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.

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.

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) and carbonate (∼1070 cm), 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.

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.

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.

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 to determine an optimal light source for detecting isotopic ratios of carbon dioxide (CO). 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 compared to up to 3.8 cm 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 CO, an uncertainty in the value of ‰ was achieved with the ICL and of ‰ 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.

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.

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.

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 (), 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 , 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.

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 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.

This article provides a detailed discussion of the evidence available to date on the application of laser-induced breakdown spectroscopy (LIBS) and supervised classification methods for the individual reassignment of commingled bone remains. Specialized bone chemistry studies have demonstrated the suitability of bone elemental composition as a distinct individual identifier. Given the widely documented ability of the LIBS technique to provide elemental emission spectra that are considered elemental fingerprints of the samples analyzed, the analytical potential of this technique has been assessed for the investigation of the contexts of commingled bone remains for their individual reassignment. The LIBS bone analysis consists of the direct ablation of micrometric portions of bone samples, either on their surface or within their internal structure. To produce reliable, accurate, and robust bone classifications, however, the available evidence suggests that LIBS spectral information must be processed by appropriate methods. When comparing the performance of seven different supervised classification methods using spectrochemical LIBS data for individual reassociation, those employing artificial intelligence-based algorithms produce analytically conclusive results, concretely individual reassociations with 100% accuracy, sensitivity, and robustness. Compared to LIBS, other techniques used for the purpose of interest exhibit limited performance in terms of robustness, sensitivity, and accuracy, as well as variations in these results depending on the type of bones used in the classification. The available literature supports the suitability of the LIBS technique for reliable individual reassociation of bone remains in a fast, simple, and cost-effective manner without the need for complicated sample processing.

We have specified and obtained a ZnSe prism with an unconventional face angle cut to 30°. This prism, with internal incidence angles ranging from 30° to 48°, allows users to record internal reflection spectra below the critical angle and attenuated total reflection (ATR) spectra above the critical angle without the need to change optics or move or replace the sample. We demonstrate its capabilities using 102 spectra of benzyl benzoate taken with - and -polarization at different angles of incidence. The subcritical spectra were analyzed to obtain , a key parameter for correcting the ATR spectra. These corrected spectra were subsequently used to determine the complex refractive index for all ATR measurements. The averaged complex refractive index function shows excellent agreement with that obtained through ATR spectroscopic ellipsometry.

Raman spectroscopy has great potential for quantitative analysis of natural gas. Helium is one of the components of natural gas and has a wide range of applications. It was believed that noble gases could not be detected using this technique due to the absence of their vibrational spectra. In this study, we demonstrated an approach to extracting the content of helium from the Raman spectrum of methane and carried out test measurements for the first time. The approach is based on the determination of changes in the ν band of methane caused by the influence of helium and other components. The necessary spectroscopic parameters characterizing the effect of methane (CH), helium (He), nitrogen (N), carbon dioxide (CO), and ethane (CH) on the ν band of methane at a resolution of 0.35 cm were obtained. The validation of the approach showed that the helium content in natural gas can be measured with an uncertainty of 1 mol% at a sample pressure of 50 bar. The measurement precision can be increased to 0.01 mol% by using a high-resolution spectrometer. The described method does not claim to replace helium detectors, but it can be considered a valuable addition to Raman gas analysis of natural gas in developing an all-in-one device. The possibilities for further improvement of the approach are also discussed.

The increasing use of light-based treatments requires a better understanding of the light tissue interaction for pigmented skin. To enhance comprehension in this area, this study proposes the use of pigmented-mimicking skin phantoms to assess the optical properties based on their tone, represented by the Individual Typology Angle (ITA) color scale. In this study, an epoxy resin matrix alongside compact facial powder and titanium dioxide was used to mimic the absorption, scattering, and shade properties of human skins. Eight phantoms covering the skin tones, light (ITA = 45.2°), tan (ITA = 23.3°), brown (ITA = 6.9°, -5.7°, and -16.9°), and dark (ITA = -34.6°, -41.6°, and -48.6°), were crafted. The absorption and reduced scattering coefficients were obtained using integrating spheres and calibrated spectrometers in the 500-900 nm range, and tones were measured using a commercial colorimeter. The experimental fitting proposed in this study could estimate the optical properties as a function of the skin tones through ITA values, by using an exponential function with a second-order polynomial exponent. This investigation aligns with prior studies involving human skin samples, and these findings hold promise for future clinical and diagnostic applications, particularly in the realm of light-based treatments to individual dermatological corrections in pigmented skin.

The insufficient capabilities of current surface-enhanced Raman scattering (SERS) substrates in enriching dilute analytes from complex media severely restrict detection sensitivity, hampering practical applications. To meet this demand, in this study, a novel super hydrophobic membrane that can be directly prepared on a large scale based on the silver nanoparticles (AgNPs) functioning with perfluorodecanethiol (PFDT) is fabricated and evaluated as an SERS substrate. Firstly, polyester (PET) films modified with sodium chloride were proven to be capable of loading AgNPs, and the sizes of AgNPs were investigated. In addition, the PFDT concentration and reaction time for functionalizing the surface of AgNPs have been optimized. The relationship between the hydrophobic properties of the film and its SERS performance was then studied. The PET@Ag-PFDT film demonstrates two orders of magnitude superior SERS performance than the unmodified PET@Ag substrate, with a detection limit of folic acid approaching 5 × 10 M.

Oyster fossils are some of the most common bivalve mollusk fossils found all over the world, they are different from other fossils because the oyster is still alive in the present day, and the body structure of modern oyster is almost the same as that of ancient one. Therefore, we designed a control experiment comparing the Raman spectra of minerals from both modern oysters and fossil oysters to explore the mechanism of oyster's fossilization process, which is considered to be helpful for investigating biological evolution or paleoenvironment. The oyster fossil sample was found in Nagi-Cho, Okayama Prefecture, Japan. We focused on the variations of band position and full width half-maximum of ν Raman band (symmetric stretching mode) of calcite (CaCO) from modern and fossil oysters and the mineral conversion between calcite and aragonite (CaCO) around the adductor muscle inside the oyster. Compared to modern oysters, the ν1 band at around 1086 cm of calcite from oyster fossils shifted to a high wavenumber region, and the possible reason for this phenomenon is considered an elemental substitution between Ca and Mg. As for aragonite around adductor muscle in fossil oysters, it has been found by Raman spectra that most of the aragonite has been converted into calcite because calcite has a relatively more stable structure.

We propose a new way of deriving the effective thickness in attenuated total reflection (ATR) spectroscopy, initially introduced by Hansen and Harrick in 1965. While following Hansen's approach, our derivation is more straightforward and includes an intermediate approximation that more closely aligns with results derived from Fresnel's equations, particularly for organic and biological materials. Using this intermediate approximation, we present improved estimations for the effective thicknesses with - and -polarized light. These estimations enabled us to enhance a recently developed ATR correction scheme that relies on effective thickness. Additionally, we examined the wavelength dependence of the product of wavenumber and effective thickness, observing that it bears a resemblance to the refractive index function of the sample. This similarity increases with the angle of incidence and the refractive index of the ATR crystal. Based on this observation, we introduce a simple correction scheme using the Kramers-Kronig transformed absorbance. This correction has the potential to address spectral shifts, facilitating applications in pattern recognition and spectra identification.

The rover landed at Jezero crater, Mars, on 18 February 2021, with a payload of scientific instruments to examine Mars' past habitability, look for signs of past life, and process samples for future return to Earth. The instrument payload includes the Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals (SHERLOC) deep ultraviolet Raman and fluorescence imaging spectrometer designed to detect, characterize, and map the presence of organics and minerals on the Martian surface. Operation and engineering constraints sometimes result in the acquisition of spectra with features near the detection limit. It is therefore important to separate instrumental (background) spectral components and spectral components inherent to Martian surface materials. For SHERLOC, the instrumental background is assessed by collecting spectra in the stowed-arm configuration where the instrument is pointed at the Martian nighttime sky with no surface sample present in its optical path. These measurements reveal weak Raman and fluorescence background spectral signatures as well as charged-coupled device pixels prone to erroneous intensity spikes separate from cosmic rays. We quantitatively describe these features and provide a subtraction procedure to remove the spectral background from surface spectra. By identifying and accounting for the SHERLOC Raman background features within the median Raman spectra of Martian target scans, we find that the undefined silicate spectral feature interpreted to be either amorphous silicate or plagioclase feldspar is ubiquitously found in every Mars target Raman scan collected through Sol 751.

The continuous evolution of deep learning has garnered significant attention in spectroscopy. This study focuses on identifying wheat flour, presenting a more efficient and accurate method by combining two-dimensional correlation spectroscopy (2D-COS) and deep learning techniques. A data set of 316 near-infrared (NIR) spectral samples of four types of wheat flour was collected. By applying three disparate 2D-COS techniques, i.e., synchronous, asynchronous, and integrated, we crafted 948 2D-COS images. These images, obtained by transforming the original one-dimensional spectra into 2D representations, offer richer information for deep learning analysis. The study introduced an 18-layer residual network incorporating a convolutional attention mechanism, specifically tailored for the 2D-COS analysis of wheat flour, aimed at enhancing the model's discriminative capabilities by refining the residual neural network's structure. Achieving an unprecedented recognition accuracy of 100% through methodical optimization and rigorous training on the synchronous 2D-COS data set of wheat flour, the proposed model is a testament to the efficacy of deep learning in spectroscopic analysis. To further exhibit the confluence of 2D-COS with deep learning, t-distributed stochastic neighbor embedding was employed to visualize the distinctive 2D-COS features within the deep learning architecture. Additionally, the model's performance was juxtaposed with prevailing NIR spectral recognition methods, including random forest, gradient boosting decision tree, and artificial neural network. This comparison cemented the proposed approach's superiority in wheat flour categorization. The findings of this study not only introduce a novel and efficient solution for wheat flour quality analysis but also underscore the significant potential of deep learning techniques in spectroscopy applications.

Modern developments in autonomous chemometric machine learning technology strive to relinquish the need for human intervention. However, such algorithms developed and used in chemometric multivariate calibration and classification applications exclude crucial expert insight when difficult and safety-critical analysis situations arise, e.g., spectral-based medical decisions such as noninvasively determining if a biopsy is cancerous. The prediction accuracy and interpolation capabilities of autonomous methods for new samples depend on the quality and scope of their training (calibration) data. Specifically, analysis patterns within target data not captured by the training data will produce undesirable outcomes. Alternatively, using an immersive analytic approach allows insertion of human expert judgment at key machine learning algorithm junctures forming a sensemaking process performed in cooperation with a computer. The capacity of immersive virtual reality (IVR) environments to render human comprehensible three-dimensional space simulating real-world encounters, suggests its suitability as a hybrid immersive human-computer interface for data analysis tasks. Using IVR maximizes human senses to capitalize on our instinctual perception of the physical environment, thereby leveraging our innate ability to recognize patterns and visualize thresholds crucial to reducing erroneous outcomes. In this first use of IVR as an immersive analytic tool for spectral data, we examine an integrated IVR real-time model selection algorithm for a recent model updating method that adapts a model from the original calibration domain to predict samples from shifted target domains. Using near-infrared data, analyte prediction errors from IVR-selected models are reduced compared to errors using an established autonomous model selection approach. Results demonstrate the viability of IVR as a human data analysis interface for spectral data analysis including classification problems.

Osteomyelitis (OM) and periprosthetic joint infections (PJIs) are major public health concerns in Western countries due to increased life expectancy. Infections usually occur due to bacterial spread through fractures, implants, or blood-borne transmission. The pathogens trigger an inflammatory response that hinders bone tissue regeneration. Treatment requires surgical intervention, which involves the precise removal of infected tissue, wound cleansing, and local and systemic antibiotic administration. (SA) is one of the most common pathogens causing infection-induced OM and PJIs. It forms antimicrobial-resistant biofilms and is frequently found in healthcare settings. In this proof-of-concept, we present an approach based on multiple spectroscopic techniques aimed at investigating the effects of SA infection on bone tissue, as well as identifying specific markers useful to detect early bacterial colonization on the tissue surface. A cross-section of a human femoral diaphysis, with negative-culture results, was divided into three parts, and the cortical and trabecular regions were separated from each other. Two portions of each bone tissue type were infected with SA for one and seven days, respectively. Multiple techniques were used to investigate the impact of the infection on bone tissue, Brillouin-Raman microspectroscopy and attenuated total reflection Fourier transform infrared spectroscopy were employed to assess and develop a new noninvasive diagnostic method to detect SA by targeting the bone of the host. The results indicate that exposure to SA infection significantly alters the bone structure, especially in the case of the trabecular type, even after just one day. Moreover, Raman spectral markers of the tissue damage were identified, indicating that this technique can detect the effect of the pathogens' presence in bone biopsies and pave the way for potential application during surgery, due to its nondestructive and contactless nature.