We utilized SHAP feature importance scores to analyze predictions made by the Cost-Sensitive Multilayer Perceptron Model (CSMLP), which was trained on VS, MS and FP features. The identified important features were visually represented by overlaying them on the spectra. Additionally, we created SHAP Summary plots and Bar Plots to showcase the feature importance of the top three classes with the highest occurrence. shap python library was used to derive all these results .