中文摘要：

研究了中红外漫反射光谱快速预测土壤重金属元素含量的可行性。以在南京江宁区和八卦洲采集的共161个土壤样品为例,利用偏最小二乘回归（PLSR）法对土壤中Ni,Cr,Cu,As,Zn,Pb,Hg和Cd等8种重金属元素数据进行了预测。通过对样品的中红外（MIR）漫反射光谱进行各种预处理,探讨了中红外光谱数据预处理对预测精度的影响,并比较了中红外光谱与可见光-近红外（VNIR）光谱对土壤重金属含量预测的精度。结果表明,依次经平滑、基线校正、多元散射校正预处理能显著提高中红外光谱数据的预测精度;经校正的中红外光谱对异地样品预测的均方根误差是可见光-近红外光谱的21%～73%,比VNIR波段更能准确预测异地样品中土壤重金属元素含量。研究表明,中红外漫反射光谱可以作为一种快速、非破坏方法预测土壤重金属元素含量,且比可见光-近红外精度高。

英文摘要：

The present paper analyzed the possibility of mid-infrared diffuse reflectance spectra for quick assessment of heavy metal element content in soil quickly.Soil samples were collected from Jiangning District and Baguazhou Island,and the numbers of sample were 103 and 58 separately.Jiangning District samples were used as calibration set while Baguazhou Island samples as validation set.To assess the utility of different pre-treatment process of MIR spectroscopy for soil heavy metal element content analysis,we used PLSR method to develop the calibration between spectral data and soil elements content.Three spectral pre-treating techniques such as smooth,log（1/N）,baseline correction,multiplicative scatter correction were used for promotion of predicting performance.The result showed that the progress of （log-BC-MSC） in turn achieved optimal calibration of MIR spectra and better prediction for ex-situ soils.Though the calibration data were treated by different pre-treating schema,the R2 of the 8 elements followed the same law：Ni〉0.8〉 Cr,Cu,Zn,Pb,Hg〉0.6 〉As,Cd.When we applied these calibrations to Baguazhou Island soils,（log-BC-MSC） treated data results in the smallest RMSEp-BGZ.We used the same calibration method to compare the predictive ability of MIR spectra to VNIR spectra.The R2 of 8 elements developed by VNIR spectral calibration are sometime larger than MIR’s,but after we applied these calibrations to validation set,the RSME of MIR data for prediction of BGZ soil samples is 21% to 73% of VNIR’s.This result showed us that for predicting ex-situ soils,MIR analysis substantially outperformed VNIR.These results indicated that MIR spectra can be used to predict soil heavy metal content quickly and non-destructively.