Identification of spectral features differentiating fungal strains in infrared absorption spectroscopic images

Abstract: There are many unknowns regarding the interaction between fungi and their surroundings. In this project, we took a closer look at hyperspectral images of several fungal strains on two different substrates. The project mainly consisted of developing a code for the classification of fungal strains and the extraction of information from it. The classifier used hyperspectral images in infrared of four strains of three species that were grown on two different substrates. A total of 192 images were used. Images were processed using software that was already created for the analysis of hyperspectral data. We developed a random forest classifier to classify the samples by fungal strains. The performance of different classifier parameters was determined and the best ones were chosen. Then, spectra and their derivatives were analyzed and their classification performances were compared. As the last step of the project, the developed random forest algorithm was used to identify the most important wavenumbers for discerning different fungal strains. One of the interesting results was an unexpectedly high increase in the accuracy of the classifier when the first derivative of spectra was used instead of plain spectra.