Portable Mid-Infrared Spectroscopy Combined with Chemometrics to Diagnose Fibromyalgia and Other Rheumatologic Syndromes Using Rapid Volumetric Absorptive Microsampling.

The diagnostic criteria for fibromyalgia (FM) have relied heavily on subjective reports of experienced symptoms coupled with examination-based evidence of diffuse tenderness due to the lack of reliable biomarkers. Rheumatic disorders that are common causes of chronic pain such as rheumatoid arthritis, systemic lupus erythematosus, osteoarthritis, and chronic low back pain are frequently found to be comorbid with FM. As a result, this can make the diagnosis of FM more challenging. We aim to develop a reliable classification algorithm using unique spectral profiles of portable FT-MIR that can be used as a real-time point-of-care device for the screening of FM. A novel volumetric absorptive microsampling (VAMS) technique ensured sample volume accuracies and minimized the variation introduced due to hematocrit-based bias. Blood samples from 337 subjects with different disorders (179 FM, 158 non-FM) collected with VAMS were analyzed. A semi-permeable membrane filtration approach was used to extract the blood samples, and spectral data were collected using a portable FT-MIR spectrometer. The OPLS-DA algorithm enabled the classification of the spectra into their corresponding classes with 84% accuracy, 83% sensitivity, and 85% specificity. The OPLS-DA regression plot indicated that spectral regions associated with amide bands and amino acids were responsible for discrimination patterns and can be potentially used as spectral biomarkers to differentiate FM and other rheumatic diseases.

Rapid Sensing: Hand-Held and Portable FTIR Applications for On-Site Food Quality Control from Farm to Fork.

Today, one of the world's biggest problems is the assurance of food integrity from farm to fork. Economically motivated food adulteration and food authenticity problems are increasing daily with considerable health and economic effects. Early detection and prevention of food integrity-related problems could be provided by the application of effective on-site food analysis technologies. FTIR spectroscopy coupled with chemometrics can be used for the rapid quality control of a wide variety of food products with fast, high-throughput, accurate and nondestructive analysis advantages. In particular, hand-held and portable FTIR instruments have the potential to surveil food quality and food safety in various critical segments of the food supply chain. In this review, we explore the abilities of hand-held and portable FTIR spectrometers combined with multivariate statistics to conduct a quality evaluation of various food products in terms of food adulteration and authenticity issues. An examination of the literature showed that comparable results were obtained based on detection limits, correlation coefficient (R2) values, standard error values and discrimination power by using both portable/hand-held FTIR spectrometers and benchtop FTIR spectrometers. In conclusion, this review highlights the potential usefulness of portable and hand-held FTIR spectrometers combined with chemometrics for maintaining the food quality through the presentation of various applications that may shed light for on-site food control at any point of the food supply chain.

Monitoring the ripening attributes of Turkish white cheese using miniaturized vibrational spectrometers.

Monitoring the ripening process by prevalent analytic methods is laborious, expensive, and time consuming. Our objective was to develop a rapid and simple method based on vibrational spectroscopic techniques to understand the biochemical changes occurring during the ripening process of Turkish white cheese and to generate predictive algorithms for the determination of the content of key cheese quality and ripening indicator compounds. Turkish white cheese samples were produced in a pilot plant scale and ripened for 100 d, and samples were analyzed at 20 d intervals during storage. The collected spectra (Fourier-transform infrared, Raman, and near-infrared) correlated with major composition characteristics (fat, protein, and moisture) and primary products of the ripening process and analyzed by pattern recognition to generate prediction (partial least squares regression) and classification (soft independent analysis of class analogy) models. The soft independent analysis of class analogy models classified cheese samples based on the unique biochemical changes taking place during the ripening process. partial least squares regression models showed good correlation (RPre = 0.87 to 0.98) between the predicted values by vibrational spectroscopy and the reference values, giving low standard errors of prediction (0.01 to 0.57). Portable and handheld vibrational spectroscopy units can be used as a rapid, simple, and in situ technique for monitoring the quality of cheese during aging and provide real-time tools for addressing deviations in manufacturing.

Analyzing the Interaction between Anthocyanins and Native or Heat-Treated Whey Proteins Using Infrared Spectroscopy.

The color stability of anthocyanins (ACN) has been shown to be improved by interaction with whey proteins (WP). In this study, we explore the ACN-WP interaction using Fourier transform infrared spectroscopy (IR). ACN from purple corn, grape, and black carrot (50 µM) were evaluated. IR spectra (4000-700 cm-1) were collected for native and preheated (40-80 °C) WP (5 mg/mL) and ACN-WP mixtures at pH 7.4. Soft independent modeling of class analogy was used to analyze the IR data. The WP secondary structure changed after heat treatments and after interaction with ACN. As expected, the WP α-helices decreased, and ß-sheet increased after heat treatment. The intensities of the WP amide I and II bands decreased after ACN addition, revealing a decrease in the WP α-helix content. Higher preheating temperatures (70-80 °C) resulted in a more disordered WP structure that favored stronger WP-ACN interactions related to amide III changes. Addition of ACN stabilized WP structure due to heat denaturation, but different ACN structures had different binding affinities with WP. WP structure had less change after interaction with ACN with simpler structures. These results increase our understanding of ACN-WP interactions, providing a potential strategy to extend anthocyanin color stability by WP addition.

Screening of Acrylamide of Par-Fried Frozen French Fries Using Portable FT-IR Spectroscopy.

Current assays for acrylamide screening rely heavily on LC-MS/MS or GC-MS, techniques that are not suitable to support point of manufacturing verification because it can take several weeks to receive results from a laboratory. A portable sensor that can detect acrylamide levels in real-time would enable in-house testing to safeguard both the safety of the consumer and the economic security of the agricultural supplier. Our objective was to develop a rapid, accurate, and real-time screening technique to detect the acrylamide content in par-fried frozen French fries based on a portable infrared device. Par-fried French fries (n = 70) were manufactured at times ranging from 1 to 5.5 min at 180 °C to yield a wide range of acrylamide levels. Spectra of samples were collected using a portable FT-IR device operating from 4000 to 700 cm-1. Acrylamide was extracted using QuEChERS and quantified using uHPLC-MS/MS. Predictive algorithms were generated using partial least squares regression (PLSR). Acrylamide levels in French fries ranged from 52.0 to 812.8 µg/kg. The best performance of the prediction algorithms required transformation of the acrylamide levels using a logarithm function with models giving a coefficient of correlation (Rcv) of 0.93 and RPD as 3.8, which means the mid-IR model can be used for process control applications. Our data corroborate the potential of portable infrared devices for acrylamide screening of high-risk foods.

Non-Invasive Monitoring of Ethanol and Methanol Levels in Grape-Derived Pisco Distillate by Vibrational Spectroscopy.

Handheld Raman and portable FT-IR spectroscopy devices were evaluated for fast and non-invasive determination of methanol and ethanol levels in Peruvian Pisco. Commercial Peruvian Pisco (n = 171) samples were kindly provided by the UNALM Alliance for Research in Alcohol and its Derivatives (Lima, Peru) and supplemented by purchases at grocery and online stores. Pisco spectra were collected on handheld Raman spectrometers equipped with either a 1064 nm or a 785 nm excitation laser and a portable infrared unit operating in transmission mode. The alcohol levels were determined by GC-MS. Calibration models used partial least-squares regression (PLSR) to develop prediction algorithms. GC-MS data revealed that 10% of Pisco samples had ethanol levels lower than 38%, indicating possible water dilution. Methanol levels ranged from 10 to 130 mg/100 mL, well below the maximum levels allowed for fruit brandies. Handheld Raman equipped with a 1064 nm excitation laser gave the best results for determining ethanol (SEP = 1.2%; RPre = 0.95) and methanol (SEP = 1.8 mg/100 mL; RPre = 0.93). Randomly selected Pisco samples were spiked with methanol (75 to 2800 mg/100 mL), and their Raman spectra were collected through their genuine commercial bottles. The prediction models gave an excellent performance (SEP = 98 mg/100 mL; RPre = 0.97), allowing for the non-destructive and non-contact determination of methanol and ethanol concentrations without opening the bottles.

Metabolic fingerprinting for diagnosis of fibromyalgia and other rheumatologic disorders.

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.

High-Throughput Phenotyping Approach for Screening Major Carotenoids of Tomato by Handheld Raman Spectroscopy Using Chemometric Methods.

Our objective was to develop a rapid technique for the non-invasive profiling and quantification of major tomato carotenoids using handheld Raman spectroscopy combined with pattern recognition techniques. A total of 106 samples with varying carotenoid profiles were provided by the Ohio State University Tomato Breeding and Genetics program and Lipman Family Farms (Naples, FL, USA). Non-destructive measurement from the surface of tomatoes was performed by a handheld Raman spectrometer equipped with a 1064 nm excitation laser, and data analysis was performed using soft independent modelling of class analogy (SIMCA)), artificial neural network (ANN), and partial least squares regression (PLSR) for classification and quantification purposes. High-performance liquid chromatography (HPLC) and UV/visible spectrophotometry were used for profiling and quantification of major carotenoids. Seven groups were identified based on their carotenoid profile, and supervised classification by SIMCA and ANN clustered samples with 93% and 100% accuracy based on a validation test data, respectively. All-trans-lycopene and ß-carotene levels were measured with a UV-visible spectrophotometer, and prediction models were developed using PLSR and ANN. Regression models developed with Raman spectra provided excellent prediction performance by ANN (rpre = 0.9, SEP = 1.1 mg/100 g) and PLSR (rpre = 0.87, SEP = 2.4 mg/100 g) for non-invasive determination of all-trans-lycopene in fruits. Although the number of samples were limited for ß-carotene quantification, PLSR modeling showed promising results (rcv = 0.99, SECV = 0.28 mg/100 g). Non-destructive evaluation of tomato carotenoids can be useful for tomato breeders as a simple and rapid tool for developing new varieties with novel profiles and for separating orange varieties with distinct carotenoids (high in ß-carotene and high in cis-lycopene).

In-Situ Screening of Soybean Quality with a Novel Handheld Near-Infrared Sensor.

This study evaluates a novel handheld sensor technology coupled with pattern recognition to provide real-time screening of several soybean traits for breeders and farmers, namely protein and fat quality. We developed predictive regression models that can quantify soybean quality traits based on near-infrared (NIR) spectra acquired by a handheld instrument. This system has been utilized to measure crude protein, essential amino acids (lysine, threonine, methionine, tryptophan, and cysteine) composition, total fat, the profile of major fatty acids, and moisture content in soybeans (n = 107), and soy products including soy isolates, soy concentrates, and soy supplement drink powders (n = 15). Reference quantification of crude protein content used the Dumas combustion method (AOAC 992.23), and individual amino acids were determined using traditional protein hydrolysis (AOAC 982.30). Fat and moisture content were determined by Soxhlet (AOAC 945.16) and Karl Fischer methods, respectively, and fatty acid composition via gas chromatography-fatty acid methyl esterification. Predictive models were built and validated using ground soybean and soy products. Robust partial least square regression (PLSR) models predicted all measured quality parameters with high integrity of fit (RPre ≥ 0.92), low root mean square error of prediction (0.02-3.07%), and high predictive performance (RPD range 2.4-8.8, RER range 7.5-29.2). Our study demonstrated that a handheld NIR sensor can supplant expensive laboratory testing that can take weeks to produce results and provide soybean breeders and growers with a rapid, accurate, and non-destructive tool that can be used in the field for real-time analysis of soybeans to facilitate faster decision-making.

Vibrational Spectroscopy for Identification of Metabolites in Biologic Samples.

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.

Nutrient intake and balancing among female Colobus angolensis palliatus inhabiting structurally distinct forest areas: Effects of group, season, and reproductive state.

Understanding intraspecific behavioral and dietary variation is critical for assessing primate populations' abilities to persist in habitats characterized by increasing anthropogenic disturbances. While it is evident that some species exhibit considerable dietary flexibility (in terms of species-specific plant parts) in relation to habitat disturbance, it is unclear if primates are characterized by similar variation and flexibility regarding nutrient intake. This study examined the effects of group, season, and reproductive state on nutrient intake and balancing in adult female Colobus angolensis palliatus in the Diani Forest, Kenya. During July 2014 to December 2015, estimates of nutrient intake were recorded for eight females from three groups inhabiting structurally and ecologically distinct forest areas differing in tree species composition and density. There were differences in metabolizable energy (ME) and macronutrient intakes among groups, seasons, and reproductive states. Most notably, females inhabiting one of the more disturbed forest areas consumed less ME and macronutrients compared to females in the more intact forest area. Contrary to prediction, females in early lactation consumed significantly less ME and macronutrients compared to non-lactating and late lactation females. Despite differences in macronutrient intake, the relative contribution of macronutrients to ME were generally more conservative among groups, seasons, and reproductive states. Average daily intake ratios of non-protein energy to available protein ranged from approximately 3.5:1-4.3:1 among groups. These results indicate that female C. a. palliatus demonstrate a consistent nutrient balancing strategy despite significant intergroup differences in consumption of species-specific plant parts. Data from additional colobine species inhabiting different forest types are required to assess the extent to which nutrient balancing is constrained by phylogeny or is more flexible to local ecological conditions.

Quantifying soluble carbohydrates in tropical leaves using a portable mid-infrared sensor: Implications for primate feeding ecology.

Identifying the nutritional composition of food items has significant ramifications for primate feeding ecology, which, in turn, influences investigations of primate sociality, cognition, and conservation. The aim of our study was to analyze water soluble carbohydrate (WSC) concentrations in the leaves of trees common to the Diani Forest of Kenya. Many of these leaves are consumed by black and white colobus monkeys (Colobus angolensis palliatus). We assessed whether the infrared spectral data collected using a portable spectrometer can be used to accurately predict WSC concentrations. WSC content was first quantified using the phenol-sulfuric acid method for young and mature leaves of 24 species and ranged from 1.15% to 9.16% dry weight. Spectral data were recorded with a spectrometer equipped with an attenuated total reflectance accessory (Agilent Cary 630) and analyzed using partial least squares regression. The spectral region from 1600 cm(-1) to 1000 cm(-1) gave unique polysaccharide bands associated with carboxyl, acetyl, and glycosidic linkages of sugar residues. The multivariate analysis gave excellent performance parameters with correlation coefficient (r(2) ) of 0.95 and standard error of cross-validation of 0.6% WSC. We found that IR spectroscopy provides a rapid and accurate technique for analyzing WSC concentrations and offers primatologists many advantages over wet chemistry methods for analyzing nutritional composition. Am. J. Primatol. 78:701-706, 2016. © 2016 Wiley Periodicals, Inc.

A pilot study of health and wellness coaching for fibromyalgia.

BACKGROUND: The purpose of this study was to test the hypothesis that a health and wellness coaching (HWC)-based intervention for fibromyalgia (FM) would result in sustained improvements in health and quality of life, and reductions in health care utilization. METHODS: Nine female subjects meeting American College of Rheumatology criteria for a diagnosis of primary FM were studied. The HWC protocol had two components, which were delivered telephonically over a twelve-month period. First, each patient met individually with a coach during the 12 month study at the patient's preference of schedule and frequency (Range:22-32 × 45-min sessions). Coaches were health professionals trained in health and wellness coaching tasks, knowledge, and skills. Second, each patient participated in bimonthly (first six months) and monthly (second six months) group classes on self-coaching strategies during the 12 month study. Prior to the intervention, and after 6 months and 12 months of coaching, the Revised Fibromyalgia Impact Questionnaire (FIQR) was used to measure health and quality of life, and the Brief Pain Inventory-Short Form (BPI) was used to measure pain intensity and interference with function. Total and rheumatology-related health encounters were documented using electronic medical records. Data were analyzed using repeated measures ANOVA. RESULTS: All nine patients finished the HWC protocol. FIQR scores improved by 35 % (P = 0.001). BPI scores decreased by 32 % overall (P = 0.006), 31 % for severity (P = 0.02), and 44 % for interference (P = 0.006). Health care utilization declined by 86 % (P = 0.006) for total and 78 % (P < 0.0001) for rheumatology-related encounters. CONCLUSION: The HWC program added to standard FM therapy produced clinically significant improvements in quality of life measures (FIQR), pain (BPI), and marked reductions in health care utilization. Such improvements do not typically occur spontaneously in FM patients, suggesting that HWC deserves further consideration as an intervention for FM.

Application of infrared portable sensor technology for predicting perceived astringency of acidic whey protein beverages.

Formulating whey protein beverages at acidic pH provides better clarity but the beverages typically develop an unpleasant and astringent flavor. Our aim was to evaluate the application of infrared spectroscopy and chemometrics in predicting astringency of acidic whey protein beverages. Whey protein isolate (WPI), whey protein concentrate (WPC), and whey protein hydrolysate (WPH) from different manufacturers were used to formulate beverages at pH ranging from 2.2 to 3.9. Trained panelists using the spectrum method of descriptive analysis tested the beverages providing astringency scores. A portable Fourier transform infrared spectroscopy attenuated total reflectance spectrometer was used for spectra collection that was analyzed by multivariate regression analysis (partial least squares regression) to build calibration models with the sensory astringency scores. Beverage astringency scores fluctuated from 1.9 to 5.2 units and were explained by pH, protein type (WPC, WPI, or WPH), source (manufacturer), and their interactions, revealing the complexity of astringency development in acidic whey protein beverages. The WPC and WPH beverages showed an increase in astringency as the pH of the solution was lowered, but no relationship was found for WPI beverages. The partial least squares regression analysis showed strong relationship between the reference astringency scores and the infrared predicted values (correlation coefficient >0.94), giving standard error of cross-validation ranging from 0.08 to 0.12 units, depending on whey protein type. Major absorption bands explaining astringency scores were associated with carboxylic groups and amide regions of proteins. The portable infrared technique allowed rapid prediction of astringency of acidic whey protein beverages, providing the industry a novel tool for monitoring sensory characteristics of whey-containing beverages.

Understanding Tomato Peelability.

Approximately 75% of all tomatoes in the United States are consumed as processed and 25% as fresh. One of the first steps during processing involves removal of the peel and, unfortunately, more than 25% of the fruits (as measured by total weight) can be lost due to overpeeling. Additionally, conventional peeling applications have a negative environmental impact. Given the great potential economic benefits, many scientists have conducted research to attempt optimizing or predicting peeling performance when processing tomatoes. The literature regarding tomato peelability is contradictory in many cases; and several topics have been subject to ample debate over the years. Divergent conclusions are probably not due to faulty investigations, but rather to the extreme variability found among tomato cultivars, the effect of growing seasons, and maybe even the effect of climatic conditions on the day of harvest or during transportation to the processing plants. This review provides an in-depth background needed for a better understanding of tomato physiology, maturation, and composition, as these could possibly influence the ease of peeling or "peelability." The research studies directly involved with peeling tomatoes and predicting peelability are discussed in this paper as well. Different peeling methods, peeling grading scales, and fruit tagging procedures are presented, as well as experiments evaluating the effect that fruit defects, maturity, growing conditions, and other factors can have on the ease of peeling. Novel approaches for peelability prediction by means of spectroscopic and magnetic resonance technology are also discussed in this review.

Application of a portable infrared instrument for simultaneous analysis of sugars, asparagine and glutamine levels in raw potato tubers.

The level of reducing sugars and asparagine in raw potatoes is critical for potato breeders and the food industry for production of commonly consumed food products including potato chips and French fries. Our objective was to evaluate the use of a portable infrared instrument for the rapid quantitation of major sugars and amino acids in raw potato tubers using single-bounce attenuated total reflectance (ATR) and dial path accessories as an alternative to time-consuming chromatographic techniques. Samples representing a total of 84 experimental and commercial potato varieties harvested in two consecutive growing seasons (2012 and 2013) were used in this study. Samples had wide ranges of sugars determined by HPLC-RID (non-detectable (ND)-7.7 mg glucose, ND-9.4 mg fructose and 0.4-5.4 mg sucrose per 1 g fresh weight), and asparagine and glutamine levels determined by GC-FID (0.7-2.9 mg and 0.3-1.7 mg per 1 g fresh weight). Infrared spectra collected from 64 varieties were used to create partial least squares regression (PLSR) calibration models that predicted the sugar and amino acid levels in an independent set of 16 validation potato varieties. Excellent linear correlations between infrared predicted and reference values were obtained. PLSR models had a high correlation coefficient of prediction (rPred >0.95) and residual predictive deviation (RPD) values ranging between 3.1 and 5.5. Overall, the results indicated that the models could be used to simultaneously predict sugars, free asparagine and glutamine levels in the raw tubers, significantly benefiting potato breeding, certain aspects of crop management, crop production and research.

Nondestructive and Rapid Screening of Aflatoxin-Contaminated Single Peanut Kernels Using Field-Portable Spectroscopy Instruments (FT-IR and Raman).

A nondestructive and rapid classification approach was developed for identifying aflatoxin-contaminated single peanut kernels using field-portable vibrational spectroscopy instruments (FT-IR and Raman). Single peanut kernels were either spiked with an aflatoxin solution (30 ppb-400 ppb) or hexane (control), and their spectra were collected via Raman and FT-IR. An uHPLC-MS/MS approach was used to verify the spiking accuracy via determining actual aflatoxin content on the surface of randomly selected peanut samples. Supervised classification using soft independent modeling of class analogies (SIMCA) showed better discrimination between aflatoxin-contaminated (30 ppb-400 ppb) and control peanuts with FT-IR compared with Raman, predicting the external validation samples with 100% accuracy. The accuracy, sensitivity, and specificity of SIMCA models generated with the portable FT-IR device outperformed the methods in other destructive studies reported in the literature, using a variety of vibrational spectroscopy benchtop systems. The discriminating power analysis showed that the bands corresponded to the C=C stretching vibrations of the ring structures of aflatoxins were most significant in explaining the variance in the model, which were also reported for Aspergillus-infected brown rice samples. Field-deployable vibrational spectroscopy devices can enable in situ identification of aflatoxin-contaminated peanuts to assure regulatory compliance as well as cost savings in the production of peanut products.

Early Diagnosis of Fibromyalgia Using Surface-Enhanced Raman Spectroscopy Combined with Chemometrics.

Fibromyalgia (FM) is a chronic muscle pain disorder that shares several clinical features with other related rheumatologic disorders. This study investigates the feasibility of using surface-enhanced Raman spectroscopy (SERS) with gold nanoparticles (AuNPs) as a fingerprinting approach to diagnose FM and other rheumatic diseases such as rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), osteoarthritis (OA), and chronic low back pain (CLBP). Blood samples were obtained on protein saver cards from FM (n = 83), non-FM (n = 54), and healthy (NC, n = 9) subjects. A semi-permeable membrane filtration method was used to obtain low-molecular-weight fraction (LMF) serum of the blood samples. SERS measurement conditions were standardized to enhance the LMF signal. An OPLS-DA algorithm created using the spectral region 750 to 1720 cm-1 enabled the classification of the spectra into their corresponding FM and non-FM classes (Rcv > 0.99) with 100% accuracy, sensitivity, and specificity. The OPLS-DA regression plot indicated that spectral regions associated with amino acids were responsible for discrimination patterns and can be potentially used as spectral biomarkers to differentiate FM and other rheumatic diseases. This exploratory work suggests that the AuNP SERS method in combination with OPLS-DA analysis has great potential for the label-free diagnosis of FM.

Surface-Enhanced Raman Spectroscopy Combined with Multivariate Analysis for Fingerprinting Clinically Similar Fibromyalgia and Long COVID Syndromes.

Fibromyalgia (FM) is a chronic central sensitivity syndrome characterized by augmented pain processing at diffuse body sites and presents as a multimorbid clinical condition. Long COVID (LC) is a heterogenous clinical syndrome that affects 10-20% of individuals following COVID-19 infection. FM and LC share similarities with regard to the pain and other clinical symptoms experienced, thereby posing a challenge for accurate diagnosis. This research explores the feasibility of using surface-enhanced Raman spectroscopy (SERS) combined with soft independent modelling of class analogies (SIMCAs) to develop classification models differentiating LC and FM. Venous blood samples were collected using two supports, dried bloodspot cards (DBS, n = 48 FM and n = 46 LC) and volumetric absorptive micro-sampling tips (VAMS, n = 39 FM and n = 39 LC). A semi-permeable membrane (10 kDa) was used to extract low molecular fraction (LMF) from the blood samples, and Raman spectra were acquired using SERS with gold nanoparticles (AuNPs). Soft independent modelling of class analogy (SIMCA) models developed with spectral data of blood samples collected in VAMS tips showed superior performance with a validation performance of 100% accuracy, sensitivity, and specificity, achieving an excellent classification accuracy of 0.86 area under the curve (AUC). Amide groups, aromatic and acidic amino acids were responsible for the discrimination patterns among FM and LC syndromes, emphasizing the findings from our previous studies. Overall, our results demonstrate the ability of AuNP SERS to identify unique metabolites that can be potentially used as spectral biomarkers to differentiate FM and LC.