Diagnosis and treatment of fibromyalgia (FM) remains a challenge owing to the lack of reliable biomarkers. Our objective was to develop a rapid biomarker-based method for diagnosing FM by using vibrational spectroscopy to differentiate patients with FM from those with rheumatoid arthritis (RA), osteoarthritis (OA), or systemic lupus erythematosus (SLE) and to identify metabolites associated with these differences. Blood samples were collected from patients with a diagnosis of FM (n ‫؍‬ 50), RA (n ‫؍‬ 29), OA (n ‫؍‬ 19), or SLE (n ‫؍‬ 23). Bloodspot samples were prepared, and spectra collected with portable FT-IR and FT-Raman microspectroscopy and subjected to metabolomics analysis by ultra-HPLC (uHPLC), coupled to a photodiode array (PDA) and tandem MS/MS. Unique IR and Raman spectral signatures were identified by pattern recognition analysis and clustered all study participants into classes (FM, RA, and SLE) with no misclassifications (p < 0.05, and interclass distances > 2.5). Furthermore, the spectra correlated (r ‫؍‬ 0.95 and 0.83 for IR and Raman, respectively) with FM pain severity measured with fibromyalgia impact questionnaire revised version (FIQR) assessments. Protein backbones and pyridine-carboxylic acids dominated this discrimination and might serve as biomarkers for syndromes such as FM. uHPLC-PDA-MS/MS provided insights into metabolites significantly differing among the disease groups, not only in molecular m/z ؉ and m/z ؊ values but also in UV-visible chromatograms. We conclude that vibrational spectroscopy may provide a reliable diagnostic test for differentiating FM from other disorders and for establishing serologic biomarkers of FM-associated pain.

The aim of this study is to develop a non-targeted approach for the authentication of extra virgin olive oil (EVOO) using vibrational spectroscopy signatures combined with pattern recognition analysis. Olive oil samples (n = 151) were grouped as EVOO, virgin olive oil (VOO)/olive oil (OO), and EVOO adulterated with vegetable oils. Spectral data was collected using a compact benchtop Raman (1064 nm) and a portable ATR-IR (5-reflections) units. Oils were characterized by their fatty acid profile, free fatty acids (FFA), peroxide value (PV), pyropheophytins (PPP), and total polar compounds (TPC) through the official methods. The soft independent model of class analogy analysis using ATR-IR spectra showed excellent sensitivity (100%) and specificity (89%) for detection of EVOO. Both techniques identified EVOO adulteration with vegetable oils, but Raman showed limited resolution detecting VOO/OO tampering. Partial least squares regression models showed excellent correlation (Rval ≥ 0.92) with reference tests and standard errors of prediction that would allow for quality control applications.

Vibrational spectroscopy (mid-infrared (IR) and Raman) and its fingerprinting capabilities offer rapid, high-throughput, and non-destructive analysis of a wide range of sample types producing a characteristic chemical “fingerprint” with a unique signature profile. Nuclear magnetic resonance (NMR) spectroscopy and an array of mass spectrometry (MS) techniques provide selectivity and specificity for screening metabolites, but demand costly instrumentation, complex sample pretreatment, are labor-intensive, require well-trained technicians to operate the instrumentation, and are less amenable for implementation in clinics. The potential for vibration spectroscopy techniques to be brought to the bedside gives hope for huge cost savings and potential revolutionary advances in diagnostics in the clinic. We discuss the utilization of current vibrational spectroscopy methodologies on biologic samples as an avenue towards rapid cost saving diagnostics.