Application Of Raman Spectroscopy Research Articles

Analytical applications of droplet deposition Raman spectroscopy.

The droplet deposition methods in Raman spectroscopy have received considerable attention in the field of analytical sensing focusing on effective pre-concentration of the studied analyte (coffee-ring effect or small spots). This review covers different analytical applications of drop-coating deposition Raman scattering (DCDRS) and droplet deposition surface-enhanced Raman scattering (SERS) spectroscopy. Two main advantages of droplet deposition Raman techniques are considered: the drying-induced segregation of the components from the mixtures (such as body fluids) and the sensitivity of detection of various analytically important molecules. Some recent advanced applications, including clinical cancer diagnosis, are discussed and summarized. Finally, the potential and further perspectives of the droplet deposition Raman methods for analytical studies are introduced.

Enhancing Structural Integrity, Optical Properties, and Room Temperature Formaldehyde Sensing Through Optimized Spray Deposition Rates

This study explores the impact of deposition rate on the properties of TiO2 thin films produced via spray pyrolysis, focusing on their application in gas sensors. The analysis covers structural, morphological, optical, and gas sensing characteristics of TiO2 films deposited at rates between 1 and 2.5 ml min−1. Studies show optimizing TiO2 film deposition rates at 2 ml min−1 significantly enhances formaldehyde detection, improving selectivity and achieving a rapid response of 7.52 at 20 ppm concentration. This study underscores the pivotal role of deposition rate optimization in augmenting the gas-sensing efficacy of TiO2 films, particularly for formaldehyde detection at ambient conditions. Optimal deposition rates are instrumental in enhancing sensor performance. The synergistic application of XRD and Raman spectroscopy unequivocally confirmed the presence of the TiO2 anatase phase, which is of paramount significance in gas sensing applications. FESEM furnished high-resolution insights into the surface morphology, revealing a spherical architecture. Furthermore, UV–vis spectroscopy was employed to assess the optical band gap of the films, which exhibited a decrement correlating with the rate of deposition. Notably, a deposition rate of 2 ml min−1 markedly improved the TiO2 films’ sensing performance. These insights are critical for developing cost-effective, high-performance gas sensors for cutting-edge applications.

Raman and NIR spectroscopy-based real-time monitoring of the membrane filtration process of a recombinant protein for the diagnosis of SARS-CoV2

This research shows the detailed comparison of Raman and near-infrared (NIR) spectroscopy as Process Analytical Technology tools for the real-time monitoring of a protein purification process. A comprehensive investigation of the application and model development of Raman and NIR spectroscopy was carried out for the real-time monitoring of a process-related impurity, imidazole, during the tangential flow filtration of Receptor-Binding Domain (RBD) of the SARS-CoV-2 Spike protein. The fast development of Raman and NIR spectroscopy-based calibration models was achieved using offline calibration data, resulting in low calibration and cross-validation errors. Raman model had an RMSEC of 1.53 mM, and an RMSECV of 1.78 mM, and the NIR model had an RMSEC of 1.87 mM and an RMSECV of 2.97 mM. Furthermore, Raman models had good robustness when applied in an inline measurement system, but on the contrary NIR spectroscopy was sensitive to the changes in the measurement environment. By utilizing the developed models, inline Raman and NIR spectroscopy were successfully applied for the real-time monitoring of a process-related impurity during the membrane filtration of a recombinant protein. The results enhance the importance of implementing real-time monitoring approaches for the broader field of diagnostic and therapeutic protein purification and underscore its potential to revolutionize the rapid development of biological products.

Decoration of Ag nanoparticle on MXene sheets for SERS studies

MXene sheets with the unique electrical and optical properties show the excellent potential for surface-enhanced Raman spectroscopy (SERS) applications. In this study, we chose Ti3C2Tx, a type of MXene, to decorate silver nanoparticles (Ag NPs) on the ultrathin two-dimensional (2D) MXene sheets. The designed Ag-MXene substrates with SERS activity showed high sensitivity, high stability, and reproducibility. The SERS signal was enhanced by the synergistic contribution of both charge-transfer (CT) and surface plasmon resonance (SPR) involving the Ag NPs and the MXene sheets. Due to the strong interaction between the probe molecules and Ag NPs which provided the nanoscale gap, the substrate exhibited remarkable SERS performance. A novel experimental strategy was developed to facilitate the controlled synthesis of noble metal NPs and MXene sheets and provide insights for further improving the practical applications of these materials in SERS detection.

Quantification of Hydrogen Isotopes Utilizing Raman Spectroscopy Paired with Chemometric Analysis for Application across Multiple Systems.

Online and real-time analysis of a chemical process is a major analytical challenge that can drastically change the way the chemical industry or chemical research operates. With in situ analyses, a new and powerful understanding of chemistry can be gained; however, building robust tools for long-term monitoring faces many challenges, including compensating for instrument drift, instrument replacement, and sensor or probe replacement. Accounting for these changes by recollecting calibration data and rebuilding quantification models can be costly and time-consuming. Here, methods to overcome these challenges are demonstrated with an application of Raman spectroscopy to monitoring hydrogen isotopes with varied speciation within dynamic gas streams. Specifically, chemical data science tools such as chemometric modeling are leveraged along with several examples of calibration transfer approaches. Furthermore, the optimization of instrument and sensor cell parameters for targeted gas-phase analyses is discussed. While the particular focus on hydrogen is highly beneficial within the nuclear energy sector, mechanisms built and demonstrated here are widely applicable to optical spectroscopy monitoring in numerous other chemical systems that can be leveraged in other processes.

SLE diagnosis research based on SERS combined with a multi-modal fusion method

As artificial intelligence technology gains widespread adoption in biomedicine, the exploration of integrating biofluidic Raman spectroscopy for enhanced disease diagnosis opens up new prospects for the practical application of Raman spectroscopy in clinical settings. However, for systemic lupus erythematosus (SLE), origin Raman spectral data (ORS) have relatively weak signals, making it challenging to obtain ideal classification results. Although the surface enhancement technique can enhance the scattering signal of Raman spectroscopic data, the sensitivity of the SERS substrate to airborne impurities and the inhomogeneous distribution of hotspots degrade part of the signal. To fully utilize both kinds of data, this paper proposes a two-branch residual-attention network (DBRAN) fusion technique, which allows the ORS to complement the degraded portion and thus improve the model's classification accuracy. The features are extracted using the residual module, which retains the original features while extracting the deep features. At the same time, the study incorporates the attention module in both the upper and lower branches to handle the weight allocation of the two modal features more efficiently. The experimental results demonstrate that both the low-level fusion method and the intermediate-level fusion method can significantly improve the diagnostic accuracy of SLE disease classification compared with a single modality, in which the intermediate-level fusion of DBRAN achieves 100% classification accuracy, sensitivity, and specificity. The accuracy is improved by 10% and 7% compared with the ORS unimodal and the SERS unimodal modalities, respectively. The experiment, by fusing the multimodal spectral, realized rapid diagnosis of SLE disease by fusing multimodal spectral data, which provides a reference idea in the field of Raman spectroscopy and can be further promoted to clinical practical applications in the future.

Applications of Data Characteristic AI-Assisted Raman Spectroscopy in Pathological Classification.

Raman spectroscopy has been widely used for label-free biomolecular analysis of cells and tissues for pathological diagnosis in vitro and in vivo. AI technology facilitates disease diagnosis based on Raman spectroscopy, including machine learning (PCA and SVM), manifold learning (UMAP), and deep learning (ResNet and AlexNet). However, it is not clear how to optimize the appropriate AI classification model for different types of Raman spectral data. Here, we selected five representative Raman spectral data sets, including endometrial carcinoma, hepatoma extracellular vesicles, bacteria, melanoma cell, diabetic skin, with different characteristics regarding sample size, spectral data size, Raman shift range, tissue sites, Kullback-Leibler (KL) divergence, and significant Raman shifts (i.e., wavenumbers with significant differences between groups), to explore the performance of different AI models (e.g., PCA-SVM, SVM, UMAP-SVM, ResNet or AlexNet). For data set of large spectral data size, Resnet performed better than PCA-SVM and UMAP. By building data characteristic-assisted AI classification model, we optimized the network parameters (e.g., principal components, activation function, and loss function) of AI model based on data size and KL divergence etc. The accuracy improved from 85.1 to 94.6% for endometrial carcinoma grading, from 77.1 to 90.7% for hepatoma extracellular vesicles detection, from 89.3 to 99.7% for melanoma cell detection, from 88.1 to 97.9% for bacterial identification, from 53.7 to 85.5% for diabetic skin screening, and mean time expense of 5 s.

Guidelines for Raman spectroscopy and imaging techniques in food safety analysis

AbstractEnsuring the highest standards of food quality and safety is crucial for maintaining human health. This requires the creation of sophisticated and highly sensitive techniques for detecting and identifying food pollutants. Raman spectroscopy, known for its rapidity, simplicity, sensitivity, and noninvasive characteristics, has become a prominent method in the field of food safety and quality analysis. This review discusses in detail the fundamental principles, instruments, methodology, and data processing techniques of Raman spectroscopy. It specifically emphasizes the practical applications of Raman spectroscopy in assessing foodborne pathogens, toxic gases in spoiled foods, and monitoring the translocation of pesticide in plants. Finally, we discussed the development trend and challenges of Raman spectroscopy in the field of food safety analysis, aiming to provide an accessible and practical guide. This review article equips readers with essential insights into conducting and understanding Raman spectroscopy experiments, thereby contributing to development in the field of food safety analysis.

Application of Raman Spectroscopy to Evaluate the Structure Changes of Lubricating Grease Modified with Montmorillonite after Tribological Tests

This study shows the experimental data obtained by Raman spectroscopy to evaluate the structural changes of vegetable lubricants modified with montmorillonite after tribological tests. The analysis of the friction factor and limiting load of wear in the test for the examined grease compound shows a substantial effect on this parameter for grease. A change in the evaluated tribo-parameter results in a modification of the structure of the tested lubricant and changes in the protection efficiency of the tribological system. The amount of thickener, oil base and additive in the grease structure has an influence on the value of anti-wear properties, as shown by the data obtained in the tribological test described in this paper. The Raman spectroscopy tests showed that, in the tribological processes, some of the ingredients undergo an oxidative reaction, which leads to the formation of oxygenated organic substances that form an organic layer on the metal surface and counteract the wear of the lubricated contact surfaces. Other compounds come into close contact with the tribological layer, which increases its ability to resist wear and shear. The efficiency of the used additive is based on the formation (during friction) of a low-shear and high-plastic-strength thin film which is chemically highly bonded to the material and has a high level of durability against wear processes. As a consequence of the thermal decomposition of the additive, chemical interactions occur among the ingredients of the material of the substrate and the lubricants.

An electron density clustering based adaptive segmentation method for protein Raman spectrum calculation

Raman spectroscopy is a powerful technique for protein detection, but the calculation of Raman spectrum is a longstanding challenging problem due to the large sizes and complex structures of protein molecules. Dividing proteins into fragments can greatly accelerate the calculation, but this usually introduces large errors originating from ignored interactions between fragments into obtained spectra. In this paper, we proposed a new adaptive segmentation method based on the strength of interactions and molecular shapes and structures, i.e., electron density clustering, to divide proteins. It can reduce errors of obtained Raman spectra by about 20% compared to the uniform segmentation method without a significant increase in computational cost. This method can facilitate the validation and analysis of detected Raman spectra of proteins and promote the application of Raman spectroscopy in biological detection.

A Distributed Deep Learning Network Based on Data Enhancement for Few-Shot Raman Spectral Classification of Litopenaeus vannamei Pathogens

Litopenaeus vannamei is a common species in aquaculture and has a high economic value. However, Litopenaeus vannamei are often invaded by pathogenic bacteria and die during the breeding process, so it is of great significance to study the identification of shrimp pathogenic bacteria. The wide application of Raman spectroscopy in identifying directions of inquiry provides a new means for this. However, the traditional Raman spectroscopy classification task requires a large amount of data to ensure the accuracy of its classification. Therefore, the question of how to obtain higher classification accuracy through the means of a small amount of Raman spectrum data is a difficult point in the research. This paper proposes a distributed deep learning network based on data enhancement for few-shot Raman spectral classification of Litopenaeus vannamei pathogens. The network consists of RSEM, RSDM, and DLCM modules. The RSEM module uses an improved generative adversarial network combined with transfer learning to generate a large amount of spectral data. The RSDM module uses improved U-NET to denoise the generated data. In addition, we designed a distributed learning classification model (DLCM) which significantly speeds up model training, improves the efficiency of the algorithm, and solves the network degradation problem that often occurs during deep learning model training. The average classification accuracy of our proposed network on four shrimp pathogenic bacteria reaches 98.9%, which is higher than several models commonly used in Raman spectroscopy classification tasks. The method proposed in this article only needs the Raman spectra of a small number of microorganisms to complete the efficient and rapid identification of shrimp pathogenic bacteria, and this method certainly has the potential to solve the problem of the spectral classification of other microorganisms.

Surface Enhanced Raman Spectroscopy Application of Aluminum Nanofilm in Improving the Performance of Optical Telescopes

Optical telescopes play a crucial role in exploring the universe, and understanding various aspects of the Earth and the universe. However, traditional optical telescopes have problems with high noise in the optical signal, which is not conducive to material analysis. To improve the performance of optical telescopes, this study utilized surface enhanced Raman spectroscopy technology of aluminum nanofilms for spectral analysis. The experimental results demonstrated that the amorphous aluminum film prepared by UV annealing exhibited Raman spectral enhancement effect at different laser wavelengths. The optimal effect can be achieved by using excitation light with a wavelength of 670 nm. The transmittance curve modified by aluminum nanofilm met the requirements of a day blind ultraviolet detector. Moreover, the bandgap energy gap was approximately 5 eV. The detector with a 7 nm aluminum deposition had the best performance improvement. The modification of aluminum nanoparticles improves the photoelectric response and reduces the time constant of the slow response stage. This study provides a foundation for further exploration and application of surface enhanced Raman spectroscopy technology in improving the performance of optical telescopes.

Raman characteristics of graphene/quartz and graphene/Ag nanoparticles/quartz substrate: Laser power dependence

The sensitivity of graphene surface to adjacent conditions plays an important role that modifies the performance and characteristics of graphene devices working under ambient conditions. Quartz is a dielectric transparent material with excellent optical transmission and therefore graphene/quartz is widely used in graphene device technology. Here, graphene/quartz and graphene/plasmonic nanoparticle/quartz structures were investigated using Raman spectroscopy for applications in nanotechnology like graphene quartz fiber (GQF) multifunctional electrode. Optically induced shift of the Fermi level of graphene monolayer on quartz substrates and on Ag nanoparticles (NPs) distributed on quartz substrates was tracked by increasing the applied laser power. Strained graphene attached to quartz substrate, thermal effects, and charge transfer were discussed using the evolution of the G-peak characteristics. Well distributed Ag NPs on quartz substrate are shown by scanning electron microscopy; an interesting property of plasmonic nanoparticles on quartz substrates. The graphene on single Ag NP verified using Raman mapping images by the surface enhanced Raman G-peak on the Ag NP. The Ag NP was driven to the plasmonic resonance by selecting the laser probe wavelength and the localized surface plasmons excited in Ag NPs were inferred by the transmission and photoluminescence spectra. Investigations of graphene on quartz and on plasmonic nanoparticles/quartz substrates can help for GQF hybrid structures which combines the enhanced thermal and electrical conductivity of graphene/plasmonic nanoparticles in addition to the extraordinary properties of quartz fibers suitable for sensing applications.

Raman and Surface-Enhanced Raman Scattering Detection in Flowing Solutions for Complex Mixture Analysis.

Raman scattering provides a chemical-specific and label-free method for identifying and quantifying molecules in flowing solutions. This review provides a comprehensive examination of the application of Raman spectroscopy and surface-enhanced Raman scattering (SERS) to flowing liquid samples. We summarize developments in online and at-line detection using Raman and SERS analysis, including the design of microfluidic devices, the development of unique SERS substrates, novel sampling interfaces, and coupling these approaches to fluid-based chemical separations (e.g., chromatography and electrophoresis). The article highlights the challenges and limitations associated with these techniques and provides examples of their applications in a variety of fields, including chemistry, biology, and environmental science. Overall, this review demonstrates the utility of Raman and SERS for analysis of complex mixtures and highlights the potential for further development and optimization of these techniques. Expected final online publication date for the Annual Review of Analytical Chemistry, Volume 17 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Spectroscopic analysis of Pu-bearing compounds in double-walled cells

Spectroscopic analysis of radiological materials has been historically limited to radiological labs with older or less advanced scientific instrumentation. The development of double-walled cells (DWCs) at the Savannah River National Laboratory (SRNL) has enabled Pu-bearing compounds to be removed from radiological laboratories and studied in our radiologically clean spectroscopy laboratory with state-of-the-art instrumentation. In this manuscript, we discuss the contributions of DWCs that have allowed the application of Raman spectroscopy, diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS), diffuse reflectance spectroscopy (DRS) in the shortwave infrared, and gamma spectroscopy at SRNL. Significant advances have been made in the understanding of the thermal decomposition of Pu(III) and Pu(IV) oxalates, alpha-induced damage to the PuO2 crystal lattice, and the effect of calcination temperatures on the quality of PuO2. These techniques have enabled methods to conduct PuO2 age dating since last calcination and estimate the calcination temperature with Raman spectroscopy and DRS. Additional spectroscopic information measured with DRIFTS has enabled the observation of the evolution of carbon species with calcination temperature, while gamma spectroscopy provides information on age dating since last purification.

Application of Raman spectroscopy during pharmaceutical process development for determination of critical quality attributes in Protein A chromatography

Raman spectroscopy is considered a Process Analytical Technology (PAT) tool in biopharmaceutical downstream processes. In the past decade, researchers have shown Raman spectroscopy's feasibility in determining Critical Quality Attributes (CQAs) in bioprocessing. This study verifies the feasibility of implementing a Raman-based PAT tool in Protein A chromatography as a CQA monitoring technique, for the purpose of accelerating process development and achieving real-time release in manufacturing. A system connecting Raman to a Tecan liquid handling station enables high-throughput model calibration. One calibration experiment collects Raman spectra of 183 samples with 8 CQAs within 25 h. After applying Butterworth high-pass filters and k-nearest neighbor (KNN) regression for model training, the model showed high predictive accuracy for fragments (Q2 = 0.965) and strong predictability for target protein concentration, aggregates, as well as charge variants (Q2≥ 0.922). The model's robustness was confirmed by varying the elution pH, load density, and residence time using 19 external validation preparative Protein A chromatography runs. The model can deliver elution profiles of multiple CQAs within a set point ± 0.3 pH range. The CQA readouts were presented as continuous chromatograms with a resolution of every 28 s for enhanced process understanding. In external validation datasets, the model maintained strong predictability especially for target protein concentration (Q2 = 0.956) and basic charge variants (Q2 = 0.943), except for overpredicted HCP (Q2 = 0.539). This study demonstrates a rapid, effective method for implementing Raman spectroscopy for in-line CQA monitoring in process development and biomanufacturing, eliminating the need for labor-intensive sample pooling and handling.

Oil Palm Leaves Phenotyping using Biomarkers Derived from Raman Spectra

The efficient production of oil from oil palm trees is heavily dependent on their health status, reflected in the oil extraction rate (OER). The 17th frond of the oil palm trees contains a significant amount of organic compounds that directly influence the overall health of the tree. Achieving an optimal balance of essential nutrients such as nitrogen (N), phosphorus (P), and potassium (K) is crucial for classifying a tree as healthy, as it results in an increased oil to bunch and fruit to bunch ratio. To accurately assess the health level of oil palm trees, this study explores the application of Raman spectroscopy, a non-invasive technique in determining the molecular fingerprint of an organic sample. In this research, Raman spectroscopy is employed to determine the health level of oil palm trees, and a machine learning-based health level classification algorithm is developed. The algorithm analyzes the organic compounds found in oil palm leaves, which were collected from 20 different trees. The extracted spectral features from these leaves are used to classify them into two health levels: healthy and not healthy. For this purpose, 31 machine learning models are tested to identify the most accurate classifier. The findings reveal that the Tree and fine K-Nearest Neighbors (KNN) classifier demonstrates the highest overall accuracy of 95% using three significant features, namely the Raman intensity, Full Width at Half Maximum (FWHM), and area under the curve. This result signifies the potential of Raman spectroscopy as a reliable and promising method for non-invasively phenotyping oil palm leaves, enabling precise prediction of the health status of oil palm trees.

Research Progress in Tumor Diagnosis Based on Raman Spectroscopy

Background: Cancer is a major disease that threatens human life and health. Raman spectroscopy can provide an effective detection method. Objective: The study aimed to introduce the application of Raman spectroscopy to tumor detection. We have introduced the current mainstream Raman spectroscopy technology and related application research. Methods: This article has first introduced the grim situation of malignant tumors in the world. The advantages of tumor diagnosis based on Raman spectroscopy have also been analyzed. Secondly, various Raman spectroscopy techniques applied in the medical field are introduced. Several studies on the application of Raman spectroscopy to tumors in different parts of the human body are discussed. Then the advantages of combining deep learning with Raman spectroscopy in the diagnosis of tumors are discussed. Finally, the related problems of tumor diagnosis methods based on Raman spectroscopy are pointed out. This may provide useful clues for future work. Conclusion: Raman spectroscopy can be an effective method for diagnosing tumors. Moreover, Raman spectroscopy diagnosis combined with deep learning can provide more convenient and accurate detection results.

Applications of Raman spectroscopy in the diagnosis and monitoring of neurodegenerative diseases.

Raman scattering is an inelastic light scattering that occurs in a manner reflective of the molecular vibrations of molecular structures and chemical conditions in a given sample of interest. Energy changes in the scattered light can be assessed to determine the vibration mode and associated molecular and chemical conditions within the sample, providing a molecular fingerprint suitable for sample identification and characterization. Raman spectroscopy represents a particularly promising approach to the molecular analysis of many diseases owing to clinical advantages including its instantaneous nature and associated high degree of stability, as well as its ability to yield signal outputs corresponding to a single molecule type without any interference from other molecules as a result of its narrow peak width. This technology is thus ideally suited to the simultaneous assessment of multiple analytes. Neurodegenerative diseases represent an increasingly significant threat to global public health owing to progressive population aging, imposing a severe physical and social burden on affected patients who tend to develop cognitive and/or motor deficits beginning between the ages of 50 and 70. Owing to a relatively limited understanding of the etiological basis for these diseases, treatments are lacking for the most common neurodegenerative diseases, which include Alzheimer's disease, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis. The present review was formulated with the goal of briefly explaining the principle of Raman spectroscopy and discussing its potential applications in the diagnosis and evaluation of neurodegenerative diseases, with a particular emphasis on the research prospects of this novel technological platform.

Simultaneous Application of Raman and Laser-Induced Breakdown Spectroscopy in the Gas Phase with a Single Laser and Detector.

Raman spectroscopy and laser-induced breakdown spectroscopy (LIBS) are powerful tools for molecular and elemental analysis, respectively. Their combined application, however, is challenging due to the differences in the signal generation and detection characteristics. This note proposes three experimental schemes for the simultaneous application of Raman and LIBS for gas-phase diagnostics. Ring-cavity optical pulse stretchers facilitate shaping suitable pulse pairs from a Q-switched laser that enables the quasi-simultaneous detection of the Raman and LIBS signals on a single detector.