Applied Spectroscopy最新文献

Multivariate regression models were optimized for the quantification of sulfuric acid (H₂SO₄) [0-8 M] and temperature (20 °C-80 °C) in the presence of ammonium sulfate ((NH₄)₂SO₄ [0-0.6 M]) using Raman spectroscopy. Optical vibrational spectroscopy is a useful nondestructive technique for the in situ analysis of complex chemical systems notoriously difficult to monitor in situ and in real-time. Multivariate analysis, a chemometrics method, can be paired with these nondestructive optical methods for determining analyte concentration and speciation in complex solutions, such as dissociated species in polyprotic acids, e.g., H₂SO₄. The effect of temperature is often overlooked although it can have a major influence on speciation and the corresponding Raman spectra. Here, partial least squares regression models were optimized for the quantification of H₂SO₄ and its two deprotonated forms as a function of temperature. Measuring bisulfate as a function of temperature is particularly challenging owing to changes in the second dissociation constant. A designed training set effectively minimized the sample set size and trained a robust predictive model with percent root mean square error of <3% for H₂SO₄. The practical strategy employed here was demonstrated to be effective for building chemometric models that directly account for dynamic temperatures with static samples and is shown to be amenable to flow cell analysis applications with a simple calibration transfer for process monitoring applications.

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.

Liquid biopsy is revolutionizing cancer management, with circulating tumor cells (CTCs), offering a transformative approach to screening, diagnosis, and treatment monitoring. However, existing CTC isolation methods relying on antigen expression or physical properties lack robustness, are operator-dependent, and suffer from automation challenges, leading to inconsistent and time-intensive analyses. A universal, unbiased methodology for CTC detection across tumor types is critically needed. Here, we present the first proof-of-concept study demonstrating the use of Fourier transform infrared (FT-IR) microspectroscopy to study cytospun blood samples coupled with a random forest (RF) classifier, for the detection of a single CTC in the blood of a lung cancer patient as confirmed via immunohistochemistry. Notably, our method utilizes glass coverslips as substrates, routinely employed in pathology departments, enabling seamless integration with histopathological analyses (e.g., staining, immunohistochemistry). Using FT-IR spectral data from in vitro growing lung cancer cells as a training model, we achieved precise CTC identification based on biochemical composition, specifically within the fingerprint region (1800cm^-1 to 1350 cm^-1). This study introduces FT-IR microspectroscopy as a novel, label-free approach for CTCs detection in liquid biopsies, with the potential to redefine cancer diagnostics. By enhancing precision and accessibility in CTC identification, the clinical implementation of this methodology may represent a significant advancement in personalized oncology, offering a clinically viable tool for real-time cancer monitoring and improved patient stratification.

Understanding the abundance of atomic oxygen in the vicinity of carbon surfaces exposed to high-enthalpy flows is critical to accurate predictions of the gas-surface interaction. A novel approach for obtaining absolute number density measurements of atomic oxygen in high-enthalpy facilities with nanosecond laser pulses is described and demonstrated using photoionization-dominated, two-photon laser-induced fluorescence. In two-photon laser-induced fluorescence measurements, the depopulation of the excited state is typically dominated by electronic quenching, which depends on the temperature, pressure, and gas composition. To account for the electronic quenching rate, the fluorescence lifetime can be measured by temporally resolving the fluorescence. This can prove challenging in high-temperature and/or high-pressure environments where the fluorescence lifetime can be less than a nanosecond. Instead, by increasing the laser intensity until photoionization dominates the depopulation of the excited state, we create a quenching-independent measurement that is proportional to absolute number density. This technique is demonstrated here in the reacting boundary layer of a graphite sample ablating in the 6000 K plume of an inductively coupled plasma torch. The boundary layer possesses a large temperature gradient that varies from about 2000 K near the sample surface to the plume temperature of 6000 K in a span of approximately 2 mm. The photoionization-dominated technique is calibrated by using the freestream oxygen concentration, assuming the torch plume is in local thermodynamic equilibrium. The spatial resolution of the measurements is 50 µm and we are able to measure the number density of atomic oxygen to within about 60 µm of the graphite sample.

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.

In this study, we have developed a plasmonic hybrid heterostructure integrating two elements: Two-dimensional (2D) reduced graphene oxide-gold nanostars composite (rGO-GNS), and gold nanostars (GNS) substrate. By harnessing the unique plasmonic properties of rGO in chemical enhancement and that of GNS in electromagnetic enhancement, the hybrid heterostructure offers synergistic enhancement effects that enable ultra-low sensitivity and accurate identification and analysis of trace quantities of target substances. It is noteworthy that the high-density hotspots generated by strong plasmonic coupling of rGO-GNS and GNS results in ultra-high surface-enhanced Raman spectroscopy (SERS) enhancement compared to individual substrate either GNS or rGO-GNS substrate. Moreover, the uniformity and reproducibility of the GNS@rGO-GNS substrate were studied by using thiophenol (TP) as a model analyte, which indicates that the SERS sensor exhibited superior signal reproducibility with an RSD value 5% and long-term stability with a minimal signal loss after 30 days. To demonstrate a potential application of our SERS substrate, SERS detection of the pesticide thiram in river water was realized with a limit of detection (LOD) up to 50 pM, showing the potential for new opportunities for efficient chemical and biological sensing applications.