Application Of Raman Spectroscopy Research Articles

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.

Application of Raman spectroscopy in relative blue ballpoint pen ink dating for forensic document analysis – a case report

In this case report, blue ballpoint ink on a questioned document was investigated using a newly developed Raman model devoted for dating of documents via writing inks. The majority of blue ballpoint pen inks contain crystal violet - a triarylmethane dye which displays a typical “molecular fingerprint” type of Raman spectrum, of which most intense peaks at 749 cm−1 and 1542 cm−1 (under 785 nm laser excitation) represents the least and the most stable bonds of crystal violet molecules, respectively. The ratios of peak intensities at above mentioned Raman shifts are utilized to estimate the relative dating of the questioned blue ballpoint pen ink in practice, results of which agreed with estimations using other traditional relative dating methods. ® 2023 Journal of Science and Technology - NTTU

Raman signal optimization based on residual network adaptive focusing

Due to its high sensitivity and specificity, Micro-Raman spectroscopy has emerged as a vital technique for molecular recognition and identification. As a weakly scattered signal, ensuring the accurate focus of the sample is essential for acquiring high quality Raman spectral signal and its analysis, especially in some complex microenvironments such as intracellular settings. Traditional autofocus methods are often time consuming or necessitate additional hardware, limiting real-time sample observation and device compatibility. Here, we propose an adaptive focusing method based on residual network to realize rapid and accurate focusing on Micro-Raman measurements. Using only a bright field image of the sample acquired on any image plane, we can predict the defocus distance with a residual network trained by Resnet50, in which the focus position is determined by combining the gradient and discrete cosine transform. Further, detailed regional division of the bright field map used for characterizing the height variation of actual sample surface is performed. As a result, a focus prediction map with 1μm accuracy is obtained from a bright field image in 120 ms. Based on this method, we successfully realize Raman signal optimization and the necessary correction of spectral information. This adaptive focusing method based on residual network is beneficial to further enhance the sensitivity and accuracy of Micro-Raman spectroscopy technology, which is of great significance in promoting the wide application of Raman spectroscopy.

Applications of Raman spectroscopy in clinical medicine

AbstractRaman spectroscopy is a nondestructive and highly effective technique for analyzing biological tissues and diagnosing diseases by providing detailed spectral information about the specific molecular structures of substances. Its efficacy in these applications has been widely recognized, making it a powerful tool in the field. This article presents a comprehensive overview of the latest developments in Raman spectroscopy and its wide‐ranging applications in the diagnosis of critical diseases, such as cancer, infections, neurodegenerative diseases, and predicting surgical outcomes. It highlights the significant contributions of Raman spectroscopy in these areas, shedding light on its potential as a valuable diagnostic tool. This article delves into the advancements of Raman spectroscopy in biomedical sciences, with a specific focus on state‐of‐the‐art techniques such as surface‐enhanced Raman spectroscopy, resonance Raman spectroscopy, and tip‐enhanced Raman spectroscopy. These techniques have shown great potential in various applications within the field. The article explores their use in ex vivo and in vivo medical diagnosis, covering topics such as sample collection, data processing, and the successful establishment of correlations between Raman spectra and biochemical information in specific diseases. Furthermore, the article discusses the limitations of the current research and offers insights into potential future directions for further exploration in the field of Raman spectroscopy in biomedical sciences.

Non-toxic arsenic nanoparticles for surface-enhanced Raman spectroscopy applications

In this study, we present green synthesis of Arsenic nanoparticles (As NPs) using different plant extracts. The resultant As NPs exhibit diverse sizes, shapes and assembly of nanotubules. In addition, As NPs have large band gap that ranges from 3.09 to 3.88 eV. They are enriched with defect states and exhibit multiple defect related photoluminescence. Owing to the aggregation and nanotubule assembly of As NPs, they are utilized for surface-enhanced Raman spectroscopy (SERS) sensing application. Under the illumination of continuous wave 785 nm laser, these As NPs demonstrate the detection of Methylene blue dye molecules via non-plasmonic chemical enhancement of SERS signal with an enhancement factor of 10 3 . Intriguingly, As NPs exhibit non-toxicity towards specific microbes, suggesting safety in handling during experiments and potential applications in SERS imaging and biomedicines.

Application of Raman spectroscopy in the detection of brain glioma

Application of Raman spectroscopy in the detection of brain glioma

Methods and Applications of Raman Spectroscopy: A Powerful Technique in Modern Research, Diagnosis, and Food Quality Control

Background:Raman spectroscopy has evolved into an important fast, rapid, direct, and non-destructive technique that has recently been applied in different fields.Objective:The present work aims to study the theoretical bases and the experimental techniques relate to Raman spectroscopy and highlight the performance as well as the different applications of the technique.Methods:Spectroscopy, in general, is the study of the interaction between electromagnetic radiation and matter, which corresponds to the emission or transmission of energy in the form of a wave at a given frequency. Raman spectroscopy is based on the inelastic diffusion of photons on electrons. The change in electron energy level leads to different modes of vibration of a molecule. These different vibration modes occur at specific frequencies for each molecule.Results:Raman spectroscopy is used in chemistry as a tool to identify molecules in a sample. Indeed, each Raman peak is associated with a vibration mode of a molecule; it is considered as a more useful approach to monitor the chemical parameters of samples tested in several fields, especially in food safety.Conclusion:This review covers the current research status and prospects of Raman spectroscopy. The Raman effect is considered from the time of its discovery as a great gift for chemists because it contributes to a better characterization of the structure of matter.

Guided principal component analysis (GPCA): a simple method for improving detection of a known analyte.

There is increasing interest in the application of Raman spectroscopy in a medical setting, ranging from supporting real-time clinical decisions e.g. surgical margins to assisting pathologists with disease classification. However, there remain a number of barriers for adoption in the medical setting due to the increased complexity of probing highly heterogeneous, dynamic biological materials. This inherent challenge can also limit the deployment of higher level analytical approaches such as Artificial Intelligence (AI) including convolutional neural networks (CNN), as there is a lack of a ground truth required for training purposes i.e. in complex clinical samples. Principal component analysis (PCA) is an unsupervised data reduction approach (orthogonal linear transformation) that has been used extensively in spectroscopy for 30+ years, due to its capability to simplify analysis of complex spectroscopic data. However, due to PCA being unsupervised features will inherently appear mixed and their rank may vary between experiments. Here we propose Guided PCA (GPCA), a simple approach that allows PCA to be guided with spectral data to ensure a consistent rank of a key target moiety by the inclusion of a reference (guiding) spectrum to the data set. This simplifies analysis, increases robustness of PCA analysis and improves quantification and the limits of detection and decreases RMSE.

Nanoscale visualization of phase separation in binary supported lipid monolayer using tip-enhanced Raman spectroscopy.

Supported lipid membranes are an important model system to study the phase separation behavior at the nanoscale. However, the conventional nanoanalytical tools often fail to provide reliable chemical characterization of the phase separated domains in a non-destructive and label-free manner. This study demonstrates the application of scanning tunneling microscopy-based tip-enhanced Raman spectroscopy (TERS) to study the nanoscale phase separation in supported d62-DPPC : DOPC lipid monolayers. Hyperspectral TERS imaging successfully revealed a clear segregation of the d62-DPPC-rich and DOPC-rich domains. Interestingly, nanoscale deposits of d62-DPPC were observed inside the DOPC-rich domains and vice versa. High-resolution TERS imaging also revealed the presence of a 40-120 nm wide interfacial region between the d62-DPPC-rich and DOPC-rich domains signifying a smooth transition rather than a sharp boundary between them. The novel insights obtained in this study demonstrate the effectiveness of TERS in studying binary lipid monolayers at the nanoscale.

A simple and efficient alginate hydrogel combined with surface-enhanced Raman spectroscopy for quantitative analysis of sodium nitrite in meat products.

Sodium nitrite is a commonly used preservative and color protectant in the food industry. Conventional analytical methods are highly susceptible to food matrix interference, time-consuming and costly. In this study, the ion cross-linking method was employed to prepare alginate hydrogel substrates, and phenosafranin was chosen as a single-molecule probe to analyze sodium nitrite. Our investigation centered on elucidating the effects of alginate and cross-linking ion concentrations on Raman signal characteristics. The optimal Raman response was observed in the precursor solution with 1% sodium alginate and 0.1 mol L-1 cross-linking ions. The relative standard deviations (RSDs) of the feature peaks from the three substrate batches ranged from 1.22% to 16.30%, attesting the robustness and consistency of the substrates. The signal reduction of the substrates after a four-week storage period remained below 10%, indicating that the substrates had good reproducibility and stability. The limits of detection (LODs) for sodium nitrite in extracts from cured meat, luncheon meat, and sliced ham were determined to range from 3.75 mg kg-1 to 8.11 mg kg-1, with low interference from the food matrix. The support vector machine algorithm was utilized to train and predict the data, which proved to be more accurate (98.6%-99.8% recovery) than the traditional linear regression model (81.9%-112.7% recovery) in predicting the spiked samples. The application of hydrogel-based surface-enhanced Raman spectroscopy (SERS) substrates for nitrite detection in food, combined with machine learning for regression prediction in data processing, collectively augmented the potential of SERS technology in the field of food analysis.

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.

Raman Spectroscopy and AI Applications in Cancer Grading: An Overview

Raman Spectroscopy and AI Applications in Cancer Grading: An Overview

Unveiling brain disorders using liquid biopsy and Raman spectroscopy.

Brain disorders, including neurodegenerative diseases (NDs) and traumatic brain injury (TBI), present significant challenges in early diagnosis and intervention. Conventional imaging modalities, while valuable, lack the molecular specificity necessary for precise disease characterization. Compared to the study of conventional brain tissues, liquid biopsy, which focuses on blood, tear, saliva, and cerebrospinal fluid (CSF), also unveils a myriad of underlying molecular processes, providing abundant predictive clinical information. In addition, liquid biopsy is minimally- to non-invasive, and highly repeatable, offering the potential for continuous monitoring. Raman spectroscopy (RS), with its ability to provide rich molecular information and cost-effectiveness, holds great potential for transformative advancements in early detection and understanding the biochemical changes associated with NDs and TBI. Recent developments in Raman enhancement technologies and advanced data analysis methods have enhanced the applicability of RS in probing the intricate molecular signatures within biological fluids, offering new insights into disease pathology. This review explores the growing role of RS as a promising and emerging tool for disease diagnosis in brain disorders, particularly through the analysis of liquid biopsy. It discusses the current landscape and future prospects of RS in the diagnosis of brain disorders, highlighting its potential as a non-invasive and molecularly specific diagnostic tool.

Systematic Raman spectroscopic study of the complexation of uranyl with fluoride.

A simple aqueous complexing system of UO22+ with F- is selected to systematically illustrate the application of Raman spectroscopy in exploring uranyl(VI) chemistry. Five successive complexes, UO2F+, UO2F2(aq), UO2F3-, UO2F42-, and UO2F53-, are identified, as well as the formation constants except for the 1 : 5 species UO2F53-, which was experimentally observed here for the first time. The standard relative molar Raman scattering intensity for each species is obtained by deconvolution of the spectra collected during titrations. The results of relativistic quantum chemical first-principles and ab initio calculations are presented for the complete set of [UO2(H2O)mFn]2-n complexes (n = 0-5), both for the gas phase as well as for aqueous solution modelling bulk water using the conductor-like screening model. Electronic structure calculations at the Møller-Plesset second-order perturbation theory level provide accurate geometrical parameters and in particular reveal that k water molecules in the second coordination sphere coordinating to the F- ligands in the resulting [UO2(H2O)mFn]2-n(H2O)k complexes need to be treated explicitly in order to obtain vibrational frequencies in very good agreement with experimental data. The thermodynamics and structural information obtained in this work and the developed methodology could be instructive for the future experimental and computational research on the complexation of the uranyl ion.

Enhancing substance identification by Raman spectroscopy using deep neural convolutional networks with an attention mechanism.

Raman spectroscopy is widely used for substance identification, providing molecular information from various components along with noise and instrument interference. Consequently, identifying components based on Raman spectra remains challenging. In this study, we collected Raman spectral data of 474 hazardous chemical substances using a portable Raman spectrometer, resulting in a dataset of 59 468 spectra. Our research employed a deep neural convolutional network based on the ResNet architecture, incorporating an attention mechanism called the SE module. By enhancing the weighting of certain spectral features, the performance of the model was significantly improved. We also investigated the classification predictive performance of the model under small-sample conditions, facilitating the addition of new hazardous chemical categories for future deployment on mobile devices. Additionally, we explored the features extracted by the convolutional neural network from Raman spectra, considering both Raman intensity and Raman shift aspects. We discovered that the neural network did not solely rely on intensity or shift for substance classification, but rather effectively combined both aspects. This research contributes to the advancement of Raman spectroscopy applications for hazardous chemical identification, particularly in scenarios with limited data availability. The findings shed light on the significance of spectral features in the model's decision-making process and have implications for broader applications of deep learning techniques in Raman spectroscopy-based substance identification.