Journal of Raman Spectroscopy最新文献

The paint coatings of two scrap metal sculptures by Gonçalo Mabunda were analyzed using XRF, μRaman, and FTIR spectroscopies. These complementary techniques were employed to investigate the materials and degradation processes of recycled paint-coated metals in an art context, an emerging field for material and preservation studies. XRF and μRaman spectroscopies offered detailed insights into the composition and degradation mechanisms of modified industrial materials. These analyses enabled the identification and characterization of several pigments, including phthalocyanine blue and green, chrome yellow and yellow-orange, titanium white, red iron oxide, monoazo red, and yellow goethite, typically used in industrial paint coatings. Alkyd and barium sulfate were identified as the binder and filler in most paint coatings using XRF and FTIR spectroscopies. The stratigraphic study of the paint samples revealed a unique multilayer coating—a silica-lined retroreflective sheet—previously uncharacterized in conservation studies. The analysis of this material offers important insights that may contribute to the conservation of artworks utilizing retroreflective sheets. The combination of colorimetry, ATR-FTIR, and μRaman provided valuable information on the degradation of paint coatings when exposed to heat during the sculptures' assembly process. Results indicate that while a coating's binder may begin to degrade at certain temperatures, the pigment may remain stable. These findings lay the groundwork for a collaborative database on the composition of paint coatings and the definition of effective conservation procedures for painted scrap metals in art and industrial heritage contexts. Overall, this study demonstrates the utility of Raman spectroscopy in conservation research for industrial materials that have been repurposed as art, and its importance in advancing understanding in this novel and rapidly growing area.

Raman spectroscopy is a non-destructive optical detection technique providing fingerprints of molecular vibrations. For on-site detection, the handheld spectrometers require high excitation laser power to overcome the Johnson noises in the detector, leading to deterioration of analyte molecules. In this work, a general deep learning algorithm of neural network integrating residual networks and U-Net (i.e., ResUNet) is developed for reconstruction of Raman spectra from the low-power handheld spectrometer. The deep learning model is trained by a predesigned Raman spectral dataset from only 30 substances covering the full detective wavenumbers of the spectrometer. Two criteria, termed as mean squared error (MSE) and structure similarity index measure (SSIM), are employed to evaluate the training performance. The denoising performance via the ResUNet model is superior to the traditional Savitzky–Golay filter and wavelet transform algorithms, with the MSE down to 0.003038 and the SSIM up to 0.7124, respectively. The F1-score is further employed to evaluate the reconstructed Raman peaks with true positives, false positives, and false negatives, by which the optimized ResUnet model achieves 77.23%. The trained model is able to reduce the excitation laser power by 28-fold in spectral acquisition and well-reconstruct major Raman characteristic peaks, avoiding the potential damage of analyte molecules using handheld spectrometers. The present work paves a new way to upgrade the low-cost handheld spectrometers achieving high sensitivity for on-site detection.

Removing fluorescence from the Raman spectra of biological samples poses significant challenges because of the diverse baseline variations. Current methods often fail to accurately remove fluorescence, compromising the results of Raman spectroscopy analyses. We introduce a novel iterative numerical optimization method using Zernike polynomials (INOM-ZP) as its foundation. By integrating the minimum point algorithm (MPA) with a genetic algorithm (GA), our approach minimizes an objective function in the real domain, avoiding premature convergence to local optima and achieving solutions close to the global minimum. This method addresses the pressing need for robust fluorescence removal techniques, thereby enhancing the accuracy of Raman spectroscopy analysis. The INOM-ZP algorithm optimally fits Zernike polynomials to experimental data associated with fluorescence, enabling automatic validation of baseline curve reconstruction and quantitative characterization of fluorescence intensity variations. Our method effectively removes fluorescence from Raman spectra across various sample types, representing a significant advancement in fluorescence removal techniques for Raman spectroscopy. The best fluorescence extraction result for a synthetic Raman spectrum in reliability was 0.99 ± 6.8203 × 10⁻⁹, with a precision of 0.00633 ± 2.1092 × 10⁻⁷ and a repeatability of 0.0125 ± 6.9984 × 10⁻⁸. Similarly, for biological spectra, such as bone, the reliability was 0.99 ± 1.835 × 10⁻⁸, with a precision of 0.0577 ± 3.889 × 10⁻⁸ and a repeatability of 0.0775 ± 1.935 × 10⁻⁷.

Pigs' teeth are a valuable model in the field of odontology. They are of particular interest because of their structural resemblance to human teeth. However, the detailed chemical composition and microstructural characteristics of pig dental tissues remain insufficiently explored. This study uses confocal Raman microscopy and energy-dispersive X-ray spectroscopy (EDX) to analyze the enamel and dentin of pig teeth. These tools are suitable to assess how pigs could be a model for human dental research as a model for tooth transplant or that mimic the human teeth. These results were directly compared with data from human teeth under similar experimental conditions. Raman spectroscopy revealed that pig dentin shares many spectral similarities with human dentin, especially in phosphate and carbonate group intensities. However, human dentin exhibited a higher intensity of type B carbonate at 1071 cm⁻¹. In enamel, both species showed similar Raman features. Moreover, pig enamel showed additional organic signals in the 2800–3000 cm⁻¹ region, suggesting a greater organic content. At the dentin-enamel junction (DEJ), pigs displayed a more gradual mineral transition with hypermineralized layers, unlike the sharper DEJ observed in humans. These findings were supported by EDX analysis, which confirmed similar elemental distributions in both species. The results highlight that pig teeth closely resemble human teeth in several key aspects, but the difference at the level of mineralization and organic composition must be considered when using pigs as an animal model for dental research or as a replacement for human teeth. This study emphasizes the utility of pigs in dental research while recognizing the limitations in fully mimicking human dental characteristics.

Accurate wavelength calibration is crucial for the reliable performance of transmission holographic spectrometers, which are widely used in analytical chemistry, materials science, and biomedical research. However, inherent optical aberrations in the focusing components often cause systematic wavelength errors—particularly when the available emission lines from standard calibration sources are unevenly distributed over the measurement range. To address this challenge, we propose a novel error calibration method that leverages the inherent symmetry of aberration-induced errors in transmission optics. Our approach involves generating an error calibration function by combining polynomial fitting with symmetry-based averaging, thereby compensating for systematic residual errors across the spectral range. Experimental validation using two widely employed atomic emission lamps demonstrated that the maximum wavelength calibration error was reduced by 20%–27% compared with conventional calibration methods. This improvement enhances the precision of spectrometric measurements and provides an effective, easily implemented supplement to existing wavelength calibration protocols, especially under challenging spectral coverage conditions.

The interactions of trifluoroethyl methanesulfonate (methylsulfonyl) (TFMSMS), a lipophilic bioactive derivative of Clomesone, with dipalmitoylphosphatidylcholine (DPPC) were investigated. Thermodynamic changes caused by TFMSMS in DPPC lipid bilayers were monitored using differential scanning calorimetry (DSC), FTIR, and Raman spectroscopy. TFMSMS influenced the thermotropic properties of DPPC membranes, abolishing the pretransition, broadening the phase-transition profile, and decreasing the T_m with increasing concentrations. Raman spectroscopy revealed TFMSMS interactions with alkyl chains, altering the order–disorder ratio. A blue shift in the peak at 3036 cm⁻¹ and the appearance of a new band at 3026 cm⁻¹ indicated a strong interaction between the choline head group and TFMSMS. FTIR results supported these findings, showing changes in the phosphate and carbonyl groups. Our research highlights the active role of lipids in cellular functions and the potential of TFMSMS in inhibiting pathogenic bacterial growth and biofilm formation.

Chronic kidney disease (CKD) has become a major global public health challenge, affecting over 850 million people, and is expected to become the fifth leading cause of death worldwide by 2040. Among CKD, IgA nephropathy (IgAN) is the most prevalent primary glomerulonephritis globally and a vital cause of end-stage renal disease (ESRD). Despite kidney biopsy being considered the gold standard for the diagnosis of IgAN, it is expensive to detect and has significant trauma. Thus, it is particularly important to create novel IgAN diagnoses that are speedy, precise, and noninvasive. In this study, we introduced an innovative method of a lightweight mixture of experts, Deep separable convolution–Mixture of Experts–RegNet (Dsc–MoE–RegNet), to diagnose IgAN based on Raman spectra of serum and tears, which utilizes not only the ability of a mixture of experts to optimize performance but also the ability of deep separable convolution to enhance efficiency, achieving a balance between model performance and complexity. Experimental evidence indicates that the Dsc–MoE–RegNet model has 100% accuracy, sensitivity, and specificity for the classification of IgAN patients and healthy controls with an AUC value of 1, outperforming popular machine learning and deep learning methods. This study demonstrated the immense promise of combining serum and tear-based Raman spectroscopy with the Dsc–MoE–RegNet method for promptly and precisely identifying patients with IgAN.