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 was to investigate the ability of a rapid biomarker-based method for diagnosis of fibromyalgia syndrome (FM) using mid-infrared microspectroscopy (IRMS) to differentiate patients with FM from those with osteoarthritis (OA) and rheumatoid arthritis (RA), and to identify molecular species associated with the spectral patterns. Under IRB approval, blood samples were collected from patients diagnosed with FM (n = 14), RA (n = 15), or OA (n = 12). Samples were prepared, placed onto a highly reflective slide, and spectra were collected using IRMS. Spectra were analyzed using multivariate statistical modeling to differentiate groups. Aliquots of samples also were subjected to metabolomic analysis. IRMS separated subjects into classes based on spectral information with no misclassifications among FM and RA or OA patients. Interclass distances of 15.4 (FM vs. RA), 14.7 (FM vs. OA) and 2.5 (RA vs. OA) among subjects, demonstrating the ability of IRMS to achieve reliable resolution of unique spectral patterns specific to FM. Metabolomic analysis revealed that RA and OA groups were metabolically similar, whereas biochemical differences were identified in the FM that were quite distinctive from those found in the other two groups. Both IRMS and metabolomic analysis identified changes in tryptophan catabolism pathway that differentiated patients with FM from those with RA or OA.

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Near-infrared spectroscopy (NIRS) is a well-established technique for determining the components of foods. Sample preparation for NIRS is easy, making it suitable for breeding and/or quality evaluation, for which a large number of samples should be analyzed. We aimed to assess the feasibility of NIRS to estimate parameters that seem to influence consumers' perception of the seed coat of common beans: dietary fiber (DF), uronic acids (UA), ashes, calcium, and magnesium. We used reference methods to analyze ground seed coats of 90 common bean samples with a wide range of genetic variability and cultivated at many locations. We registered the NIR spectra on intact beans and ground seed coat samples. We derived partial least-squares (PLS) regression equations from a set of calibration samples and tested their predictive power in an external validation set. For intact beans, only RER values for ashes and calcium are good enough for very rough screening. For ground seed coat samples, the RPD and RER values for ashes (3.49 and 14.09, respectively) and calcium (3.57 and 12.70, respectively) are good enough for screening. RPD and RER values for DF (2.60 and 9.15, respectively) and RER values for magnesium (6.57) also enable rough screening. A poorer correlation was achieved for UA. We conclude that NIRS can help in common bean breeding research and quality evaluation.

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Fish oil dietary supplements have been linked with health benefits, due to high omega‐3 concentration. The sources of these effects, polyunsaturated fatty acids such as eicosapentaenoic acid and docosahexaenoic acid, are almost exclusively found in seafood products. Our objectives were to characterize the composition of commercial omega‐3 dietary supplements dietary supplements and to generate partial least square regression (PLSR) models using infrared spectroscopy and chemometrics. Fatty acid (FA) composition of oils was determined by FA methyl ester gas chromatography. The supplements encompassed a wide range of FA profiles and delivery methods. Infrared spectral data were collected by portable mid‐infrared Fourier transform infrared (MID FT‐IR) equipment. Principal components analysis (PCA) separated samples based on the type of ester present in the fish oil dietary supplements, showing a strong influence of the 1038 cm−1 band, which is typically associated with C=C and C–O stretching vibrations. In addition, PLSR was used to correlate the spectra data with GC‐FAME results. PCA using the spectroscopy data allowed for tight clustering of fish oil into distinct classes, depending on the source and processing. PLSR using MID FT‐IR spectra and FA composition generated multivariate models with high correlation coefficient (R ≥ 0.93), and SEP between 0.53 and 2.13 g of FA per 100 g of oil. Our results indicate that IR spectroscopy combined with chemometrics provides for robust screening of FA composition of fish oil supplements, and discriminate types of FAs esterification.

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