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 works, the studies carried out in this thesis explored the spray ionization mass spectrometry on paper (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. For a first study, described in chapter 1, the works focusedon the differentiation of the control and case groups, using exploratory analysis with PCA and supervised analyzes with PCA-LDA, SPA-LDA, GA-LDA, PCA- QDA, SPA-QDA, GA-QDA, PCA-SVM, SPA-SVM and GA-SVM. The results obtained by the classifications showed 100% separation for both classes, involving the PCA unsupervised technique and the SPA-LDA supervised technique. With the results shown in Chapter 1, a useful tool in the screening of fibromyalgia is suggested, with the advantages of providing quick results, simple operation, low cost and the use of small aliquots of bloodplasma samples. A second  study (Chapter  2) focused  on  the  classification  of the main  clinical  variables  evaluated  in  the  diagnosis  of  fibromyalgia.  According  to  the questionnaires applied to the patients, the spectral data were organized into different sets of data, representing the different levels of each symptom studied. Among some of the results found, it was observed that at moderate levels, it was possible to recognize a tendency of good differentiation between patients with and without fibromyalgia, with 100% effectiveness in distinguishing samples from the control group in the EVA symptom (PCA-LDA), and 100% in the distinction of samples from the case group for the CAT symptom (PCA-LDA). These results provide a theoretical contribution on the approach of the main symptoms evaluated in the diagnosis of fibromyalgia, demonstrating the good execution of the analytical methodology in the detection of the disease