SPECTROSCOPIC PEAT CLASSIFICATION AND CALIBRATION USING ELECTRON-SPIN-RESONANCE AND MULTIVARIATE DATA-ANALYSIS

Electron Spin Resonance (ESR) measurements were performed on particle size fractions of high and low humified Sphagnum fuscum and Carex rost rata. Each peat sample was divided into seven particle size groups, wh ere the size varies between >2.0 and <0.045 mm. Each peat sample was a lso subjected to traditional chemical analysis, where the amounts of s ome chemical constituents have been determined: ash, carbon, hydrogen, nitrogen, sulphur, rhamnose, fucose, arabinose, xylose, mannose, gala ctose, glucose, and Klason lignin (including the bitumen fraction). Tw o respiration rate data, the carbon dioxide emission after 2 days and 25 days, have also been included. In order to handle all data in a rat ional manner, multivariate data analysis was used. According to princi pal component analysis of the ESR data, each peat type forms a well de fined class, which implies that a calibration model has to be created for each peat class. The main differences between the different peat c lasses were the amount of stable organic radicals, but two peat classe s could only be separated based on differences in type of organic radi cals. The partial least squares modeling, i.e., modeling of the correl ation between the ESR measurements and the chemical constituents and t he respiration data, works well for the low humified Carex peat type c lass and the low humified Sphagnum peat, slightly worse with the high humified Carex peat and the high humified Sphagnum peat types. Many of the dependent variables were well modeled. and unknown test samples w ere correctly predicted.