Raman Imaging Research Articles

Toward video-rate compressive spontaneous Raman imaging via single-photon avalanche diode arrays.

Spontaneous Raman microscopy is well-known for its remarkable chemical contrast yet suffers from slow acquisition speeds. Recently, the compressive Raman microspectroscopy framework has shown that a significant speed advantage is brought by leveraging shot-noise-limited detection using a single-photon avalanche diode (SPAD). However, current imaging speeds of compressive Raman architectures are fundamentally limited by SPAD sensitivity and dead time. Here, we demonstrate an efficient and scalable compressive Raman parallelization scheme based on SPAD arrays. We show that parallelization using line excitation, instead of spatial multiplexing, allows to reach effective pixel dwell times (τ pdt ) of 0.8 µs. Such fast speed represents over one order-of-magnitude speed-up over previous demonstrations. This effective parallelization not only allows for demonstrating unprecedented chemical imaging speeds using the otherwise weak spontaneous Raman effect but also paves the way for true video-rate inexpensive molecular microspectroscopy.

Volumetric Imaging From Raman Perspective: Review and Prospect

AbstractVolumetric imaging, which supports quantitative and comprehensive assessment of a 3D sample from an entire volume, has attracted tremendous attention in biomedical research. Fluorescence imaging techniques, such as optical sectioning and light sheet microscopy, enable to reconstruct the 3D distribution of chemicals within a sample. However, current methods rely on exogenous labels, from which considerable perturbation may be introduced in living systems. Raman imaging offers a feasible solution to visualize components in biological samples in a label‐free manner. Besides, the integration of Raman microscopy with 3D approaches will benefit the research of biomedical samples on novel devices, which is dominated by the strongly enhanced spatial resolution, imaging speed, and overall field of view as well as complemented more details of samples. In this overview, recent achievements in 3D visualization of biological samples from the Raman perspective, are explored including scanning mechanism, light sheet, tomography strategy, compressive sensing, holography, and tissue clearing. Importantly, these platforms are compatible with biomedical research, thus allowing the imaging of chemical constituents and the distribution of samples in a whole volume. As a unique volumetric imaging tool for biological discovery, these methods may provide a strategy to accelerate new discoveries across diverse fields of research.

Mapping cellular stress and lipid dysregulation in Alzheimer-related progressive neurodegeneration using label-free Raman microscopy.

Aβ plaques are a main feature of Alzheimer's disease, and pathological alterations especially in their microenvironment have recently come into focus. However, a holistic imaging approach unveiling these changes and their biochemical nature is still lacking. In this context, we leverage confocal Raman microscopy as unbiased tool for non-destructive, label-free differentiation of progressive biomolecular changes in the Aβ plaque microenvironment in brain tissue of a murine model of cerebral amyloidosis. By developing a detailed approach, overcoming many challenges of chemical imaging, we identify spatially-resolved molecular signatures of disease-associated structures. Specifically, our study reveals nuclear condensation, indicating cellular degeneration, and increased levels of cytochrome c, showing mitochondrial dysfunction, in the vicinity of Aβ plaques. Further, we observe severe accumulation of especially unsaturated lipids. Thus, our study contributes to a comprehensive understanding of disease progression in the Aβ plaque microenvironment, underscoring the prospective of Raman imaging in neurodegenerative disorder research.

Remote Detection and Identification of Plastics with Hyperspectral Raman Imaging Lidar

Remote Detection and Identification of Plastics with Hyperspectral Raman Imaging Lidar

Oxidation kinetics and electrical properties of oxide scales formed under exposure to air and Ar–H2-H2O atmospheres on the Crofer 22 H ferritic steel for high-temperature applications such as interconnects in solid oxide cell stacks

A 100 h isothermal oxidation kinetics study for Crofer 22H was conducted in air and the Ar–H2-H2O gas mixture (p(H2)/p(H2O) = 94/6) in the range of 973–1123 K. The parabolic rate constant was independent of oxygen partial pressure in the range from 6.2 × 10−24 to 0.21 atm at 1023 and 1073 K, while at 973 and 1123 K it was higher in air than in Ar–H2-H2O. The scales consisted of Cr2O3 and manganese chromium spinel with an Mn:Cr ratio dependent on the oxidation conditions. Cross-scale resistance was evaluated with regards to the application of the steel in solid oxide fuel cells using a number of methods, including X-ray diffraction, scanning electron microscopy, confocal Raman imaging and area-specific resistance measurements.

The Identification of Six Estrogen Preparations by Combining Thin-Layer Chromatography with Micro-Raman Imaging Spectroscopy

A method for identifying six estrogen preparations by thin-layer chromatography combined with Raman imaging microscopy (TLC-RIM) was established. An appropriate pretreatment method was adopted to extract and purify the six estrogen preparations. After that, each estrogen extraction solution was spotted on a thin-layer chromatography plate. Estriol (E3), estradiol (E2), estradiol valerate (EV), estradiol benzoate (EB), nilestriol (CEE), and ethinylestradiol (EE2) were separated by TLC, and their Rf value and localization were determined under a UV lamp at 254 nm, followed by the in situ enrichment of the drug component. Using a 532 nm laser as the light source, the Raman scattering spectrum of the component was directly collected by micro-Raman imaging. The Rf values after TLC separation of the six estrogens and their Raman spectra can, respectively, reflect differences in polarity and structure, and they are not affected by the excipients of preparation. The detection limits of the six estrogens are 0.636, 1.00, 0.687, 0.497, 0.649, and 0.626 mg/mL. Based on the intensity of the minimum characteristic peak, the stability results within 40 min showed that the RSD of each substance is 1.34, 2.06, 1.65, 3.99, 1.16, and 2.71%, respectively. This method has strong specificity, good stability, and high sensitivity, and it can provide a new reference for improving the identification standards of estrogen preparations.

Femtosecond Laser-Induced Recrystallized Nanotexturing for Identity Document Security With Physical Unclonable Functions.

Counterfeit identity (ID) documents pose a serious threat to personal credit and national security. As a promising candidate, optical physical unclonable functions (PUFs) offer a robust defense mechanism against counterfeits. Despite the innovations in chemically synthesized PUFs, challenges persist, including harmful chemical treatments, low yields, and incompatibility of reaction conditions with the ID document materials. More notably, surface relief nanostructures for PUFs, such as wrinkles, are still at risk of being replicated through scanning lithography or nanoimprint. Here, a femtosecond laser-induced recrystallized silicon nanotexture is reported as latent PUF nanofingerprint for document anti-counterfeiting. With femtosecond laser irradiation, nanotextures spontaneously emerge within 100ms of exposure. By introducing a low-absorption metal layer, surface plasmon polariton waves are excited on the silicon-metal multilayer nanofilms with long-range boosting, ensuring the uniqueness and non-replicability of the final nanotextures. Furthermore, the femtosecond laser induces a phase transition in the latent nanotexture from amorphous to polycrystalline state, rather than creating replicable relief wrinkles. The random nanotextures are easily identifiable through optical microscopy and Raman imaging, yet they remain undetectable by surface characterization methods such as scanning electron and atomic force microscopies. This property significantly hinders counterfeiting efforts, as it prevents the precise replication of these nanostructures.

Deep learning-driven super-resolution in Raman hyperspectral imaging: Efficient high-resolution reconstruction from low-resolution data

Deep learning (DL) has become an indispensable tool in hyperspectral data analysis, automatically extracting valuable features from complex, high-dimensional datasets. Super-resolution reconstruction, an essential aspect of hyperspectral data, involves enhancing spatial resolution, particularly relevant to low-resolution hyperspectral data. Yet, the pursuit of super-resolution in hyperspectral analysis is fraught with challenges, including acquiring ground truth high-resolution data for training, generalization, and scalability. The pressing issue of extended spectral acquisition times, notably for high-resolution scans, is a significant roadblock in hyperspectral imaging. Super-resolution methods offer a promising solution by providing higher spatial resolution data to expedite data collection and yield more efficient outcomes. This paper delves into a practical application of these concepts using Raman imaging, where spectral acquisition times can be prohibitively long. In this context, DL-based super-resolution models demonstrate their efficacy by predicting and reconstructing high-resolution Raman data from low-resolution input, eliminating the need for resource-intensive high-resolution scans. While previous work often relied on substantial high-resolution datasets, this study showcases the ability to achieve similar outcomes even with limited data, presenting a more practical and cost-effective approach. The results offer a glimpse into the transformative potential of this technology to streamline hyperspectral imaging applications by saving valuable time and resources through the successful generation of high-resolution data from low-resolution inputs.

Chemical Distribution Uniformity Assessment of "Intra-Tablet" by Hyperspectral Raman Imaging Analysis.

This study aimed to develop a new index, Distribution Uniformity Index (DUI), to assess the "intra-tablet" homogeneity. High-resolution hyperspectral Raman imaging was adopted to scan a tablet to get the components' distribution. The heuristic algorithm was applied to generate a Raman heatmap with RGB colors quantitatively correlated with the concentrations of each component. DUI is defined as the ratio of the area under the uniformity curve of the sample image to that of the randomized image. The accuracy and applicability of DUI were verified by constructing model images with controlled uniformity and random regions. The effects of "intra-tablet" homogeneity on the disintegration and dissolution of spironolactone tablets were investigated. DUI value was directly obtained from heuristic visual analysis of macro-pixel from hyperspectral Raman images. A good linear relationship and good repeatability were confirmed between DUI and the uniformity of model images. The size of CaSO4·2H2O affected the "intra-tablet" homogeneity of spironolactone tablets, which was detected by the DUI value. The better "intra-tablet" homogeneity led to a higher disintegration and dissolution of spironolactone tablets. DUI represents a novel index to evaluate the "intra-tablet" homogeneity and is beneficial for formulation research and development.

Improving LIBS-based mineral identification with Raman imaging and spectral knowledge distillation

Combining data from different sensing modalities has been a promising research topic for building better and more reliable data-driven models. In particular, it is known that multimodal spectral imaging can improve the analytical capabilities of standalone spectroscopy techniques through fusion, hyphenation, or knowledge distillation techniques. In this manuscript, we focus on the latter, exploring how one can increase the performance of a Laser-induced Breakdown Spectroscopy system for mineral classification problems using additional spectral imaging techniques. Specifically, focusing on a scenario where Raman spectroscopy delivers accurate mineral classification performance, we show how to deploy a knowledge distillation pipeline where Raman spectroscopy may act as an autonomous supervisor for LIBS. For a case study concerning a challenging Li-bearing mineral identification of spodumene and petalite, our results demonstrate the advantages of this method in improving the performance of a single-technique system. LIBS trained with labels obtained by Raman presents an enhanced classification performance. Furthermore, leveraging the interpretability of the model deployed, the workflow opens opportunities for the deployment of assisted feature discovery pipelines, which may impact future academic and industrial applications.

Tobacco stalk nanofibrillated cellulose: An eco-friendly binder on fluidized bed granulation

The search for new excipients that provide formulation alternatives over existing ones and that are derived from natural resources has advanced over the years. Emerging from this scenario, an important alternative to traditional excipients is nanofibrillated cellulose (NFC), which is generally obtained from agricultural wastes and is a cellulose-rich material. Most of the time, the nanofibers are presented in aqueous dispersions that form a highly entangled network of fibrous particles that exhibit gel-like behavior. Therefore, the present work aimed to explore the gel-like behavior of NFC from tobacco stalks to evaluate it as a binder in a fluid bed granulation process for further application as a pharmaceutical ingredient. The granulation process was carried out using top spray mode with an inlet air temperature of 80 °C, airflow between 9 and 11 mm3 h−1, and atomizing air pressure of 0.7 bar. Povidone (PVP) was used as a standard binder for comparison purposes. The binder systems were sprayed on the powder blend of theophylline and maltodextrin at a feed rate of 3.05 mL⋅min−1. Granule properties, such as yield, moisture content, morphology, label-free Raman imaging, particle size distribution, drug content, and flow properties, were evaluated. The process using NFC provided granules with excellent drug uniformity, low moisture content (<2 %), high yield (>92 %), and passable flow properties. Their behavior was comparable to that of granules prepared with the classical PVP binder system. Therefore, this study opens a perspective for the reuse of NFC from tobacco stalk, an agricultural waste, as an alternative binder for the granulation process, contributing to environmental sustainability.

Raman spectroscopy and imaging in bio and life sciences

AbstractRaman spectroscopy is a well‐known technique to study different materials and determine their chemical composition. It is based on the inelastic scattering of light and provides a unique spectrum for the sample under study. The advances in lasers and detectors, their combination with imaging systems and the discovery of new phenomena have expanded the use of this technique in several research fields. In the particular case of biology and life sciences, it has been used to provide chemical and compositional information on plant and animal tissues, cells, biofluids, etc., with the advantage that the presence of water molecules normally does not cause interference in the measured signal.

Microscopic water dispersion and hydrogen-bonding structures in margarine spreads with Raman hyperspectral imaging and machine learning

Margarine, a water-in-oil (W/O) emulsion, offers advantages such as lower costs in comparison to similar products, but large amounts of saturated fats pose health risks. Reduction of saturated fat content is difficult and often leads to “oil-off,” i.e., the seepage of liquid oil from the mixture, resulting in undesirable appearance and texture. Investigations into the phenomenon have often focused on morphology at the water-oil interfaces, and this work establishes Raman imaging as a powerful application for observing microscopic morphologies of W/O emulsions. We analyze morphologies of 5 distinct margarine spreads that differ in manufacturing date, formulation, and manufacturing process. More robust H-bonding in the oil phase of the emulsions co-occurred with smaller amounts of oil-off, suggesting that H-bonding interactions between emulsifier molecules, water, and crystallized fats in the lipid phase of the W/O emulsions results in an emulsion that is less susceptible to the production of oil-off.

Insights into pharmaceutical co-crystallization using coherent Raman microscopy

Formulating active pharmaceutical ingredients (APIs) as co-crystals requires a thorough understanding of co-crystallization behavior under different process conditions. This study employs two forms of coherent Raman microscopy, narrowband coherent anti-Stokes Raman scattering (CARS) and stimulated Raman scattering (SRS) with spectral focusing, to study co-crystallization via liquid-assisted ball milling. Indomethacin and nicotinamide served as the model API and co-former, and the results were compared with established analytical methods. Narrowband CARS, with univariate peak position analysis, was useful to visualize co-crystal formation, but suffered some degree of signal mixing that affected component identification. Hyperspectral SRS imaging, combined with classical least squares multivariate analysis, separated the different components with high confidence and proved to be a robust and rapid tool to qualitatively and quantitatively image co-crystallization. The coherent Raman imaging results explained divergent co-crystallization endpoints obtained with the conventional solid-state analysis methods. CARS and SRS microscopies also revealed the presence of otherwise undetected trace forms. Finally, we also demonstrated the dramatic reversal of partial co-crystal formation during milling, depending on ethanol content. Overall, the study demonstrates the added value coherent Raman microscopy can provide for analysis of co-crystallization processes.

Challenges in Raman Spectroscopy of (micro)Plastics: The interfering role of colourants

Rising plastic consumption leads to widespread microplastic (MP) contamination. Raman spectroscopy is widely used for MP identification due to its ability to analyse particles as small as 1 μm. However, it faces challenges such as interference from pigments and additives. In this study, we aim to assess the accuracy of Raman micro-spectroscopy in identifying coloured plastic samples by applying various oxidative treatments to eliminate the possible interference effect caused by colourants associated with the sample. Standard and coloured microplastics were analysed using a Raman imaging microscope. Coloured plastics were treated with H2O2 30%, Sodium hypochlorite 5%, and Fenton reagent ( H2O2 30% and Ferrous sulphate 0.2 M) for 24, 48, and 72 hours. The Raman spectra were acquired after treatment to assess the impact of the treatment procedure on the polymer identification. Our results revealed that colourants significantly impact Raman spectra by peak broadening and/or fluorescence effects, which reduces identification accuracy and match scores Red pigments particularly obscure polymer identification. Treatments like oxidation and Fenton's reagent showed limited effectiveness. Additives in plastic samples can affect the accuracy of polymer identification by the Raman spectroscopy technique. Common treatment procedures do not improve the accuracy of identification. In order to improve the reliability of Raman analysis, essential factors such as utilizing multiple excitation lasers and appropriate CCD detectors, establishing a comprehensive reference library of colourants and additives, and employing advanced techniques like time-gated Raman spectroscopy or Surface-Enhanced Raman Spectroscopy (SERS) should be considered.

Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders

Raman spectroscopy is widely used across scientific domains to characterize the chemical composition of samples in a nondestructive, label-free manner. Many applications entail the unmixing of signals from mixtures of molecular species to identify the individual components present and their proportions, yet conventional methods for chemometrics often struggle with complex mixture scenarios encountered in practice. Here, we develop hyperspectral unmixing algorithms based on autoencoder neural networks, and we systematically validate them using both synthetic and experimental benchmark datasets created in-house. Our results demonstrate that unmixing autoencoders provide improved accuracy, robustness, and efficiency compared to standard unmixing methods. We also showcase the applicability of autoencoders to complex biological settings by showing improved biochemical characterization of volumetric Raman imaging data from a monocytic cell.

Heterogeneity of Endothelial Cells Impacts the Functionality of Human Pancreatic In Vitro Models.

Endothelial cells (ECs) play a crucial role in maintaining tissue homeostasis and functionality. Depending on their tissue of origin, ECs can be highly heterogeneous regarding their morphology, gene and protein expression, functionality, and signaling pathways. Understanding the interaction between organ-specific ECs and their surrounding tissue is therefore critical when investigating tissue homeostasis, disease development, and progression. In vitro models often lack organ-specific ECs, potentially limiting the translatability and validity of the obtained results. The goal of this study was to assess the differences between commonly used EC sources in tissue engineering applications, including human umbilical vein ECs (HUVECs), human dermal microvascular ECs (hdmvECs), and human foreskin microvascular ECs (hfmvECs), and organ-specific human pancreatic microvascular ECs (hpmvECs), and test their impact on functionality within an in vitro pancreas test system used for diabetes research. Utilizing high-resolution Raman microspectroscopy and Raman imaging in combination with established protein and gene expression analyses and exposure to defined physical signals within microfluidic cultures, we identified that ECs exhibit significant differences in their biochemical composition, relevant protein expression, angiogenic potential, and response to the application of mechanical shear stress. Proof-of-concept results showed that the coculture of isolated human islets of Langerhans with hpmvECs significantly increased the functionality when compared with control islets and islets cocultured with HUVECs. Our study demonstrates that the choice of EC type significantly impacts the experimental results, which needs to be considered when implementing ECs into in vitro models.

Enhanced stimulated Raman and fluorescence imaging by single-frame trained BDN

Hyperspectral and multispectral imaging capture an expanded dimension of information that facilitates discoveries. However, image features are frequently obscured by noise generated from the limited photodamage threshold of the specimen. Although machine learning approaches demonstrate considerable promise in addressing this challenge, they typically require extensive datasets, which can be difficult to obtain. Here, we introduce BiFormer denoising network (BDN), designed to effectively and efficiently extract image features by utilizing both local and global level connections, sparse architectures, and fine-tuning. Experimental results indicate that BDN enhances the quality of stimulated Raman scattering (SRS) images by up to 16-fold in signal-to-noise ratio (SNR), particularly improving subtle features at higher spatial frequencies. Furthermore, BDN is successfully adapted to fluorescence imaging, achieving significant improvements in SNR and order-of-magnitude reduction in exposure time, thereby showcasing its versatility across various imaging modalities. Collectively, BDN exhibits substantial potential for spectroscopic imaging applications in the fields of biomedicine and materials science.

Surface-Enhanced, Low-Frequency Raman Spectroscopy: A Sensitive Screening Tool for Structural Characterization of Pharmaceuticals.

Surface-enhanced, low-frequency Raman spectroscopy (SELFRS) was explored for its potential as a structural screening tool within pharmaceutical applications, including facile small-scale multicomponent analysis. Paracetamol was used as the model drug, and its crystallization behavior with or without the presence of a templating agent (benzoic acid) was investigated using commercial silver-based SERS substrates. The Raman imaging was carried out using two different LFR-enabled instruments employing 532 and 785 nm incident lasers, where each of the setups showed certain affinity for differentiating lattice vibrations of the polymorphic forms of interest: form I and form II. A comparison of SELFRS, SERS, and their combination using chemometrics showed the potential for the LFR spectral range to improve surface-enhanced measurements either individually or in combination with the typically-used fingerprint region without the need to alter the experimental configuration. Additionally, the use of crystallization additives that helped to drive the formation of metastable form II was shown using SELFRS to provide additional mechanistic understanding of the template-assisted crystallization processes.

Formulation and evaluation of carrier-based dry powders containing budesonide and arformoterol for inhalation therapy

ABSTRACT Asthma and Chronic Obstructive Pulmonary Disease (COPD) are major global health issues, with inhalation therapy being a primary treatment method. However, dry powder inhalers (DPIs) often face the issue of drug particle aggregation, which can reduce drug delivery efficiency. This study aims to investigate particle aggregation and optimize the cohesion–adhesion balance to improve inhalation efficiency. Advanced techniques such as atomic force microscopy and Raman imaging were employed to analyze particle interactions and aggregation behavior, focusing on lactose ratios, particle shapes, and drug-drug interactions. The therapeutic efficacy of optimized formulations containing Budesonide (BUD) and Arformoterol (AFT) was evaluated in an asthma model. Specifically, sRAW, neutrophil count, and tidal volume values were significantly improved compared to the positive control group, with p-values below 0.01. AFT demonstrated equivalent efficacy to Formoterol at half the dose. Additionally, pharmacokinetic (PK) studies showed that both drugs exhibited similar in vivo behavior, confirming the therapeutic superiority of AFT. (p-value for AUC0-t and Cmax: 0.646 and 0.153, respectively) The key findings highlight that formulation optimization significantly reduces particle aggregation and improves drug delivery efficiency in DPI (FPF for AFT and BUD: 39.4% and 50.6%, respectively), suggesting the potential for enhanced clinical outcomes in asthma and COPD patients. This study provides valuable insights for the formulation development of inhalable therapies.