This thesis presents the development of new analytical methodologies that combine instrumental mass spectrometry readings with multivariate classification, in the search for the differentiation of blood plasma samples for the detection of fibromyalgia. Within the execution of the research, the studies carried out in this thesis explored the Paper Spray Ionization Mass Spectrometry (PSI-MS) to obtain spectra from the blood plasma of patients with and without fibromyalgia, and the processing of these spectral data by multivariate analysis models. The multivariate classification techniques used represent Principal Component Analysis (PCA), Successive Projection Algorithm (SPA), Genetic Algorithm (GA), Linear Discriminant Analysis (LDA) and Quadratic (QDA) and Support Vector Machine (SVM) proposing PCA-LDA, SPA- LDA, GA-LDA, PCA-QDA, SPAQDA, GA-QDA, PCA-SVM, SPA-SVM and GA-SVM models. For a first study, using a total of twenty samples, the sensitivity and specificity results showed 100% separation between classes, involving the supervised SPA-LDA technique, in addition to good classifications with other models. For a second study the classifications obtained 100% sensitivity for the PCA-LDA model, with sixty four samples, using patients with symptoms related to catastrophizing pain (CAT) as a parameter. In addition to this last result, the second study also presented classifications of different datasets with samples of patients with and without fibromyalgia, related to other common symptoms in the disease. Another important result, present in both studies, is the association of variables selected by the classification models to the lysophosphatidylcholine (LysoPC) metabolite class, mentioned in studies on fibromyalgia biomarkers. Thus, the results demonstrated suggest the development of useful tools in the screening of fibromyalgia, with the advantages of providing quick results, of simple operation and use of small aliquots of blood plasma  samples,  also  providing  a  good  theoretical  contribution  on  the  diagnostic  approach  of  fibromyalgia,  demonstrating  the  good  execution  of  the  analytical methodology in the detection of the disease.