Abstract
Fibromyalgia typically involves pain, fatigue, and mood disruptions, often necessitating over two years and around four medical consultations for diagnosis. The combination of spectroscopy and chemometric techniques holds promise as a cost-effective and accurate strategy for screening fibromyalgia according to the association between the symptoms and spectral data. The study aimed to explore the association between spectrochemical analysis coupled to chemometric techniques with fibromyalgia symptoms. A total of 126 controls and 126 patients with fibromyalgia participated in the study. Blood plasma was analyzed using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy in conjunction with chemometric techniques for posterior association between pain, kinesiophobia, pain catastrophizing, impact of fibromyalgia, quality of life and anxiety. The datasets underwent multivariate classification using supervised models. Different chemometric algorithms were tested to classify the spectral data and the association between symptoms. A clear accuracy discrimination was observed to moderate and severe pain (82.1%; 100%); kinesiophobia (84.6%; 80.8%), catastrophizing (87.5%; 81.8%), impact of fibromyalgia (74.8%; 77.8%), anxiety (100%; 76.9%) and mild and regular quality of life (93.2%; 81.4%). The obtained favorable classification results validate the effectiveness of this technique as an analytical tool for fibromyalgia detection.