Applied Spectroscopy最新文献

The dynamic spectroscopic method, as a noninvasive blood component measurement method, currently uses spectrometers as the main measurement instrument. However, spectrometers have limited accuracy in measuring light intensity at each wavelength, which restricts the measurement accuracy of the dynamic spectrum method. In this paper, a combination of a multispectral camera and a spectrometer is utilized for the first time to measure spectral photoplethysmography (PPG) signals. Both the high amplitude resolution and high accuracy of the multispectral camera in terms of sampling values and the advantage of the spectrometer in terms of the number of wavelengths are exploited. According to the experimental data, this method effectively improves the measurement results. In particular, when measuring for hemoglobin, the mean absolute percentage error (MAPE) decreased by 25.3% and 22.9%, respectively compared with a single spectrometer and a multispectral camera. For platelet measurements, the MAPE decreased by 28.9% and 22.8%, respectively. For total bilirubin measurements, the MAPE decreased by 14.5 and 26.3%, respectively. It demonstrates that the noninvasive blood component measurement method of a combined multispectral camera and spectrometer can effectively reduce the interference of non-target components and improve measurement accuracy.

Raman microscopes are widely used in various fields and their spectral resolutions differ greatly depending on the system and optical components. Thus, it is important to evaluate the spectral resolution of Raman systems under measurement conditions. Although calcite is a crystal with a trigonal structure and its narrow peak at ∼1086 cm^-1 has been used to evaluate the spectral resolution of Raman spectrometers, the peak width of calcite itself, ∼1.3 cm^-1 at full width half-maximum (FWHM), is not negligible under high spectral resolution conditions. Because the calcite peak at ∼1086 cm^-1 originates from symmetric stretching, which is a common vibration mode for carbonate salts, we examined strontium carbonate (SrCO₃), barium carbonate (BaCO₃), and lead carbonate (PbCO₃) reagents to find a material having a narrower peak width than calcite. SrCO₃, BaCO₃, and PbCO₃ peaks originating from symmetric stretching were observed at 1072, 1059, and 1054 cm^-1, respectively, and their peak widths at FWHM (0.67, 0.92, and 1.09 cm^-1, respectively) were narrower than that of calcite (1.36 cm^-1). The narrow peak width of SrCO₃ was strongly dependent on its purity, probably due to its high structural regularity, and the change in the peak width (FWHM) was only 0.12 cm^-1 between 5 °C and 45 °C. Thus, we concluded that the high-purity SrCO₃ peak at 1072 cm^-1 was the narrowest peak of Raman scattering light under ambient conditions and is suitable for evaluating high spectral resolution for Raman spectrometers.

Fiber-based Raman spectroscopy enhances the Raman signal by maximizing the overlap of the optical field and the gas species. However, filling the hollow-core fiber (HCF) with gas requires time that is dependent on the fiber core diameter, fiber length, and pressure of the gas. At ambient pressure, the fiber gas uptake is driven by diffusion into the fiber ends, severely limiting the response time of the system. By laser drilling access holes to the core along the length of the fiber, the uptake time of the gas is reduced, improving the system response time. In this work, we study the carbon dioxide (CO₂) sensor dynamics based on Raman signal intensity generated in HCFs. The signal intensity versus gas concentration is characterized by controlling the CO₂ concentration in the surrounding environment of the fiber. Next, we characterize the gas uptake time in HCFs as a function of fiber length. Finally, we optimize the access hole configuration along the fiber, demonstrating reduced sensor uptake time by a factor of three.

Poly(3-hydrxybutyrate-co-3-hydroxyhexanoate) (PHBHx) is a biopolymer that is produced and degraded by microbes. Because of the potential to replace polymers derived from petrochemicals with these materials, there is a high level of expectation for its commercial uses if its physical and chemical properties can be understood and controlled. Among other things these properties are determined by the polymer's morphology - that is its crystallinity, and orientation of both crystalline and amorphous phases. The focus on the Raman characteristics of the crystalline phase enables elucidation of the characteristics of the polymer experiencing dynamic crystallization under various conditions. In this article we will start by reviewing the changes in the Raman spectrum from an amorphous to a crystalline material in an isothermal crystallization study. In that study a correlation field splitting between a CH stretching band that interacts with the carbonyl group on the opposite chain in the unit cell was identified. Then we will show the polarized Raman spectra of single crystals which enable an explanation of the residual amorphous material seen in the spectra of single crystals. Using the information from the single crystal measurements we can then study the Raman behavior of spherulites and confirm the model that proposes an explanation for the appearance of rings in the polarized light microscope (PLM) images of some spherulites. The polarized Raman studies confirm that the crystal ribbons that grow along the radii are twisting about the growth direction. The two-dimensional correlation spectroscopy (2D-COS) analysis of the polarized spectra of spherulites suggest the presence of strain that has been proposed to induce the twisting.

Non-melanoma skin tumors, mainly basal cell carcinoma and squamous cell carcinoma, are the most common human cancers. Early detection and discrimination of skin tumors is of paramount importance to decision making and treatment. The main treatment for these skin tumors is surgical excision, but its extent is strongly influenced by the preoperative diagnosis. This study presents a new method for skin tumor discrimination based on tumor oxygenation levels extracted from hyperspectral images. Hyperspectral images of 16 skin tumors (four actinic keratoses, six basal cell carcinomas, six squamous cell carcinomas) were obtained prior excision and pathological diagnosis. The concentrations of oxyhemoglobin, deoxyhemoglobin and oxygen saturation levels were measured from hyperspectral images using an algorithm based on the modified Beer-Lambert law. The results were compared with pathology diagnosis. The results revealed that there were statistically significant differences in the mean oxyhemoglobin concentrations and oxygen saturation levels between actinic keratoses and basal cell carcinomas, between basal cell carcinomas and squamous cell carcinomas and between actinic keratoses and squamous cell carcinomas. Deoxyhemoglobin concentrations were not statistically different between the two carcinoma types but were different between carcinomas and actinic keratoses. In conclusion, the proposed method proved that it could be used as a reliable non-invasive diagnostic tool for differentiating benign from malignant skin tumors with the possibility of extending its applications to other medical research areas.

We have begun introducing complex-valued principal component regression (PCR) into spectroscopy. Unlike traditional methods that are constrained to either the real or imaginary axis, this approach allows principal components (PCs) to span the entire complex plane. While this added flexibility enhances modeling capabilities, it also introduces challenges, as existing tools often fail to identify optimal solutions. To address this, we explored two different strategies for computing eigenvectors. The most natural approach is to apply singular value decomposition (SVD) directly to the matrix of complex refractive index spectra. As an alternative, we combined the eigenvectors of the imaginary parts determined by SVD with their Kramers-Kronig transforms, which resulted in 2^N possible superpositions for N PCs. Although the optimal solution may still be unknown, the proposed second method for complex-valued PCR consistently outperformed conventional PCR in the systems investigated. This highlights its potential to enhance data analysis in infrared and Raman spectroscopy.

Understanding the biochemical aging mechanisms of bloodstains is essential for developing reliable forensic methods to estimate the time since deposition (TSD). Although fluorescence spectroscopy is effective for tracking endogenous fluorophores such as tryptophan, nicotinamide adenine dinucleotide (NADH), and flavins, its utility is limited by spectral overlap and sample variability. In this study, we employed two-dimensional correlation spectroscopy (2D-COS) and 2D gradient mapping method to investigate the time-dependent fluorescence changes in bloodstains, gaining molecular-level insights into the aging process. 2D-COS uncovered hidden spectral components and revealed sequential molecular changes, especially in NADH- and flavin-associated bands. The 2D gradient maps further visualized these spectral trends quantitatively over 24 hours of aging. This study focuses on uncovering the biochemical mechanisms underlying bloodstain aging, probed by fluorescence spectroscopy. These findings deepen our fundamental understanding of ex vivo blood degradation and establish a foundation for more accurate and robust forensic applications.

Discrete frequency infrared (IR) imaging is an exciting experimental technique that has shown promise in various applications in biomedical science. This technique often involves acquiring IR absorptive images at specific frequencies of interest that enable pathologically relevant chemical contrast. However, certain applications, such as tracking the spatial variations in protein secondary structure of tissue specimens, necessary for the characterization of neurodegenerative diseases, require deeper analysis of spectral data. In such cases, the conventional analytical approach involves band fitting the hyperspectral data to extract the relative populations of different structures through their fitted areas under the curve (AUC). While Gaussian spectral fitting for one spectrum is viable, expanding that to an image with millions of pixels, as often applicable for tissue specimens, becomes a computationally expensive process. Alternatives like principal component analysis (PCA) are less structurally interpretable and incompatible with sparsely sampled data. Furthermore, this detracts from the key advantages of discrete frequency imaging by necessitating the acquisition of more finely sampled spectral data that is optimal for curve fitting, resulting in significantly longer data acquisition times, larger datasets, and additional computational overhead. In this work, we demonstrate that a simple two-step regressive neural network model can be utilized to mitigate these challenges and employ discrete frequency imaging for retrieving the results from band fitting without significant loss of fidelity. Our model reduces the data acquisition time nearly six-fold by requiring only seven wavenumbers to accurately interpolate spectral information at a higher resolution and subsequently using the upscaled spectra to accurately predict the component AUCs, which is more than 3000 times faster than spectral fitting. Our approach thus drastically cuts down the data acquisition and analysis time and predicts key differences in protein structure that can be vital towards broadening potential applications of discrete frequency imaging.

Rocks are an extremely important and indispensable part of the Earth's crust, with wide applications in various fields such as geology, environmental monitoring, and industry. Traditional methods often rely on a single analytical technique or visual inspection, but this may not achieve the accuracy required for thorough classification. Laser-induced breakdown spectroscopy (LIBS) technology mainly provides information on the composition and content of rock elements, while images can provide appearance information such as color and texture. The multilayer perceptron (MLP) and DenseNet121 models were selected for processing preprocessed LIBS and image data, respectively. When using LIBS and images separately for classification, the accuracy rates were 93.63% and 90.90%, respectively. However, after fusing the bimodal data using LIBS and images, we achieved a significant performance improvement of 97.27% in accuracy. This study indicates that advanced neural network models can effectively integrate LIBS and image data and improve the performance of rock classification.

Proteins are biomolecules with characteristic three-dimensional (3D) arrangements that render them different vital functions. In the last 20 years, there has been a growing interest in biopharmaceutical proteins, especially antibodies, due to their therapeutic application^. The functionality of a protein depends on the preservation of its native form, which under certain stressing conditions can undergo changes at different structural levels that cause them to lose their activity.¹ Although mass spectrometry is a powerful technique for primary structure determination, it often fails to give information at higher order levels. Like infrared (IR), Raman spectra are well known to contain bands (especially the amide I from 1625-1725cm^-1) that correlate with secondary structure (SS) content. However, unlike circular dichroism (CD), the most well-established technique for SS analysis, Raman spectroscopy allows a much wider ranges of optical density, making possible the analysis of highly concentrated samples with no prior dilution. Moreover, water is a weak scatterer below 3000 cm^-1, which confers Raman an advantage over IR for the analysis of complex aqueous pharmaceutical samples as the signal from water dominates the amide I region. The most traditional procedure to extract information on SS content is band-fitting. However, in most cases, we found the method to be ambiguous, limited by spectral noise and subjected to the judgment of the analyzer. Self-organizing maps (SOM) is a type of self-learning algorithm that organizes data in a two-dimensional (2D) space based on spectral similarity and class with no bias from the analyzer and very little effect from noise. In this work, a set of protein spectra with known SS content were collected in both solid and aqueous state with back-scatter Raman spectroscopy and used to train a SOM algorithm for SS prediction. The results were compared with those by partial least squares (PLS) regression, band-fitting, and X-ray data in the literature. The prediction errors observed by SOM were comparable to those by PLS and far from those obtained by band-fitting, proving Raman-SOM as viable alternative to the aforementioned methods.