A Comprehensive Review on Raman Spectroscopy Applications

Raman microscopy allows to follow internalization, subcellular accumulation and fate of iron oxide nanoparticles in cells

An important issue in the context of both potenial toxicity of iron oxide nanoparticles (IONP) and their medical applications is tracking of the internalization process of these nanomaterials into living cells, as well as their localization and fate within them. The typical methods used for this purpose are transmission electron microscopy, confocal fluorescence microscopy as well as light-scattering techniques including dark-field microscopy and flow cytometry. All the techniques mentioned have their advantages and disadvantages. Among the problems it is necessary to mention complicated sample preparation, difficult interpretation of experimental data requiring qualified and experienced personnel, different behavior of fluorescently labeled IONP comparing to those label-free or finally the lack of possibility of chemical composition characteristics of nanomaterials. The purpose of the present investigation was the assessment of the usefulness of Raman microscopy for the tracking of the internalization of IONP into cells, as well as the optimization of this process. Moreover, the study focused on identification of the potential differences in the cellular fate of superparamagnetic nanoparticles having magnetite and maghemite core. The Raman spectra of U87MG cells which internalized IONP presented additional bands which position depended on the used laser wavelength. They occurred at the wavenumber range 1700-2400 cm-1 for laser 488 nm and below the wavenumber of 800 cm-1 in case of laser 532 nm. The intensity of the mentioned Raman bands was higher for the green laser (532 nm) and their position, was independent and not characteristic on the primary core material of IONP (magnetite, maghemite). The obtained results showed that Raman microscopy is an excellent, non-destructive and objective technique that allows monitoring the process of internalization of IONP into cells and visualizing such nanoparticles and/or their metabolism products within them at low exposure levels. What is more, the process of tracking IONP using the technique may be further improved by using appropriate wavelength and power of the laser source.

Quantitative detection of heavy metal Cd in vegetable oils: A nondestructive method based on Raman spectroscopy combined with chemometrics

Heavy metal contaminants in vegetable oils can cause irreversible damage to human health. In this study, the quantitative detection of Cd in vegetable oils was investigated based on Raman spectroscopy combined with chemometric methods. The necessary preprocessing of the Raman signal was performed using baseline calibration and the Savitzky-Golay method. Three variable optimization methods were applied to the preprocessed Raman spectra. Namely, bootstrap soft shrinkage, multiple feature spaces ensemble strategy with least absolute shrinkage and selection operator, and competitive adaptive reweighted sampling (CARS), respectively. Partial least squares regression (PLSR) modeling for the determination of Cd in vegetable oils. The results show that three variable optimization algorithms improved the predictive performance of the model. Among them, the CARS-PLSR model has strong generalization performance and robustness. Its prediction coefficient of determination (R P 2 $R_{\mathrm{P}}^2$ ) was 0.9995, the root mean square error of prediction was 0.3533 mg/kg, and the relative prediction deviation was 44.3748, respectively. In summary, rapid quantitative analysis of Cd contamination in vegetable oils can be realized based on Raman spectroscopy combined with chemometrics.

Diabetes and Cognitive Decline: An Innovative Approach to Analyzing the Biophysical and Vibrational Properties of the Hippocampus

Diabetes Mellitus (DM) is a disease characterized by high blood glucose levels, known as hyperglycemia. Diabetes represents a risk factor for the development of neurodegenerative diseases, such as Alzheimer's Disease (AD), one of the most prevalent neurodegenerative diseases worldwide, which leads to progressive mental, behavioral, and functional decline, affecting many brain structures, especially the hippocampus. Here, we aim to characterize the ultrastructural, nanomechanical, and vibrational changes in hyperglycemic hippocampal tissue using atomic force microscopy (AFM) and Raman spectroscopy. DM was induced in rats by streptozotocin injection (type 1) or dietary intervention (type 2). Cryosections of the hippocampus were prepared and analyzed on an MM8 AFM (Bruker) in Peak Force Quantitative Nanomechanics mode, performing 25 μm2 scans in 9 regions of 3 samples from each group. Ultrastructural and nanomechanical data such as surface roughness, area, volume, Young's modulus, and adhesion were evaluated. The hippocampal samples were also analyzed on a T64000 Spectrometer (Horiba), using a laser λ = 632.8 nm, and for each sample, four spectra were obtained in different regions. AFM analyses show changes on the ultrastructural scale since diabetic animals had hippocampal tissue with greater roughness and volume. Meanwhile, diabetic tissues had decreased adhesion and Young's modulus compared to control tissues. These were corroboratedby Raman data that shows changes in the molecular composition of diabetic tissues. The individual spectra show that the most significant changes are in the amide, cholesterol, and lipid bands. Overall, the data presented here show that hyperglycemia induces biophysical alterations in the hippocampal tissue of diabetic rats, providing novel biophysical and vibrational cues on the relationship between hyperglycemia and dementia.

Accuracy and limitations of the bond polarizability model in modeling of Raman scattering from molecular dynamics simulations

Calculation of Raman scattering from molecular dynamics (MD) simulations requires accurate modeling of the evolution of the electronic polarizability of the system along its MD trajectory. For large systems, this necessitates the use of atomistic models to represent the dependence of electronic polarizability on atomic coordinates. The bond polarizability model (BPM) is the simplest such model and has been used for modeling the Raman spectra of molecular systems but has not been applied to solid-state systems. Here, we systematically investigate the accuracy and limitations of the BPM parameterized from the density functional theory results for a series of simple molecules, such as CO2, SO2, H2S, H2O, NH3, and CH4; the more complex CH2O, CH3OH, CH3CH2OH, and thiophene molecules; and the BaTiO3 and CsPbBr3 perovskite solids. We find that BPM can reliably reproduce the overall features of the Raman spectra, such as shifts of peak positions. However, with the exception of highly symmetric systems, the assumption of non-interacting bonds limits the quantitative accuracy of the BPM; this assumption also leads to qualitatively inaccurate polarizability evolution and Raman spectra for systems where large deviations from the ground state structure are present.

Raman spectroscopy - a visit to the literature on plant, food, and agricultural studies

Raman spectroscopy, a fast, non-invasive, and label-free optical technique, has significantly advanced plant and food studies and precision agriculture by providing detailed molecular insights into biological tissues. Utilizing the Raman scattering effect generates unique spectral fingerprints that comprehensively analyze tissue composition, concentration, and molecular structure. These fingerprints are obtained without chemical additives or extensive sample preparation, making Raman spectroscopy particularly suitable for in-field applications. Technological enhancements such as surface-enhanced Raman scattering, Fourier-transform-Raman spectroscopy, and chemometrics have increased Raman spectroscopy sensitivity and precision. These and other advancements enable real-time monitoring of compound translocation within plants and improve the detection of chemical and biological contaminants, essential for food safety and crop optimization. Integrating Raman spectroscopy into agronomic practices is transformative and marks a shift toward more sustainable farming activities. It assesses crop quality - as well as the quality of the food that originated from crop production - early plant stress detection and supports targeted breeding programs. Advanced data processing techniques and machine learning integration efficiently handle complex spectral data, providing a dynamic and detailed view of food conditions and plant health under varying environmental and biological stresses. As global agriculture faces the dual challenges of increasing productivity and sustainability, Raman spectroscopy stands out as an indispensable tool, enhancing farming practices' precision, food safety, and environmental compatibility. This review is intended to select and briefly comment on outstanding literature to give researchers, students, and consultants a reference for works of literature in Raman spectroscopy mainly focused on plant, food, and agronomic sciences. © 2024 Society of Chemical Industry.

In situ single-droplet analysis of emulsified fat using confocal Raman microscopy: insights into crystal network formation within spatial resolution

Fat crystallization is one of the predominant factors influencing the structure and properties of fat-containing emulsions. In the present study, the role of emulsifiers on fat crystallization dynamics within droplet multiphase systems was evaluated via single-droplet analysis, taking advantage of the non-destructive properties of confocal Raman microscopy. Palm oil droplets dispersed in water were used as a model system, due to palm oil's well-known crystallization properties. Emulsion droplets of the same size were generated using two different emulsifiers (Whey Protein Isolate and Tween 60), at various concentrations. Fast and slow cooling treatments were applied to affect fat crystallisation and network formation as well as droplet morphology, and crystallization dynamics. Raman imaging analysis demonstrated that the chemical structure and concentration of the emulsifier significantly influenced both crystal nucleation within the droplets, as well as the spatial distribution and morphology of the fat crystal network. Additionally, analysis of the spectra of the crystallized phase provided essential information regarding the impact of the emulsifiers on the microstructure, degree of structural order, and structural arrangements of the fat crystal networks. Furthermore, by performing single droplet analysis during cooling it was possible to observe shape distortions in Tween 60 stabilized droplets, as a consequence of the formation of a three-dimensional network of fat crystals that strongly interacted with the interface. On the other hand, the droplets retained their shape when whey proteins were absorbed at the interface. Confocal Raman microscopy, in combination with polarized light microscopy, is, therefore, a well-suited tool for in situ, single-droplet analysis of emulsified oil systems, providing essential information about emulsified fat crystallization dynamics, contributing to better understanding and designing products with enhanced structure and function.

A method for accurate identification of Uyghur medicinal components based on Raman spectroscopy and multi-label deep learning

Uyghur medicine is one of the four major ethnic medicines in China and is a component of traditional Chinese medicine. The intrinsic quality of Uyghur medicinal materials will directly affect the clinical efficacy of Uyghur medicinal preparations. However, in recent years, problems such as adulteration of Uyghur medicinal materials and foreign bodies with the same name still exist, so it is necessary to strengthen the quality control of Uyghur medicines to guarantee Uyghur medicinal efficacy. Identifying the components of Uyghur medicines can clarify the types of medicinal materials used, is a crucial step to realizing the quality control of Uyghur medicines, and is also an important step in screening the effective components of Uyghur medicines. Currently, the method of identifying the components of Uyghur medicines relies on manual detection, which has the problems of high toxicity of the unfolding agent, poor stability, high cost, low efficiency, etc. Therefore, this paper proposes a method based on Raman spectroscopy and multi-label deep learning model to construct a model Mix2Com for accurate identification of Uyghur medicine components. The experiments use computer-simulated mixtures as the dataset, introduce the Long Short-Term Memory Model (LSTM) and Attention mechanism to encode the Raman spectral data, use multiple parallel networks for decoding, and ultimately realize the macro parallel prediction of medicine components. The results show that the model is trained to achieve 90.76% accuracy, 99.41% precision, 95.42% recall value and 97.37% F1 score. Compared to the traditional XGBoost model, the method proposed in the experiment improves the accuracy by 49% and the recall value by 18%; compared with the DeepRaman model, the accuracy is improved by 9% and the recall value is improved by 14%. The method proposed in this paper provides a new solution for the accurate identification of Uyghur medicinal components. It helps to improve the quality standard of Uyghur medicinal materials, advance the research on screening of effective chemical components of Uyghur medicines and their action mechanisms, and then promote the modernization and development of Uyghur medicine.

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.

Integrating Wireless Remote Sensing and Sensors for Monitoring Pesticide Pollution in Surface and Groundwater

Water constitutes an indispensable resource crucial for the sustenance of humanity, as it plays an integral role in various sectors such as agriculture, industrial processes, and domestic consumption. Even though water covers 71% of the global land surface, governments have been grappling with the challenge of ensuring the provision of safe water for domestic use. A contributing factor to this situation is the persistent contamination of available water sources rendering them unfit for human consumption. A common contaminant, pesticides are not frequently tested for despite their serious effects on biodiversity. Pesticide determination in water quality assessment is a challenging task because the procedures involved in the extraction and detection are complex. This reduces their popularity in many monitoring campaigns despite their harmful effects. If the existing methods of pesticide analysis are adapted by leveraging new technologies, then information concerning their presence in water ecosystems can be exposed. Furthermore, beyond the advantages conferred by the integration of wireless sensor networks (WSNs), the Internet of Things (IoT), Machine Learning (ML), and big data analytics, a notable outcome is the attainment of a heightened degree of granularity in the information of water ecosystems. This paper discusses methods of pesticide detection in water, emphasizing the possible use of electrochemical sensors, biosensors, and paper-based sensors in wireless sensing. It also explores the application of WSNs in water, the IoT, computing models, ML, and big data analytics, and their potential for integration as technologies useful for pesticide monitoring in water.

Impact of the preservation media on ex vivo bone samples for full field mechanical testing

The preservation method to store bone tissue for posterior analysis is a widespread practice. However, the method's potential influence on the material's mechanical properties is often overlooked during single-point experimentation. Saline and formaldehyde solutions are the most common among the employed preservation media. A full field analysis of the mice femoral bone deformation using non-destructive optical techniques is conducted to assess the influence of the storage media on the viscoelastic properties of the tissue. Three different groups are subjected to a standard three-point bending test. The first group is the control, with fresh post-mortem samples. The second and third groups used saline and formaldehyde solutions, respectively. During the mechanical test, the bone's surface and internal deformation are monitored simultaneously using digital holographic interferometry and Fourier-domain optical coherence tomography. A mechanical comparison among the three groups is presented. The results show that after 48 h of immersion in saline solution, the mice bones keep their viscoelastic behavior similar to fresh bones. Meanwhile, 48 h in formaldehyde modifies the response and affects the marrow structure. The high sensitivity of the optical phase also makes it possible to observe changes in the anisotropy of the samples. As a comparison, Raman spectroscopy analyzes the three bone groups to prove that the preservation media does not affect a single-point inspection.

Bridging Smart Nanosystems with Clinically Relevant Models and Advanced Imaging for Precision Drug Delivery

Intracellular delivery of nano-drug-carriers (NDC) to specific cells, diseased regions, or solid tumors has entered the era of precision medicine that requires systematic knowledge of nano-biological interactions from multidisciplinary perspectives. To this end, this review first provides an overview of membrane-disruption methods such as electroporation, sonoporation, photoporation, microfluidic delivery, and microinjection with the merits of high-throughput and enhanced efficiency for in vitro NDC delivery. The impact of NDC characteristics including particle size, shape, charge, hydrophobicity, and elasticity on cellular uptake are elaborated and several types of NDC systems aiming for hierarchical targeting and delivery in vivo are reviewed. Emerging in vitro or ex vivo human/animal-derived pathophysiological models are further explored and highly recommended for use in NDC studies since they might mimic in vivo delivery features and fill the translational gaps from animals to humans. The exploration of modern microscopy techniques for precise nanoparticle (NP) tracking at the cellular, organ, and organismal levels informs the tailored development of NDCs for in vivo application and clinical translation. Overall, the review integrates the latest insights into smart nanosystem engineering, physiological models, imaging-based validation tools, all directed towards enhancing the precise and efficient intracellular delivery of NDCs.

Opto-Lipidomics of Tissues

Lipid metabolism and signaling play pivotal functions in biology and disease development. Despite this, currently available optical techniques are limited in their ability to directly visualize the lipidome in tissues. In this study, opto-lipidomics, a new approach to optical molecular tissue imaging is introduced. The capability of vibrational Raman spectroscopy is expanded to identify individual lipids in complex tissue matrices through correlation with desorption electrospray ionization (DESI) - mass spectrometry (MS) imaging in an integrated instrument. A computational pipeline of inter-modality analysis is established to infer lipidomic information from optical vibrational spectra. Opto-lipidomic imaging of transient cerebral ischemia-reperfusion injury in a murine model of ischemic stroke demonstrates the visualization and identification of lipids in disease with high molecular specificity using Raman scattered light. Furthermore, opto-lipidomics in a handheld fiber-optic Raman probe is deployed and demonstrates real-time classification of bulk brain tissues based on specific lipid abundances. Opto-lipidomics opens a host of new opportunities to study lipid biomarkers for diagnostics, prognostics, and novel therapeutic targets.

Biosynthesis of ZnO, Bi2O3 and ZnO-Bi2O3 bimetallic nanoparticles and their cytotoxic and antibacterial effects

This work introduces an easy method for producing Bi2O3, ZnO, ZnO-Bi2O3 nanoparticles (NPs) by Biebersteinia Multifida extract. Our products have been characterized through the outcomes which recorded with using powder X-ray diffractometry (PXRD), Raman, energy dispersive X-ray (EDX), field emission-scanning electron microscopy (FE-SEM), and Fourier-transform infrared (FT-IR) techniques. The finding of SEM presented porous structure and spherical morphology for Bi2O3 and ZnO NPs, respectively. While FE-SEM image of bimetallic nanoparticles showed both porous and spherical morphologies for them; so that spherical particles of ZnO have sat on the porous structure of Bi2O3 NPs. According to the PXRD results, the crystallite sizes of Bi2O3, ZnO and ZnO-Bi2O3 NPs have been obtained 57.69, 21.93, and 43.42 nm, respectively. Antibacterial performance of NPs has been studied on Staphylococcus epidermidis and Pseudomonas aeruginosa bacteria, to distinguish the minimum microbial inhibitory concentration (MIC). Antimicrobial outcomes have showed a better effect for ZnO-Bi2O3 NPs. Besides, wondering about the cytotoxic action against cancer cell lines, the MTT results have verified the intense cytotoxic function versus breast cancer cells (MCF-7). According to these observations, obtained products can prosper medical and biological applications.

Raman Flow Cytometry and Its Biomedical Applications

Raman flow cytometry (RFC) uniquely integrates the "label-free" capability of Raman spectroscopy with the "high-throughput" attribute of traditional flow cytometry (FCM), offering exceptional performance in cell characterization and sorting. Unlike conventional FCM, RFC stands out for its elimination of the dependency on fluorescent labels, thereby reducing interference with the natural state of cells. Furthermore, it significantly enhances the detection information, providing a more comprehensive chemical fingerprint of cells. This review thoroughly discusses the fundamental principles and technological advantages of RFC and elaborates on its various applications in the biomedical field, from identifying and characterizing cancer cells for in vivo cancer detection and surveillance to sorting stem cells, paving the way for cell therapy, and identifying metabolic products of microbial cells, enabling the differentiation of microbial subgroups. Moreover, we delve into the current challenges and future directions regarding the improvement in sensitivity and throughput. This holds significant implications for the field of cell analysis, especially for the advancement of metabolomics.

Development of Analytical Method for the Quantitation of Monoclonal Antibodies Solutions via Raman Spectroscopy: The Case of Bevacizumab

Personalized dosages of monoclonal antibodies are being used more regularly to treat various diseases, rendering their quantitation more essential than ever for the right dose administration to the patients. A promising alternative, which overcomes the obstacles of the well-established chromatographic techniques regarding the quantification of biopharmaceuticals, is Raman spectroscopy. This study aimed to develop and validate a novel analytical method for the quantitation of bevacizumab in solutions via Raman spectroscopy. For this purpose, a droplet of the solution was left to dry on a highly reflective carrier and a home-made apparatus was employed for rotation of the sample. Hence, each recorded Raman spectrum was the average of the signal acquired simultaneously from multiple points on a circular circumference. The method was validated, and the detection limit of the antibody was found to be 1.06 mg/mL. Bevacizumab was found to be highly distributed at the formed coffee ring of the dried droplet, though this was a function of solution concentration. Finally, Raman spectra at different distances on the coffee ring were obtained from the four quarters. The lowest bevacizumab detection limit was found at a distance of 75 μm from the external side of the coffee ring and it was determined to be equal to 0.53 mg/mL.

Diagnosing arsenic-mediated biochemical responses in rice cultivars using Raman spectroscopy

Rice (Oryza sativa) is the primary crop for nearly half of the world's population. Groundwater in many rice-growing parts of the world often has elevated levels of arsenite and arsenate. At the same time, rice can accumulate up to 20 times more arsenic compared to other staple crops. This places an enormous amount of people at risk of chronic arsenic poisoning. In this study, we investigated whether Raman spectroscopy (RS) could be used to diagnose arsenic toxicity in rice based on biochemical changes that were induced by arsenic accumulation. We modeled arsenite and arsenate stresses in four different rice cultivars grown in hydroponics over a nine-day window. Our results demonstrate that Raman spectra acquired from rice leaves, coupled with partial least squares-discriminant analysis, enabled accurate detection and identification of arsenic stress with approximately 89% accuracy. We also performed high-performance liquid chromatography (HPLC)-analysis of rice leaves to identify the key molecular analytes sensed by RS in confirming arsenic poisoning. We found that RS primarily detected a decrease in the concentration of lutein and an increase in the concentration of vanillic and ferulic acids due to the accumulation of arsenite and arsenate in rice. This showed that these molecules are detectable indicators of biochemical response to arsenic accumulation. Finally, a cross-correlation of RS with HPLC and ICP-MS demonstrated RS's potential for a label-free, non-invasive, and non-destructive quantification of arsenic accumulation in rice.

Laser-based analytical techniques in cultural heritage science - Tutorial review

This tutorial review combines the fundamentals of the design and operation of lasers with their usage in applications related to conservation and cultural heritage (CH) science - as components of analytical devices for the study of the chemical composition of materials. The development of laser instruments and their fundamental physical background, including a short explanation of their properties and parameters, are briefly summarised, and an overview of different laser-based analytical techniques is given. The analytical techniques covered in this tutorial are divided into three groups based on their technical aspects and properties: (1) vibrational spectroscopy, (2) elemental analysis, and (3) different molecular mass spectrometric techniques.

Unified and vector theory of Raman scattering in gas-filled hollow-core fiber across temporal regimes

Raman scattering has found renewed interest owing to the development of gas-filled hollow-core fibers, which constitute a unique platform for exploration of novel ultrafast nonlinear phenomena beyond conventional solid-core-fiber and free-space systems. Much progress has been made through models for particular interaction regimes, which are delineated by the relation of the excitation pulse duration to the time scales of the Raman response. However, current experimental settings are not limited to one regime, prompting the need for tools spanning multiple regimes. Here, we present a theoretical framework that accomplishes this goal. The theory allows us to review recent progress with a fresh perspective, makes new connections between distinct temporal regimes of Raman scattering, and reveals new degrees of freedom for controlling Raman physics. Specific topics that are addressed include transient Raman gain, the interplay of electronic and Raman nonlinearities in short-pulse propagation, and interactions of short pulses mediated by phonon waves. The theoretical model also accommodates vector effects, which have been largely neglected in prior works on Raman scattering in gases. The polarization dependence of transient Raman gain and vector effects on pulse interactions via phonon waves is investigated with the model. Throughout this Perspective, theoretical results are compared to the results of realistic numerical simulations. The numerical code that implements the new theory is freely available. We hope that the unified theoretical framework and numerical tool described here will accelerate the exploration of new Raman-scattering phenomena and enable new applications.

Development of a universal method for vibrational analysis of the terminal alkyne C≡C stretch

The terminal alkyne C≡C stretch has a large Raman scattering cross section in the "silent" region for biomolecules. This has led to many Raman tag and probe studies using this moiety to study biomolecular systems. A computational investigation of these systems is vital to aid in the interpretation of these results. In this work, we develop a method for computing terminal alkyne vibrational frequencies and isotropic transition polarizabilities that can easily and accurately be applied to any terminal alkyne molecule. We apply the discrete variable representation method to a localized version of the C≡C stretch normal mode. The errors of (1) vibrational localization to the terminal alkyne moiety, (2) anharmonic normal mode isolation, and (3) discretization of the Born-Oppenheimer potential energy surface are quantified and found to be generally small and cancel each other. This results in a method with low error compared to other anharmonic vibrational methods like second-order vibrational perturbation theory and to experiments. Several density functionals are tested using the method, and TPSS-D3, an inexpensive nonempirical density functional with dispersion corrections, is found to perform surprisingly well. Diffuse basis functions are found to be important for the accuracy of computed frequencies. Finally, the computation of vibrational properties like isotropic transition polarizabilities and the universality of the localized normal mode for terminal alkynes are demonstrated.

Calibration of Dual-Channel Raman Spectrometer via Optical Frequency Comb

The portable Raman spectrometer boasts portability, rapid analysis, and high flexibility. It stands as a crucial and powerful technical tool for analyzing the chemical composition of samples, whether biological or non-biological, across diverse fields. To improve the resolution of grating spectrometers and ensure a wide spectral range, many spectrometer systems have been designed with double-grating structures. However, the impact of external forces, such as installation deviations and inevitable collisions, may cause differences between the actual state of the internal spectrometer components and their theoretical values. Therefore, spectrometers must be calibrated to establish the relationship between the wavelength and the pixel positions. The characteristic peaks of commonly used calibration substances are primarily distributed in the 200-2000 cm-1 range. The distribution of characteristic peaks in other wavenumber ranges is sparse, especially for spectrometers with double-channel spectral structures and wide spectral ranges. This uneven distribution of spectral peaks generates significant errors in the polynomial fitting results used to calibrate spectrometers. Therefore, to satisfy the calibration requirements of a dual-channel portable Raman spectrometer with a wide spectral range, this study designed a calibration method based on an optical frequency comb, which generates dense and uniform comb-like spectral signals at equal intervals. The method was verified experimentally and compared to the traditional calibration method of using a mercury-argon lamp. The results showed that the error bandwidth of the calibration results of the proposed method was significantly smaller than that of the mercury-argon lamp method, thus demonstrating a substantial improvement in the calibration accuracy.

Molecularly imprinted polymers (MIP) combined with Raman spectroscopy for selective detection of Δ⁹-tetrahydrocannabinol (THC)

A proof-of-concept sensor is developed for the sensitive and selective detection of Trans-Δ⁹-tetrahydrocannabinol (THC) based on a molecularly imprinted polymer (MIP) synthesized with a THC template which was analyzed using Raman spectroscopy to perform label-free monitoring of THC based on a single identifying Raman peak. The MIP sensor produced a peak at 1614 cm-1 in the Raman spectrum originating from the THC target molecule, allowing for the selective quantification of bound THC with the lowest detection limit of 250 ppm. A higher sensitivity of the MIP to the THC target molecule was observed compared to the non-imprinted polymer (NIP) control which confirmed the presence of THC-specific recognition sites within the synthesized MIP sensing material. The selectivity of this MIP-based sensor was determined by measuring the Raman spectrum of MIP exposed to Cannabidiol (CBD), ethanol, and acetone.

Towards routine organic structure determination using Raman microscopy

Raman microscopy can reveal a compound-specific vibrational "fingerprint" from micrograms of material with no sample preparation. We expect this increasingly available instrumentation to routinely assist synthetic chemists in structure determination; however, interpreting the information-dense spectra can be challenging for unreported compounds. Appropriate theoretical calculations using the highly efficient r2SCAN-3c method can accurately predict peak positions but are less precise in matching peak heights. To limit incorrect biases while comparing experimental and theoretical spectra, we introduce a user-friendly software that gives a match score to assist with structure determination. The capabilities and limitations of this approach are demonstrated for several proof-of-concept examples including the characterization of intermediates in the total synthesis of deoxyaspidodispermine.

Using a 3D Silicon Micro-Channel Device and Raman Spectroscopy for the Analysis of Whole Blood and Abnormal Blood

Blood testing is a crucial application in the field of clinical studies for disease diagnosis and screening, biomarker discovery, organ function assessment, and the personalization of medication. Therefore, it is of the utmost importance to collect precise data in a short time. In this study, we utilized Raman spectroscopy to analyze blood samples for the extraction of comprehensive biological information, including the primary components and compositions present in the blood. Short-wavelength (532 nm green light) Raman scattering spectroscopy was applied for the analysis of the blood samples, plasma, and serum for detection of the biological characteristics in each sample type. Our results indicated that the whole blood had a high hemoglobin content, which suggests that hemoglobin is a major component of blood. The characteristic Raman peaks of hemoglobin were observed at 690, 989, 1015, 1182, 1233, 1315, and 1562-1649 cm-1. Analysis of the plasma and serum samples indicated the presence of β-carotene, which exhibited characteristic peaks at 1013, 1172, and 1526 cm-1. This novel 3D silicon micro-channel device technology holds immense potential in the field of medical blood testing. It can serve as the basis for the detection of various diseases and biomarkers, providing real-time data to help medical professionals and patients better understand their health conditions. Changes in biological data collected in this manner could potentially be used for clinical diagnosis.

Micromolding-Assisted Production of SERS-Active Microcylinders for Size- and Charge-Selective Molecular Detection

Surface-enhanced Raman scattering (SERS) is an effective technique for amplifying the Raman signal of molecules by using metal nanostructures. However, these metal surfaces are susceptible to contamination by undesirable adhesives in complex mixtures, typically necessitating a time-consuming and costly sample pretreatment. In order to circumvent this, metal nanoparticles have been uniformly embedded within microgels by using microfluidics. In this work, we introduce a simple, scalable micromolding method for creating SERS-active cylindrical microgels designed to eliminate the need for pretreatment. These microcylinders are created through the simultaneous photoreduction and photo-cross-linking of precursor solutions. These solutions are optimized for consistent, high-intensity Raman signals as well as molecular size and charge selectivity. A sequential micromolding method is employed to design dual-compartment microcylinders, offering additional functionalities such as optical encoding, magnetoresponsiveness, and dual-charge selectivity. These SERS-active microcylinders provide robust Raman signals of small molecules, even in the presence of adhesive proteins, without compromising sensitivity. To demonstrate this capability, we directly detect pyocyanin in saliva and tartrazine in whole milk without any need for sample pretreatment.

A review on solution- and vapor-responsive sensors for the detection of phthalates

Phthalic acid esters, largely referred to as phthalates, are today acknowledged as important pollutants used in the manufacture of polyvinyl chloride (PVC)-based plastics, whose use extends to almost every aspect of modern life. The risk of exposure to phthalates is particularly relevant as high concentrations are regularly found in drinking water, food-contact materials and medical devices, motivating an immense body of research devoted to methods for their detection in liquid samples. Conversely, phthalate vapors have only recently been acknowledged as potentially important atmospheric pollutants and as early fire indicators; additionally, deposition of these vapors can pose significant problems to the proper functioning of spacecraft and diverse on-board devices, leading to major space agencies recognizing the need of developing vapor-responsive phthalate sensors. In this manuscript we present a literature survey on solution- and vapor-responsive sensors and analytical assays for the detection of phthalates, providing a detailed analysis of a vast array of analytical data to offer a clear idea on the analytical performance (limits of detection and quantification, linear range) and advantages provided by each class of sensor covered in this review (electrochemical, optical and vapor-responsive) in the context of their potential real-life applications; the manuscript also gives detailed fundamental information on the various physicochemical responses exploited by these sensors and assays that could potentially be harnessed by new researchers entering the field.

Observation and Measurement of Ice Morphology in Foods: A Review

Freezing is an effective technology with which to maintain food quality. However, the formation of ice crystals during this process can cause damage to the cellular structure, leading to food deterioration. A good understanding of the relationship between food microstructure and ice morphology, as well as the ability to effectively measure and control ice crystals, is very useful to achieve high-quality frozen foods. Hence, a brief discussion is presented on the fundamentals/principles of optical microscopic techniques (light microscopy), electronic microscopic techniques (transmission electron microscopy (TEM) and scanning electron microscopy (SEM)), as well as other non-invasive techniques (X-rays, spectroscopy, and magnetic resonance) and their application to measuring ice formation rates and characterizing ice crystals, providing insight into the freezing mechanisms as well as direct monitoring of the entire process. And, in addition, this review compares (the negative and positive aspects of) the use of simple and cheap but destructive technologies (optical microscopy) with detailed microscopic technologies at the micro/nanometer scale but with pretreatments that alter the original sample (SEM and TEM), and non-destructive technologies that do not require sample preparation but which have high acquisition and operational costs. Also included are images and examples which demonstrate how useful an analysis using these techniques can be.

Application of surface-enhanced Raman scattering to qualitative and quantitative analysis of arsenic species

Given the toxicity of arsenic, there is an urgent need for the development of efficient and reliable detection systems. Raman spectroscopy, a powerful tool for material characterization and analysis, can be used to explore the properties of a wide range of different materials. Surface-enhanced Raman spectroscopy (SERS) can detect low concentrations of chemicals. This review focuses on the progress of qualitative and quantitative studies of the adsorption processes of inorganic arsenic and organic arsenic in aqueous media using Raman spectroscopy in recent years and discusses the application of Raman spectroscopy theory simulations to arsenic adsorption processes. Sliver nanoparticles are generally used as the SERS substrate to detect arsenic. Inorganic arsenic is chemisorbed onto the silver surface by forming As-O-Ag bonds, and the Raman shift difference in the As-O stretching (∼60 cm-1) between As(V) and As(III) allows SERS to detect and distinguish between As(V) and As(III) in groundwater samples. For organic arsenicals, specific compounds can be identified based on spectral differences in the vibration modes of the chemical bonds. Under the same laser excitation, the intensity of the Raman spectra for different arsenic concentrations is linearly related to the concentration, thus allowing quantitative analysis of arsenic. Molecular modeling of adsorbed analytes via density functional theory calculation (DFT) can predict the Raman shifts of analytes in different laser wavelengths.

Vibrational spectroscopies for biochemical investigation of X-ray exposure effects on SH-SY5Y human neuroblastoma cells

Neuroblastoma is the most recurring cancer in childhood and adolescence. The SH-SY5Y neuroblastoma cell line is generally adopted for elaborating new therapeutical approaches and/or elaborating strategies for the prevention of central nervous system disturbances. In fact, it represents a valid model system for investigating in vitro the effects on the brain of X-ray exposure using vibrational spectroscopies that can detect early radiation-induced molecular alterations of potential clinical usefulness. In recent years, we dedicated significant efforts in the use of Fourier-transform and Raman microspectroscopy techniques for characterizing such radiation-induced effects on SH-SY5Y cells by examining the contributions from different cell components (DNA, proteins, lipids, and carbohydrates) to the vibrational spectra. In this review, we aim at revising and comparing the main results of our studies to provide a wide outlook of the latest outcomes and a framework for future radiobiology research using vibrational spectroscopies. A short description of our experimental approaches and data analysis procedures is also reported.

Surface-Enhanced Raman Scattering (SERS) Substrates Based on Ag-Nanoparticles and Ag-Nanoparticles/Poly (methyl methacrylate) Composites

SERS substrates formed by spherical silver nanoparticles (Ag-NPs) with a 15 nm average diameter adsorbed on Si substrate at three different concentrations and Ag/PMMA composites formed by an opal of PMMA microspheres of 298 nm average diameter were synthesized. The Ag-NPs were varied at three different concentrations. We have observed from SEM micrographs, in the Ag/PMMA composites, the periodicity of the PMMA opals is slightly altered as the Ag-NP concentration is increased; as a consequence of this effect, the PBGs maxima shift toward longer wavelengths, decrease in intensity, and broaden as the Ag-NP concentration is increased in the composites. The performance of single Ag-NP and Ag/PMMA composites as SERS substrates was determined using methylene blue (MB) as a probe molecule with concentrations in the range of 0.5 µM to 2.5 µM. We found that in both single Ag-NP and Ag/PMMA composites as SERS substrates, the enhancement factor (EF) increases as the Ag-NP concentration is increased. We highlight that the SERS substrate with the highest concentration of Ag-NPs has the highest EF due to the formation of metallic clusters on the surface, which generates more "hot spots". The comparison of the EFs of the single Ag-NP with those of Ag/PMMA composite SERS substrates shows that the EFs of the former are nearly 10-fold higher than those of Ag/PMMA composites. This result is obtained probably due to the porosity of the PMMA microspheres that decreases the local electric field strength. Furthermore, PMMA exerts a shielding effect that affects the optical efficiency of Ag-NPs. Moreover, the metal-dielectric surface interaction contributes to the decrease in the EF. Other aspect to consider in our results is in relation to the difference in the EF of the Ag/PMMA composite and Ag-NP SERS substrates and is due to the existing mismatch between the frequency range of the PMMA opal stop band and the LSPR frequency range of the Ag metal nanoparticles adsorbed on the PMMA opal host matrix.

Molecular Fingerprint Detection Using Raman and Infrared Spectroscopy Technologies for Cancer Detection: A Progress Review

Molecular vibrations play a crucial role in physical chemistry and biochemistry, and Raman and infrared spectroscopy are the two most used techniques for vibrational spectroscopy. These techniques provide unique fingerprints of the molecules in a sample, which can be used to identify the chemical bonds, functional groups, and structures of the molecules. In this review article, recent research and development activities for molecular fingerprint detection using Raman and infrared spectroscopy are discussed, with a focus on identifying specific biomolecules and studying the chemical composition of biological samples for cancer diagnosis applications. The working principle and instrumentation of each technique are also discussed for a better understanding of the analytical versatility of vibrational spectroscopy. Raman spectroscopy is an invaluable tool for studying molecules and their interactions, and its use is likely to continue to grow in the future. Research has demonstrated that Raman spectroscopy is capable of accurately diagnosing various types of cancer, making it a valuable alternative to traditional diagnostic methods such as endoscopy. Infrared spectroscopy can provide complementary information to Raman spectroscopy and detect a wide range of biomolecules at low concentrations, even in complex biological samples. The article concludes with a comparison of the techniques and insights into future directions.

Portable near-infrared and Raman spectroscopic devices as complementary tools for instantaneous quality control of turmeric powder

INTRODUCTION

Process analytical technology (PAT) guidance is implemented in the quality assurance of phytocompounds to achieve the Industry 4.0 concept. Near-infrared (NIR) and Raman spectroscopies are feasible for rapid, reliable quantitative analysis through transparent packaging without removing the samples from their original containers. These instruments can serve PAT guidance.

OBJECTIVE

This study aimed to develop online portable NIR and Raman spectroscopic methods for quantifying total curcuminoids in turmeric samples through a plastic bag. The method mimicked an in-line measurement mode in PAT compared with placing samples into a glass vessel (at-line mode).

MATERIALS AND METHODS

Sixty-three curcuminoid standard-spiked samples were prepared. Then, 15 samples were randomly selected as fixed validation samples, and 40 of the 48 remaining samples were chosen as calibration set. The results obtained from the partial least square regression (PLSR) models constructed by using the spectra acquired from NIR and Raman were compared with the reference values from high-performance liquid chromatography (HPLC).

RESULTS

The optimum PLSR model of at-line Raman was achieved with three latent variables and a root mean square error of prediction (RMSEP) of 0.46. Meanwhile, the PLSR model of at-line NIR with one latent variable offered an RMSEP of 0.43. For the in-line mode, PLSR models created from Raman and NIR spectra had one latent variable with RMSEP of 0.49 and 0.42, respectively. The R² values for prediction were 0.88-0.92.

CONCLUSION

The models established from the spectra from portable NIR and Raman spectroscopic devices with the appropriate spectral pretreatments allowed the determination of total curcuminoid contents through plastic bag.

Raman spectroscopy: Principles and recent applications in food safety

Food contaminant is a significant issue because of the adverse effects on human health and economy. Traditional detection methods such as liquid chromatography-mass spectroscopy for detecting food contaminants are expensive and time-consuming, and require highly-trained personnel and complicated sample pretreatment. Raman spectroscopy is an advanced analytical technique in a manner of non-destructive, rapid, cost-effective, and ultrasensitive sensing various hazards in agri-foods. In this chapter, we summarized the principle of Raman spectroscopy and surface enhanced Raman spectroscopy, the methods to process Raman spectra, the recent applications of Raman/SERS (surface-enhanced Raman spectroscopy) in detecting chemical contaminants (e.g., pesticides, antibiotics, mycotoxins, heavy metals, and food adulterants) and microbiological hazards (e.g., Salmonella, Campylobacter, Shiga toxigenic E. coli, Listeria, and Staphylococcus aureus) in foods.

Application of Spectroscopy Techniques for Monitoring (Bio)Catalytic Processes in Continuously Operated Microreactor Systems

In the last twenty years, the application of microreactors in chemical and biochemical industrial processes has increased significantly. The use of microreactor systems ensures efficient process intensification due to the excellent heat and mass transfer within the microchannels. Monitoring the concentrations in the microchannels is critical for a better understanding of the physical and chemical processes occurring in micromixers and microreactors. Therefore, there is a growing interest in performing in-line and on-line analyses of chemical and/or biochemical processes. This creates tremendous opportunities for the incorporation of spectroscopic detection techniques into production and processing lines in various industries. In this work, an overview of current applications of ultraviolet–visible, infrared, Raman spectroscopy, NMR, MALDI-TOF-MS, and ESI-MS for monitoring (bio)catalytic processes in continuously operated microreactor systems is presented. The manuscript includes a description of the advantages and disadvantages of the analytical methods listed, with particular emphasis on the chemometric methods used for spectroscopic data analysis.

An Overview on Carbon Quantum Dots Optical and Chemical Features

Carbon quantum dots are the materials of a new era with astonishing properties such as high photoluminescence, chemical tuneability and high biocompatibility. Since their discovery, carbon quantum dots have been described as nanometric high-fluorescent carbon nanoparticles, but this definition has become weaker year after year. Nowadays, the classification and the physical explanation of carbon quantum dots optical properties and their chemical structure remain matter of debate. In this review, we provide a clear discussion on these points, providing a starting point for the rationalization of their classification and a comprehensive view on the optical and chemical features of carbon quantum dots.

Plasmonic surface-enhanced Raman scattering nano-substrates for detection of anionic environmental contaminants: Current progress and future perspectives

Surface-enhanced Raman scattering spectroscopy (SERS) is a powerful technique of vibrational spectroscopy based on the inelastic scattering of incident photons by molecular species. It has unique properties such as ultra-sensitivity, selectivity, non-destructivity, speed, and fingerprinting properties for analytical and sensing applications. This enables SERS to be widely used in real-world sample analysis and basic plasmonic mechanistic studies. However, the desirable properties of SERS are compromised by the high cost and low reproducibility of the signals. The development of multifunctional, stable and reusable nano-engineered SERS substrates is a viable solution to circumvent these drawbacks. Recently, plasmonic SERS active nano-substrates with various morphologies have attracted the attention of researchers due to promising properties such as the formation of dense hot spots, additional stability, tunable and controlled morphology, and surface functionalization. This comprehensive review focused on the current advances in the field of SERS active nanosubstrates suitable for the detection and quantification of anionic environmental pollutants. The common fabrication methods, including the techniques for morphological adjustments and surface modification, substrate categories, and the design of nanotechnologically fabricated plasmonic SERS substrates for anion detection are systematically presented. Here, the need for the design, synthesis, and functionalization of SERS nano-substrates for anions of great environmental importance is explained in detail. In addition, the broad categories of SERS nano-substrates, namely colloid-based SERS substrates and solid-support SERS substrates are discussed. Moreover, a brief discussion of SERS detection of certain anionic pollutants in the environment is presented. Finally, the prospects in the fabrication and commercialization of pilot-scale handheld SERS sensors and the construction of smart nanosubstrates integrated with novel amplifying materials for the detection of anions of environmental and health concern are proposed.

Effect of Red Mud Addition on Electrical and Magnetic Properties of Hemp-Derived-Biochar-Containing Epoxy Composites

Waste stream valorization is a difficult task where the economic and environmental issues must be balanced. The use of complex metal-rich waste such as red mud is challenging due to the wide variety of metal oxides present such as iron, aluminum, and titanium. The simple separation of each metal is not economically feasible, so alternative routes must be implemented. In this study, we investigated the use of red mud mixed with hemp waste to produce biochar with high conductivity and good magnetic properties induced by the reduction of the metal oxides present in the red mud through carbothermal processes occurring during the co-pyrolysis. The resulting biochar enriched with thermally-reduced red mud is used for the preparation of epoxy-based composites that are tested for electric and magnetic properties. The electric properties are investigated under DC (direct current) regime with or without pressure applied and under AC (alternating current) in a frequency range from 0.5 up to 16 GHz. The magnetic measurements show the effective tailoring of hemp-derived biochar with magnetic structures during the co-pyrolytic process.

High-throughput computation of Raman spectra from first principles

Raman spectroscopy is a widely-used non-destructive material characterization method, which provides information about the vibrational modes of the material and therefore of its atomic structure and chemical composition. Interpretation of the spectra requires comparison to known references and to this end, experimental databases of spectra have been collected. Reference Raman spectra could also be simulated using atomistic first-principles methods but these are computationally demanding and thus the existing databases of computational Raman spectra are fairly small. In this work, we developed an optimized workflow to calculate the Raman spectra efficiently and taking full advantage of the phonon properties found in existing material databases. The workflow was benchmarked and validated by comparison to experiments and previous computational methods for select technologically relevant material systems. Using the workflow, we performed high-throughput calculations for a large set of materials (5099) belonging to many different material classes, and collected the results to a database. Finally, the contents of database are analyzed and the calculated spectra are shown to agree well with the experimental ones.

An experimental guide to in operando electrochemical Raman spectroscopy

Electrochemical Raman spectroscopy can provide valuable insights into electrochemical reaction mechanisms. However, it also shows various pitfalls and challenges. This paper gives an overview of the necessary theoretical background, crucial practical considerations for successful measurement, and guidance for in situ/in operando electrochemical Raman spectroscopy. Several parameters must be optimized for suitable reaction and measurement conditions. From the experimental side, considerations for the setup, suitable signal enhancement methods, choice of material, laser, and objective lens are discussed. Different interface phenomena are reviewed in the context of data interpretation and evaluation.

Graphical Abstract

Comprehensive Study of the Ammonium Sulfamate-Urea Binary System

The physicochemical properties of binary systems are of great importance for the application of the latter. We report on the investigation of an ammonium sulfamate-urea binary system with different component ratios using a combination of experimental (FTIR, XRD, TGA/DSC, and melting point) and theoretical (DFT, QTAIM, ELF, RDG, ADMP, etc.) techniques. It is shown that, at a temperature of 100 °C, the system under study remains thermally and chemically stable for up to 30 min. It was established using X-ray diffraction analysis that the heating time barely affects the X-ray characteristics of the system. Data on the aggregate states in specified temperature ranges were obtained with thermal analysis and determination of the melting point. The structures of the ammonium sulfamate-urea system with different component ratios were optimized within the density functional theory. The atom-centered density matrix propagation calculation of the ammonium sulfamate-urea system with different component ratios was performed at temperatures of 100, 300, and 500 K. Regardless of the component ratio, a regular increase in the potential energy variation (curve amplitude) with an increase in temperature from 100 to 500 K was found.

Spontaneous Scattering of Raman Photons from Cavity-QED Systems in the Ultrastrong Coupling Regime

We show that spontaneous Raman scattering of incident radiation can be observed in cavity-QED systems without external enhancement or coupling to any vibrational degree of freedom. Raman scattering processes can be evidenced as resonances in the emission spectrum, which become clearly visible as the cavity-QED system approaches the ultrastrong coupling regime. We provide a quantum mechanical description of the effect, and show that ultrastrong light-matter coupling is a necessary condition for the observation of Raman scattering. This effect, and its strong sensitivity to the system parameters, opens new avenues for the characterization of cavity QED setups and the generation of quantum states of light.

Fabrication of Three-Dimensional ZnO: Ga@ITO@Ag SERS-Active Substrate for Sensitive and Repeatable Detectability

Vertically aligned ZnO: Ga nanotowers can be directly synthesized on a glass substrate with a ZnO seed film via the chemical bath method. A novel heterostructure of ZnO: Ga@ITO@Ag nanotowers was subsequently deposited in the ITO layer and Ag nanoparticles via the facile two-step ion-sputtering processes on the ZnO: Ga nanotowers. The appropriate ion-sputtering times of the ITO layer and Ag nanoparticles can benefit the fabrication of ZnO: Ga@ITO@Ag nanotowers with higher surface-enhanced Raman scattering (SERS) enhancement in detecting rhodamine 6G (R6G) molecules. Compared with ZnO: Ga@Ag nanotowers, ZnO: Ga@ITO@Ag nanotowers exhibited a high SERS enhancement factor of 2.25 × 10⁸ and a lower detection limit (10^-14 M) for detecting R6G molecules. In addition, the ITO layer used as an intermediate layer between ZnO: Ga nanotowers and Ag nanoparticles can improve SERS enhancement, sensitivity, uniformity, reusability, detection limit, and stability for detecting amoxicillin molecules. This phenomenon shall be ascribed to the ITO layer exhibiting a synergistic Raman enhancement effect through interfacial charge transfer for enhancing SERS activity. As a result, ZnO: Ga@ITO@Ag nanotowers can construct a three-dimensional SERS substrate for potential applications in environmentally friendly and cost-effective chemical or drug detection.

Raman Spectroscopy in Open-World Learning Settings Using the Objectosphere Approach

Raman spectroscopy, combined with machine learning techniques, holds great promise for many applications as a rapid, sensitive, and label-free identification method. Such approaches perform well when classifying spectra of chemical species that were encountered during the training phase. That is, species that are known to the neural network. However, in real-world settings, such as in clinical applications, there will always be substances whose spectra have not yet been taken. When the neural network encounters these new species during the testing phase, the number of false positives becomes uncontrollable, limiting the usefulness of these techniques, especially in public safety applications. To overcome these barriers, we implemented the recently introduced Entropic Open Set and Objectosphere loss functions. To demonstrate the efficacy and efficiency of this approach, we compiled a database of hyperspectral Raman images of 40 chemical species separating them into three class categorizations. The known class consisted of 20 biologically relevant species comprising amino acids, the ignored class was 10 "irrelevant" species comprising bio-related chemicals, and the never seen before class was 10 various chemical species that the neural network had not seen before. We show that this approach not only enables the network to effectively separate the unknown species while preserving high accuracy on the known ones and reducing false positives but also performs better than the current gold standards in machine learning techniques. This opens the door to using Raman spectroscopy, combined with our novel machine learning algorithm, in a variety of practical applications. Availability and implementation: freely available on the web at https://github.com/BalytskyiJaroslaw/RamanOpenSet.git.

Biochemical changes of macrophages and U87MG cells occurring as a result of the exposure to iron oxide nanoparticles detected with the Raman microspectroscopy

The core size of iron oxide nanoparticles (IONPs) is a crucial factor defining not only their magnetic properties but also toxicological profile and biocompatibility. On the other hand, particular IONPs may induce different biological response depending on the dose, exposure time, but mainly depending on the examined system. New light on this problem may be shed by the information concerning biomolecular anomalies appearing in various cell lines in response to the action of IONPs with different core diameters and this was accomplished in the present study. Using Raman microscopy we studied the abnormalities in the accumulation of proteins, lipids and organic matter within the nucleus, cytoplasm and cellular membrane of macrophages, HEK293T and U87MG cell line occurring as a result of 24-hour long exposure to PEG-coated magnetite IONPs. The examined nanoparticles had 5, 10 and 30 nm cores and were administered in doses 5 and 25 μg Fe/ml. The obtained results showed significant anomalies in biochemical composition of macrophages and the U87MG cells, but not the HEK293T cells, occurring as a result of exposure to all of the examined nanoparticles. However, IONPs with 10 nm core diminished the accumulation of biomolecules in cells only when they were administered at a larger dose. The Raman spectra recorded for the macrophages subjected to 30 nm IONPs and for the U87MG cells exposed to 5 and 10 nm showed the presence of additional bands in the wavenumber range 1700-2400 cm^-1, probably resulting from the appearance of Fe adducts within cells. Our results indicate, moreover, that smaller IONPs may be effectively internalized into the U87MG cells, which points at their diagnostic/therapeutic potential in the case of glioblastoma multiforme.

Label-Free Detection of DNA via Surface-Enhanced Raman Spectroscopy Using Au@Ag Nanoparticles

DNA is a building block of life; surface-enhanced Raman spectroscopy (SERS) has been broadly applied in the detection of biomolecules but there are challenges in obtaining high-quality DNA SERS signals under non-destructive conditions. Here, we developed a novel label-free approach for DNA detection based on SERS, in which the Au@AgNPs core-shell structure was selected as the enhancement substrate, which not only solved the problem of the weak enhancement effect of gold nanoparticles but also overcame the disadvantage of the inhomogeneous shapes of silver nanoparticles, thereby improving the sensitivity and reproducibility of the SERS signals of DNA molecules. The method obtained SERS signals for four DNA bases (A, C, G, and T) without destroying the structure, then further detected and qualified different specific structures of DNA molecules. These results promote the application of SERS technology in the field of biomolecular detection.

Analysis and comparison of machine learning methods for blood identification using single-cell laser tweezer Raman spectroscopy

Raman spectroscopy, a "fingerprint" spectrum of substances, can be used to characterize various biological and chemical samples. To allow for blood classification using single-cell Raman spectroscopy, several machine learning algorithms were implemented and compared. A single-cell laser optical tweezer Raman spectroscopy system was established to obtain the Raman spectra of red blood cells. The Boruta algorithm extracted the spectral feature frequency shift, reduced the spectral dimension, and determined the essential features that affect classification. Next, seven machine learning classification models are analyzed and compared based on the classification accuracy, precision, and recall indicators. The results show that support vector machines and artificial neural networks are the two most appropriate machine learning algorithms for single-cell Raman spectrum blood classification, and this finding provides essential guidance for future research studies.

Thermally Stable Magneto-Plasmonic Nanoparticles for SERS with Tunable Plasmon Resonance

Bifunctional magneto-plasmonic nanoparticles that exhibit synergistically magnetic and plasmonic properties are advanced substrates for surface-enhanced Raman spectroscopy (SERS) because of their excellent controllability and improved detection potentiality. In this study, composite magneto-plasmonic nanoparticles (Fe3O4@AgNPs) were formed by mixing colloid solutions of 50 nm-sized magnetite nanoparticles with 13 nm-sized silver nanoparticles. After drying of the layer of composite Fe3O4@AgNPs under a strong magnetic field, they outperformed the conventional silver nanoparticles during SERS measurements in terms of signal intensity, spot-to-spot, and sample-to-sample reproducibility. The SERS enhancement factor of Fe3O4@AgNP-adsorbed 4-mercaptobenzoic acid (4-MBA) was estimated to be 3.1 × 107 for a 633 nm excitation. In addition, we show that simply by changing the initial volumes of the colloid solutions, it is possible to control the average density of the silver nanoparticles, which are attached to a single magnetite nanoparticle. UV-Vis and SERS data revealed a possibility to tune the plasmonic resonance frequency of Fe3O4@AgNPs. In this research, the plasmon resonance maximum varied from 470 to 800 nm, suggesting the possibility to choose the most suitable nanoparticle composition for the particular SERS experiment design. We emphasize the increased thermal stability of composite nanoparticles under 532 and 442 nm laser light irradiation compared to that of bare Fe3O4 nanoparticles. The Fe3O4@AgNPs were further characterized by XRD, TEM, and magnetization measurements.