In clinical analysis, the most relevant factors for the reliability of the results are accuracy, precision, reproducibility and repeatability parameters of the assays depending on the methods used. In addition to the traditional methods recommended for these tests, Raman spectroscopy has been used to evaluate the possibility of being used for this purpose, with partial least squares (PLS) regression being applied as a modeling of the spectra to quantify the analytes of interest. The comparative assay of analytes such as triglycerides, total cholesterol, HDL, creatinine, urea and glucose from 100 human serum samples, evaluated on a portable Raman spectrometer (785 nm) and by gold standard spectrophotometry (reference analytical method), showed that the correlation between the real values and the values predicted by the PLS model for lipids – triglycerides and total cholesterol, are strong (r > 0.8), and for the other parameters are weak (r < 0.4). At this research stage, the analytes HDL, creatinine, urea and glucose, particularly the latter, have the potential to be increased through the use of signal processing (filtering) and analyte-addition techniques, which can reach a level of results that is acceptable to large-scale laboratory screening, at a lower cost and faster than current colorimetric method.