Diffuse reflectance spectra of bacterial colonies, from hyperspectral images, allowed for a label-free Gram classification into Gram-positive (GP) and Gram-negative (GN) types. Thirty-eight strains belonging to 14 bacterial species typically encountered in urinary tract infections (UTI) were cultivated on chromID CPS Elite translucent chromogenic culture medium to build training and testing sets. Using Support Vector Machine (SVM) supervised learning models, we demonstrated excellent classification rates with a percentage of correctly classified samples as high as 95%. Because determination of discriminant spectral channels is critical both for fundamental reasons to help understand the origin of the discriminant signal and for practical reasons to envision simpler multispectral systems, parsimonious analysis was conducted employing a Fused LASSO (Least Absolute Shrinkage and Selection Operator) or based on an uncertainty test in Partial least squares PLS regression analysis. Two prominent distinct spectral regions were thus identified allowing to hypothesize that cytochrome ratios might be, at least in part, at the origin of the differences observed between Gram-negative and Gram-positive bacteria populations.