Prediction of Atrazine Sorption Coefficients in Soils Using Mid-Infrared Spectroscopy and Partial Least-Squares Analysis

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• 作者：Rai S. Kookana ; Les J. Janik ; Mohsen Forouzangohar ; Sean T. Forrester
• 刊名：Journal of Agricultural and Food Chemistry
• 出版年：2008
• 出版时间：May 14, 2008
• 年：2008
• 卷：56
• 期：9
• 页码：3208 - 3213
• 全文大小：360K
• 年卷期：v.56,no.9(May 14, 2008)
• ISSN：1520-5118

文摘

This study explored the potential of mid-infrared spectroscopy (MIR) with partial least-squares (PLS) analysis to predict sorption coefficients (K_d) of pesticides in soil. The MIR technique has the advantage of being sensitive to both the content and the chemistry of soil organic matter and mineralogy, the important factors in the sorption of nonionic pesticides. MIR spectra and batch K_d values of atrazine were determined on a set of 31 soil samples as reference data for PLS calibration. The samples, with high variability in soil organic carbon content (SOC), were chosen from 10 southern Australian soil profiles (A1, A2, B, and C in one case). PLS calibrations, developed for the prediction of K_d from the MIR spectra and reference K_d data, were compared with predictions from K_oc-based indirect estimation using SOC content. The reference K_d data for the 31 samples ranged from 0.31 to 5.48 L/kg, whereas K_oc ranged from 30 to 680 L/kg. Both coefficients generally increased with total SOC content but showed a relatively poor coefficient of determination (R² = 0.53; P > 0.0001) and a high standard error of prediction (SEP =1.22) for the prediction of K_d from K_oc. This poor prediction suggested that total SOC content alone could explain only half of the variation in K_d. In contrast, the regression plot of PLS predicted versus measured K_d resulted in an improved correlation, with R² = 0.72 (P > 0.0001) and standard error of cross-validation (SECV) = 0.63 for three PLS factors. With the advantages of MIR-PLS in mind, (i) more accurate prediction of K_d, (ii) an ability to reflect the nature and content of SOC as well as mineralogy, and (iii) high repeatability and throughput, it is proposed that MIR-PLS has the potential for an improved and rapid assessment of pesticide sorption in soils.