Tile-Based Fisher Ratio Analysis with Support Vector Machine Regression Modeling of GC×GC-TOFMS Data of VOCs Produced by Malassezia pachydermatis Grown at Variable pHs

Malassezia yeasts are commensal microorganisms found in human and animal skin. Species of Malassezia have been connected to skin and opportunistic infections, where certain microenvironmental conditions are required in the host for the pathogenic processes to occur. We present the analysis of the volatile space of Malassezia pachydermatis grown at three pH values (5.7, 9.7, and 12.4) by comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS). Since changes in pH also affect the growth media and the volatile organic compounds (VOCs) produced by it, media blanks at the three pHs were analyzed, with 5 replicates of each of the 6 samples. Following data collection, GC×GC-TOFMS chromatograms were analyzed by Fisher ratio software that found 566 analytes, out of which 288 were tentatively identified with a mass spectrum match value (MV) ≥ 800 based upon a NIST library search. A signal pattern for each of the 566 analytes was obtained by averaging the replicates, and two metrics (R and RSD) were calculated for each signal pattern. The R metric was defined to focus upon the differences between analyte signals of media blanks and M. pachydermatis by taking away the influence of pH changes, while the RSD metric was defined to evaluate only the influence of pH. Based on the R metric magnitude, the analytes were split into 3 categories: media analytes consumed by M. pachydermatis, analytes at similar concentration at a given pH in the media and M. pachydermatis, and analytes produced in M. pachydermatis only. Many of the M. pachydermatis produced analytes were already shown to be produced by other yeast species and shown to have biological significance when the pH is varied. Further, there is evidence of some bioconversions between the consumed analytes discovered versus the analytes produced. We also verified our classification results using a support vector machine (SVM) model, where cross-validation provided a very promising outcome with true positive rate (TPR) and true negative rate (TNR) both being over 0.95 and the error being below 0.03 (or 3%).