Pattern recognition of soil samples based on the microbial fatty acid contents

The problem of distinguishing particles in ambient airborne particulate matter derived from different soils is difficult when based only on elemental composition. However, biologically derived chemical species associated with a specific crop in a farming area could be more useful in discriminating soil samples. Phospholipid fatty acids (PLFAs) extracted from microorganisms in soils have been used to fingerprint soil microbial community composition. A set of 72 PLFAs was found to occur in at least 10% of all soil samples studied, and data on these PLFAs were used to distinguish soils planted with different crops. This paper describes the application of discriminant partial least squares (D-PLS) and regularized discriminant analysis (RDA) to PLFA data from soils. A variable selection approach based on the PLS regression coefficients has been proposed to identify the most important PLFA variables for the classification and to improve classification results. RDA uses a regularized covariance matrix estimate for the conventional statistical discriminant analysis methods and provides some advantages. The results showed that both the D-PLS and RDA methods provided satisfactory performance in classifying soil samples, with RDA being slightly better. The study also indicated that the variable selection strategy was able to improve the classification results and to help identify the most important PLFAs for distinguishing soils. The best classification performance has been achieved by applying the RDA analysis to the selected-variable PLFA data.