The prediction of soil carbon fractions using mid-infrared-partial least square analysis uri icon

abstract

  • This paper describes the application of mid-infrared (MIR) spectroscopy and partial least-squares (PLS) analysis to predict the concentration of organic carbon fractions present in soil. The PLS calibrations were derived from a standard set of soils that had been analysed for total organic carbon (TOC), particulate organic carbon (POC), and charcoal carbon (char-C) using physical and chemical means. PLS calibration models from this standard set of soils allowed the prediction of TOC, POC, and char-C fractions with a coefficient of determination (R-2) of measured v. predicted data ranging between 0.97 and 0.73. For the POC fraction, the coefficient of determination could be improved (R-2 = 0.94) through the use of local calibration sets. The capacity to estimate soil fractions such as char-C rapidly and inexpensively makes this approach highly attractive for studies where large numbers of analyses are required. Inclusion of a set of soils from Kenya demonstrated the robustness of the method for total organic carbon and charcoal carbon prediction.
  • This paper describes the application of mid-infrared (MIR) spectroscopy and partial least-squares (PLS) analysis to predict the concentration of organic carbon fractions present in soil. The PLS calibrations were derived from a standard set of soils that had been analysed for total organic carbon (TOC), particulate organic carbon (POC), and charcoal carbon (char-C) using physical and chemical means. PLS calibration models from this standard set of soils allowed the prediction of TOC, POC, and char-C fractions with a coefficient of determination (R2) of measured v. predicted data ranging between 0.97 and 0.73. For the POC fraction, the coefficient of determination could be improved (R2 = 0.94) through the use of local calibration sets. The capacity to estimate soil fractions such as char-C rapidly and inexpensively makes this approach highly attractive for studies where large numbers of analyses are required. Inclusion of a set of soils from Kenya demonstrated the robustness of the method for total organic carbon and charcoal carbon prediction

publication date

  • 2007
  • 2007
  • 2007