Science Press(北京:科学出版社),2015.
[4]LI Jun-jie,HOU Chang-jun,HUO Dan-qun,et al(李俊杰,侯长军,霍丹群,等).Journal of Food Safety&Quality(食品安全质量检测学报),2013,4(1):19.
[5]Nan J X,Wang J,Piao X F,et al.Talanta,2015,142:64.
[6]Zhang L,Wang Y,Sun C,et al.Food Analytical Methods,2013,(6):1508.
[7]Zheng Q Q,Yu Y H,Fan K,et al.Analytical and Bioanalytical Chemistry,2016,408(21):5819.
[8]Wei M,Wang J J.Sensors and Actuators B:Chemical,2015,211:290.
[9]ZHANG Ling-biao,HE Jian-guo,LIU Gui-shan,et al(张令标,何建国,刘贵珊,等).Food and Machinery(食品与机械),2014,(1):82.
[10]WANG Hai-yang,LIU Yan-de,ZHANG Yu-xiang(王海阳,刘燕德,张宇翔).Transactions of the Chinese Society of Agricultural Engineering(农业工程学报),2017,33(2):291.
[11]Weng S Z,Li M,Chen C,et al.Royal Society of Chemicstry,2015,6(7):2563.
[12]Shao Y N,LI Y,Jiang L J,et al.Water Research,2016,104:432.
[13]HU Rong-ming,GUO Jiang-bo,HUANG Yuan-cheng,et al(胡荣明,郭江波,黄远程,等).Chinese Journal of Posticide Science(农药学学报),2015,17(5):563.
[14]ZHOU Qing,ZHOU Bin,WANG Ren-chao,et al(周清,周斌,王人潮,等).Journal of South China Agricultural University(华南农业大学学报),2005,26(1):31.
[15]CHU Xiao-li(褚小立).Molecular Spectroscopy Analytical Technology Combined with Chemometrics and Its Applications(化学计量学方法与分子光谱分析技术).Beijing:Chemical Industry Press(北京:化学工业出版社),2011.
[16]Malley D F,Ronicke H,Findlay D L,et al.Journal of Paleolimnology,1999,21(3):295.
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