Analyses of organic and inorganic carbon are of great interest in the field of soil analyses. Soil samples from a national monitoring project were provided for this study, including more than 130 forest sites from Austria. We investigated the humus layers (if present undecomposed litter (L), of mixed samples of F- (intermediate decomposed organic matter) and H-(highly decomposed organic matter) (FH)) and upper mineral soil layers (0–5 and 5–10 cm) of the samples. Mid-infrared spectra were recorded and evaluated by their band areas; subsequently we calculated models with the partial least squares approach. This was done by correlating calculated data of the mid-infrared spectra with gas-volumetrically determined carbonate values and measurements of organic carbon from an elemental analyzer. For carbonate determination, this approach gave satisfying results. For measurements of organic carbon, it was necessary to discriminate into humus layers and mineral soils or even more groups to obtain satisfactory correlations between spectroscopically determined and conventionally measured values. These additional factors were the presence of carbonate, the forest type, and the dominant tree species. In mineral soils, fewer subdivisions were necessary to obtain useful results. In humus layers, groupings of sites with more similar characteristics had to be formed in order to obtain satisfying results. The conclusion is that the chemical background of soil organic matter leading to different proportions of functional groups, especially in the less humified organic matter of the humus layers, plays a key role in analyses with mid-infrared spectroscopy. Keeping this in mind, the present approach has a significant potential for the prediction of properties of forest soil layers, such as, e.g., carbonate and organic carbon contents.
Michael Tatzber, Franz Mutsch, Axel Mentler, Ernst Leitgeb, Michael Englisch, and Martin H. Gerzabek, "Determination of Organic and Inorganic Carbon in Forest Soil Samples by Mid-Infrared Spectroscopy and Partial Least Squares Regression," Appl. Spectrosc. 64, 1167-1175 (2010)