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�
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(RC^2) 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(RP^2)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 RC^2 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 RP^2 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 pred
作者
马瑞峻
张亚丽
陈瑜
张亚莉
邱志
萧金庆
MA Rui-jun;ZHANG Ya-li;CHEN Yu;ZHANG Ya-li;QIU Zhi;XIAO Jin-qing(College of Engineering, South China Agricultural University, Guangzhou 510642, China)
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2019年第3期923-930,共8页
Spectroscopy and Spectral Analysis
基金
国家重点研发计划(2016YFD0800901)
国家自然科学基金项目(51309103)资助
关键词
高光谱
毒死蜱
偏最小二乘法
相关性分析法
定量模型
特征波长
特征波段
Hyper-spectrum
Chlorpyrifos
Partial least squares
Correlation analysis
Quantitative model
Characteristic wavelength
Characteristic band
