Raman spectroscopy records the inelastic scattering of photons originating from striking a sample with monochromatic light. Inelastic, or Raman, scattered photons shift in wavelength due to excitation of the vibrational modes of molecules struck by the incident light. The Raman scattered photons are representative of all of the covalent bonds contained within a sample. Raman spectra taken of biological systems such as proteins, bacterial colonies, and liquid waste, are difficult to interpret due to the complexity of their covalent bond landscape and mixtures of molecules in highly variable concentrations. Rather than deconstructing Raman spectra to attempt assignment of specific bonds and functional groups to wavenumber peaks, here we have developed a chemometric analysis pipeline for quantifying the similarities and differences among a set of Raman spectra. This quantification aids in both classification of samples, and in measuring how samples change over time. The chemometric approach for interpretation of Raman spectra was made freely available in a user-friendly format via a MATLAB add-on called the Raman Data Analysis (RDA) Toolbox. Demonstrations of the RDA Toolbox functionalities on Raman spectra taken of various common biological systems are included, such as determination of protein concentration and monitoring bacterial culture growth. The RDA Toolbox and Raman spectroscopy are also used to initiate research in novel areas. Fast and accurate evaluation of enzyme specific activity is required for engineering enzymes, and results of Raman assays, evaluated in the RDA Toolbox, are successfully correlated to absorbance activity assays of an enzyme WT and mutant library. Further development of this research could alleviate the bottleneck of screening mutant libraries in enzyme engineering projects. The Toolbox is then used in a distinctly different application for evaluating urine and spent dialysate samples from patients with end stage renal disease. Categorization between samples from healthy volunteers and patients is accomplished with close to 100% accuracy, and evidence indicating that Raman spectroscopy can serve as an early diagnostic tool for infections of the peritoneal membrane is presented.