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.

A novel approach using portable Fourier transform infrared spectroscopy (FT-IR) with an attenuated total reflectance accessory was developed to screen the quality of Pisco distillates. Pisco samples (n = 188) were obtained from distilleries in Peru, and their quality was determined by gas chromatography. FTIR discriminated the three distillate fractions (head, body, and tail) of Pisco, identifying their alcohol fingerprint (3091-2660 cm^-1 and 1190-821 cm^-1) as contributing to the class differentiation. Partial least squares regression showed strong correlations (R_pre = 0.93-1.00) and low errors of prediction (RMSEP) in quantifying 6 key quality components (ethanol, methanol, acetaldehyde, acetic acid, furfural, and titratable acidity). This approach offers a scalable alternative to conventional empirical methods, enabling distilleries to make precise distillation cuts, reduce production costs, and ensure consistent product quality. This is the first report of a portable FT-IR system capable of in-line monitoring of key quality parameters in the spirit distilleries.

In this study, we introduced a portable spatially offset Raman spectroscopy (SORS) system that permits rapid, non-destructive acquisition of Raman spectra from bottled Pisco spirits, providing a practical and non-invasive solution for in situ quality control. Pisco, a Peruvian distilled spirit, was selected as a case study because methanol occurs naturally during fermentation and distillation, and the product is susceptible to adulteration. Methanol and ethanol levels in Pisco were determined using gas chromatography (GC–FID). Methanol levels in 94 authentic Pisco samples ranged from 7.4 to 67 mg/100 mL, remaining below the regulatory limits established for fruit brandies. For pure Pisco samples, the handheld SORS device demonstrated strong predictive performance for determining methanol (SEP = 0.003%, Rpre = 0.92) and ethanol (SEP = 1.25%, Rpre = 0.98) content. To further assess model applicability across a broader methanol concentration range, randomly selected Pisco samples were fortified with methanol (0.11–9.85%), resulting in a prediction model with excellent performance for methanol quantification (SEP = 0.17%; Rpre = 0.995). Overall, the SORS-based approach showed robust analytical capability, underscoring its potential as a non-contact, non-destructive technique for rapid quantification of methanol and ethanol in sealed glass containers.