Applied Spectroscopy最新文献

Laser-induced breakdown spectroscopy (LIBS) is a rapid method for detecting total iron (TFe) content in iron ores. However, accuracy and measurement error of univariate regression analysis in LIBS are limited due to factors such as laser energy fluctuations and spectral interference. To address this, multiple regression analysis and feature selection/extraction are needed to reduce redundant information, decrease the correlation between variables, and quantify the TFe content of iron ores accurately. Overall, 339 batches of iron ore samples from five countries were obtained from the ports of China during the discharging, and 2034 representative spectra were collected. A convolutional neural network (CNN) model for total iron content prediction in iron ores is established. The performance of variable importance random forest (VI-RF), variable importance back propagation artificial neural network (VI-BP-ANN), and CNN-assisted LIBS in predicting the TFe content of iron ores was compared. Coefficient of determination (R²), root mean square error (RMSE), mean relative error (MRE), and modeling time were selected for model evaluation. The result shows that variable importance significantly enhances the quantitative accuracy and reduces modeling time compared to traditional BP-ANN and RF models. Moreover, the CNN model outperformed manual feature selection methods (VI-BP-ANN and VI-RF), exhibiting the shortest modeling time, highest R², lowest RMSE, and MRE. CNN model's unique characteristics, such as weight sharing and local connection, make it well suited for analyzing high-dimensional LIBS data in multivariate regression analysis. Our approach demonstrates the effectiveness of machine learning and deep learning approaches in improving the accuracy of LIBS for TFe content prediction in iron ores. CNN-assisted LIBS method holds great potential for practical applications in the mining industry.

Quantum cascade lasers (QCLs) and interband cascade lasers (ICLs) are widely used as light sources in tunable laser absorption spectroscopy because they emit in the mid-infrared region where many strong and characteristic absorption bands are present. In this paper, we compare the performance of these lasers emitting at about 2310.1 cm^-1 to determine an optimal light source for detecting isotopic ratios of carbon dioxide (CO₂). Our results show that the QCL has a higher relative intensity noise of up to 15 dBc/Hz compared to the ICL over the entire measured frequency range. In addition, it has a higher frequency fluctuation. However, the maximum tuning range of the QCL is up to 5.2 cm^-1 compared to up to 3.8 cm^-1 for the ICL. Both lasers lose more than half of their tuning range when the tuning rate is increased to 10 kHz. When measuring the isotope ratio of CO₂, an uncertainty in the ${\delta }^{13}$ value of ${\sigma }_{13C,\min }^{\mathrm{ICL}}=0.17$‰ was achieved with the ICL and of ${\sigma }_{13C,\min }^{\mathrm{QCL}}=0.42$‰ with the QCL, both at an integration time of 0.2 s. In summary, the QCL is more appropriate for applications that require a larger spectral tuning range, such as the measurement of a complex gas mixture, while the ICL has an excellent signal-to-noise ratio and is therefore better suited for applications that require higher precision.

In this paper, a single-step synthetic approach is presented for the development of bimetallic molybdenum-copper nanoclusters (Mo-CuNCs), shielded by a small molecule 6-thioguanine (6-TG). The Mo-CuNCs possessed a small size, high fluorescence, stable behavior, and good solubility in water. The 6-TG-Mo-CuNCs exhibit strong blue fluorescence emission at 410 nm after exciting at 330 nm as compared to its monometallic nanoclusters. Utilizing 6-TG-Mo-CuNCs superior biochemical stability, uric acid (UA) can be specifically detected as an oxidative stress biomarker using an inner filter effect mechanism. The probe demonstrated good sensing capability for detecting UA within the range of 0.09-5.00 μM and a detection limit of 0.237 μM. The method feasibility is further validated by quantifying UA in urine and plasma samples.

Ferroptosis, a regulated form of cell death driven by oxidative stress and lipid peroxidation, has emerged as a pivotal research focus with implications across various cellular contexts. In this study, we employed a multifaceted approach, integrating label-free Raman spectroscopy and microfluidics to study the mechanisms underpinning ferroptosis. Our investigations included the ferroptosis initiation based on the changes in the lipid Raman band at 1436 cm^-1 under different cellular states, the generation of reactive oxygen species (ROS), lipid peroxidation, DNA damage/repair, and mitochondrial dysfunction. Importantly, our work highlighted the dynamic role of vital cellular components, such as nicotinamide adenine dinucleotide phosphate hydrogen (NADPH), ferredoxin clusters, and other key factors such as glutathione peroxidase 4 and nuclear factor erythroid 2, which collectively influenced cellular responses to redox imbalance and oxidative stress. Quantum mechanical (QM) and molecular docking simulations (MD) provided further evidence of interactions between the ferredoxin (containing 4Fe-4S clusters), NADPH, and ROS, which led to the production of reactive Fe species in the cells. As such, our approach not only offered a real-time, multidimensional perspective on ferroptosis but also provided valuable methods and insights for therapeutic interventions in diverse biomedical contexts.

Extraction of components from individual refinery streams (e.g., reformates and alkylates) in finished gasoline was undertaken using Raman spectroscopy to characterize the chemical content of the finished product. Modified alternating least squares (MALS) was used for separating Raman spectroscopic data sets of the finished product into its pure individual components. The advantages of MALS over alternating least squares (ALS) for multicomponent resolution are highlighted in this study using three Raman spectroscopic data sets which provide a suitable benchmark for comparing the performance of these two methods. MALS is superior to ALS in terms of accuracy and can better resolve components than ALS, and it is also more robust toward collinear data. Finally, components near the noise level usually cannot be extracted by ALS because of instability when inverting the covariance structure which inflates the noise present in the data. However, these same components can be extracted by MALS due to the stabilization of the least squares regression with respect to the matrix inversion using modified techniques from ridge regression.

Analyzing the composition of animal hair fibers in textiles is crucial for ensuring the quality of yarns and fabrics made from animal hair. Among others, Fourier transform infrared (FT-IR) spectroscopy is a technique that identifies vibrations associated with chemical bonds, including those found in amino acid groups. Cashmere, mohair, yak, camel, alpaca, vicuña, llama, and sheep hair fibers were analyzed via attenuated total reflection FT-IR (ATR FT-IR) spectroscopy and scanning electron microscopy techniques aiming at the discrimination among them to identify possible commercial frauds. ATR FT-IR, being a novel approach, was coupled with chemometric tools (partial least squares discriminant analysis, PLS-DA), building classification/prediction models, which were cross-validated. PLS-DA models provided an excellent differentiation among animal hair of both camelids and eight animal species. In addition, the combination of ATR FT-IR and PLS-DA was used to discriminate the cashmere hair from different origins (Afghanistan, Australia, China, Iran, and Mongolia). The model showed very good discrimination ability (accuracy 87%), with variance expression of 94.88% and mean squared error of cross-validation of 0.1525.

The presence of lead has been identified as a critical health risk in drinking water systems serviced by Pb-bearing plumbing. Among several corrosion control strategies, the use of sodium silicates has attracted interest due to the advantages it offers compared to other approaches, such as phosphate dosage. However, the interaction of silicate ions with lead corrosion scales and other ubiquitous dissolved species such as Al ions in drinking water is not well understood. In this work, surface and bulk spectroscopic analysis of the solid scale is combined with quantitative analysis of the aqueous phase. A detailed spectroscopic probing of the transformations taking place on the solid phase enables us to develop a mechanistic framework for reports published in the last four years in the open literature, suggesting that silicates may not be an adequate corrosion control option in drinking water systems rich in solid lead carbonates. The spectroscopic data obtained demonstrate that in the presence of chlorine residual, silicates inhibit Pb(II) carbonates from oxidizing into less soluble Pb(IV) oxides thus, negatively impacting water quality. Furthermore, aluminum ions interact with silicates resulting in the formation of solid allophane phase over the lead scale surface, extending into the bulk. However, the formation of this new solid allophane phase does not protect against lead dissolution.

Archaeological human remains provide key insight into lifestyles, health, and diseases affecting past societies. However, only limited analyses can be conducted without causing damage due to the destructive nature of current technologies. The same problem exists with current clinical analyses of the skeleton, and the preferred advanced imaging techniques only provide macroscopic information. Raman spectroscopy could provide chemical information without detriment to archaeological bone samples and perhaps the need for invasive diagnostic procedures in the future. This study measured archaeological human vertebrae to investigate if chemical differences with aging were detectable with Raman spectroscopy and if differences in mineral chemistry could contribute to information on bone mineral diseases. The three lowest bones of the spine (lumbar vertebrae L3-L5), which are subject to the heaviest loading in life, of nine adults from three age groups (18-25, 25-45, and 45+ years) were provided by the Thornton Abbey Project. Three biomechanically important anatomical locations were selected for analysis; likely sites chosen to measure any chemical changes associated with aging, the vertebral body center and the zygapophyseal joints. Results detected chemical changes associated with aging. These changes relate to the minerals phosphate (∼960 cm^-1) and carbonate (∼1070 cm^-1), which are fundamental to bone function. Overall mineralization was found to increase with aging, but while carbonate increased with age, phosphate increased up to ∼45 years and then declined. These fluctuations were found in all three vertebrae, but were more distinct in L5, particularly in the vertebral body, indicating this is an optimal area for detecting bone mineral chemistry changes with aging. This is the first Raman analysis of bone samples from the historically significant site of Thornton Abbey. Results detected age-related changes, illustrating that ancient remains can be used to enhance understanding of modern diseases and provide information on the health and lifestyle of historic individuals.

Vibrational spectroscopy methods such as mid-infrared (MIR), near-infrared (NIR), and Raman spectroscopies have been shown to have great potential for in vivo biomedical applications, such as arthroscopic evaluation of joint injuries and degeneration. Considering that these techniques provide complementary chemical information, in this study, we hypothesized that combining the MIR, NIR, and Raman data from human osteochondral samples can improve the detection of cartilage degradation. This study evaluated 272 osteochondral samples from 18 human knee joins, comprising both healthy and damaged tissue according to the reference Osteoarthritis Research Society International grading system. We established the one-block and multi-block classification models using partial least squares discriminant analysis (PLSDA), random forest, and support vector machine (SVM) algorithms. Feature modeling by principal component analysis was tested for the SVM (PCA-SVM) models. The best one-block models were built using MIR and Raman data, discriminating healthy cartilage from damaged with an accuracy of 77.5% for MIR and 77.8% for Raman using the PCA-SVM algorithm, whereas the NIR data did not perform as well achieving only 68.5% accuracy for the best model using PCA-SVM. The multi-block approach allowed an improvement with an accuracy of 81.4% for the best model by PCA-SVM. Fusing three blocks using MIR, NIR, and Raman by multi-block PLSDA significantly improved the performance of the single-block models to 79.1% correct classification. The significance was proven by statistical testing using analysis of variance. Thus, the study suggests the potential and the complementary value of the fusion of different spectroscopic techniques and provides valuable data analysis tools for the diagnostics of cartilage health.

Raman spectroscopy is used to monitor the development of live neurons exposed to cytosine arabinoside (ara-C). Ara-C is widely used to culture neurons and exclude non-neuronal cells. In this study, Raman spectra obtained from neurons exposed to ara-C were plotted using an analytical model of neuronal development to evaluate the impact of ara-C on neuronal development. After two days of culturing, neurons were exposed to ara-C for 24 h at final concentrations of 0 (control), 5, and 25 μM. Principal component analysis (PCA) was performed to build an analytical model for evaluating neurodevelopmental disorders caused by ara-C treatment. We projected the Raman spectra obtained from ara-C-treated cells onto the control group dataset. The distribution of PC1 scores for neurons exposed to ara-C at a final concentration of 5 μM was not significantly different from that of the control group. In contrast, under a final concentration of 25 μM, the data population at 10 and 15 days of culturing overlapped significantly with that of neurons at 4 days of normal culturing. These results suggest that Raman spectroscopy can detect very small physiological alterations in the neurons even after a short-term exposure (24 h) of ara-C. Our analytical method has high potential to evaluate the developmental stages for living neurons under exposure to chemicals.