Analysing Raman spectra of crystalline cellulose degradation by fungi using artificial neural networks
This thesis investigates the use of artificial neural networks for classifying Raman spectra of partially degraded cellulose samples by fungal species. A multilayer perceptron configuration of 4 hidden layers and 128 hidden nodes was able to classify a set of 60 samples with an overall prediction accuracy of 0.55. Results show that data resolution is an important factor when optimizing classifier