Raman Hyperspectral Imaging Research Articles

Remote Detection and Identification of Plastics with Hyperspectral Raman Imaging Lidar

Remote Detection and Identification of Plastics with Hyperspectral Raman Imaging Lidar

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.

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.

Machine Learning and Hyperspectral Imaging for Analysis of Human Papillomaviruses (HPV) Vaccine Self-Healing Particles.

Human papillomaviruses (HPV) are known to cause a variety of diseases, including cervical cancer and genital warts. HPV is a highly prevalent virus and is considered the most common sexually transmitted disease. Because of the risks associated with HPV, Gardasil, a quadrivalent recombinant vaccine, was developed by Merck & Co., Inc., Rahway, NJ, USA, and approved by the Food and Drug Administration (FDA) in 2006. The second generation of the vaccine, Gardasil9, was subsequently approved by the FDA in 2014, providing significant protection against HPV. The HPV vaccine may be given as 2 or 3 doses; however, vaccine administration as a single dose with a sustained release mechanism may potentially offer benefits to meet emerging health needs. To explore this, HPV vaccines were formulated within microporous self-healing particles (SHPs) to enable potential controlled release of HPV virus-like particle (VLP) antigen. Machine learning, in the form of multivariate curve resolution-alternating least-squares (MCR-ALS), with Raman hyperspectral imaging was used to determine the molecular identity and spatial distribution of all relevant species within this HPV vaccine formulation. The results indicate that machine learning with Raman hyperspectral imaging was able to spatially resolve HPV VLP antigens within SHP vaccines for the first time, providing crucial information necessary for vaccine development.

A novel low-quality Raman hyperspectral image reconstruction method for corn component mapping

Raman imaging used to obtain spatial distribution information of chemical components on the sample surface has been widely applied in the nutritional assessment and quality control of cereal grains. However, obtaining high-quality Raman hyperspectral images of cereal grain based on point scanning is time-consuming due to the limitations of the instrumental imaging principle and Raman hyperspectral images acquired in a short time are low-quality with noise interference, limiting its practical application in the visualization of cereal grain composition. To rapidly and effectively achieve high-quality component distribution mappings of cereal grains based on Raman hyperspectral images, we propose a novel Swin Transformer-based method combining spatial-spectral denoising and image reconstruction (SwinSSID-IR) for low-quality Raman hyperspectral image reconstruction. The novel denoising network named SwinSSID innovatively introduces Swin Transformer blocks (STB) to effectively eliminate noise from raw low-quality Raman hyperspectral images of corn kernels. Next, the super-resolution network named SwinIR rapidly generates high-quality component distribution mappings of corn with exceptional clarity and detail from the denoised low-quality Raman hyperspectral image at characteristic peak. Experimental results show the competitiveness of the proposed method compared with other similar advanced methods and its effective application in non-destructive visual analysis of corn components.

Machine Learning for Deconvolution and Segmentation of Hyperspectral Imaging Data from Biopharmaceutical Resins.

Biopharmaceutical resins are pivotal inert matrices used across industry and academia, playing crucial roles in a myriad of applications. For biopharmaceutical process research and development applications, a deep understanding of the physical and chemical properties of the resin itself is frequently required, including for drug purification, drug delivery, and immobilized biocatalysis. Nevertheless, the prevailing methodologies currently employed for elucidating these important aspects of biopharmaceutical resins are often lacking, frequently require significant sample alteration, are destructive or ionizing in nature, and may not adequately provide representative information. In this work, we propose the use of unsupervised machine learning technologies, in the form of both non-negative matrix factorization (NMF) and k-means segmentation, in conjugation with Raman hyperspectral imaging to rapidly elucidate the molecular and spatial properties of biopharmaceutical resins. Leveraging our proposed technology, we offer a new approach to comprehensively understanding important resin-based systems for application across biopharmaceuticals and beyond. Specifically, focusing herein on a representative resin widely utilized across the industry (i.e., Immobead 150P), our findings showcase the ability of our machine learning-based technology to molecularly identify and spatially resolve all chemical species present. Further, we offer a comprehensive evaluation of optimal excitation for hyperspectral imaging data collection, demonstrating results across 532, 638, and 785 nm excitation. In all cases, our proposed technology deconvoluted, both spatially and spectrally, resin and glass substrates via NMF. After NMF deconvolution, image segmentation was also successfully accomplished in all data sets via k-means clustering. To the best of our knowledge, this is the first report utilizing the combination of two unsupervised machine learning methodologies, combining NMF and k-means, for the rapid deconvolution and segmentation of biopharmaceutical resins. As such, we offer a powerful new data-rich experimentation tool for application across multidisciplinary fields for a deeper understanding of resins.

Oxidative weathering of pyrite in acidic environments: Data-driven experimental evaluation coupled with Raman hyperspectral imaging

This study deals with the identification of the factors that affect pyrite oxidation in acid mine drainage conditions. For this scope, weathering experiments have been carried at laboratory scale based on the design of experiments methodology to evaluate the effect of factors such as major ion concentrations, crystal size, and humic acids presence over the amount of elemental sulfur produced due to the involved weathering reactions. In particular, metal and anionic concentrations in solution were quantified by inductively coupled plasma-atomic emission spectroscopy and ion-chromatography techniques, respectively, whereas the amount of elemental sulfur was quantified with a high-performance liquid chromatography with diode-array detection technique after proper extraction procedure. A partial least squares regression was calculated to establish a quantitative relationship between the considered factors and the amount of elemental sulfur. After evaluation of the model, ferric iron, crystal size and the presence of humic acids were identified as the relevant factors for pyrite oxidation under acidic conditions. In addition, the surface of the samples was characterized by Raman imaging spectroscopy and subsequently analyzed by explorative hyperspectral analysis methods to assess the spatial distribution of the elemental sulfur as the main weathering product, resulting in a homogenous distribution.

Automated and parallel transfer of arrays of oriented graphene ribbons

The transfer of two-dimensional materials from their growth substrate onto application wafers is a critical bottleneck in scaling-up devices based on such nanomaterials. Here, we present an innovative approach to achieve the automated and simultaneous transfer of arrays of graphene ribbons, with precise control over their orientation and alignment onto patterned wafers. The transfer is performed in a simple, yet efficient apparatus consisting of an array of glass columns, strategically shaped to control ribbon orientation and arranged to match the destination wafer, coupled to a dual inflow/outflow pumping system. This apparatus enables the transfer of a custom array of parallel graphene ribbons in a fraction of the time required with traditional methods. The quality of the transferred graphene was evaluated using optical imaging, scanning electron microscopy, hyperspectral Raman imaging, and electrical transport: all consistently indicating that the transferred graphene exhibits excellent quality, comparable to a manual transfer by an expert user. The proposed apparatus offers several competitive advantages, including ease of use, high transfer throughput, and reduced nanomaterial consumption. Moreover, it can be used repeatedly on the same wafer to assemble arrays of overlayed materials with controlled relative orientations. This approach thus opens promising opportunities for the large-scale fabrication of various heterostructures and devices based on vertical assemblies of 2D nanomaterials.

Raman Hyperspectral Imaging as a Potential Tool for Rapid and Nondestructive Identification of Aflatoxin Contamination in Corn Kernels

The potential of Raman hyperspectral imaging with a 785 nm excitation line laser was examined for the detection of aflatoxin contamination in corn kernels. Nine-hundred kernels were artificially inoculated in the laboratory, with 300 kernels each inoculated with AF13 (aflatoxigenic) fungus, AF36 (nonaflatoxigenic) fungus, and sterile distilled water (control). One-hundred kernels from each treatment were subsequently incubated for 3, 5, and 8 days. The mean spectra of single kernels were extracted from the endosperm side and the embryo area of the germ side, and local Raman peaks were identified based upon the calculated reference spectra of aflatoxin-negative and -positive categories separately. The principal component analysis-linear discriminant analysis models were established using different types of variable inputs including original full spectra, preprocessed full spectra, and identified local peaks over kernel endosperm-side, germ-side, and both sides. The results of the established discriminant models showed that the germ-side spectra performed better than the endosperm-side spectra. Based upon the 20 ppb-threshold, the best mean prediction accuracy of 82.6% was achieved for the aflatoxin-negative category using the original spectra in the combined form of both kernel sides, and the best mean prediction accuracy of 86.7% was obtained for the -positive category using the preprocessed germ-side spectra. Based upon the 100 ppb-threshold, the best mean prediction accuracies of 85.0% and 89.6% were achieved for the aflatoxin-negative and -positive categories separately, using the same type of variable inputs for the 20 ppb-threshold. In terms of overall prediction accuracy, the models established upon the original spectra in the combined form of both kernel sides achieved the best predictive performance, regardless of the threshold. The mean overall prediction accuracies of 81.8% and 84.5% were achieved with the 20 ppb- and 100 ppb-thresholds, respectively.

A comparison of multivariate curve resolution with endmember extraction methods in hyperspectral Raman imaging

Hyperspectral Raman imaging not only offers spectroscopic fingerprints but also reveals morphological information such as spatial distributions in an analytical sample. However, the spectrum-per-pixel nature of hyperspectral imaging (HSI) results in a vast amount of data. Furthermore, HSI often requires pre- and post-processing steps to extract valuable chemical information. To derive pure spectral signatures and concentration abundance maps of the active spectroscopic compounds, both endmember extraction (EX) and Multivariate Curve Resolution (MCR) techniques are widely employed. The objective of this study is to carry out a systematic investigation based on Raman mapping datasets to highlight the similarities and differences between these two approaches in retrieving pure variables, and ultimately provide guidelines for pure variable extraction. Numerical simulations and Raman mapping experiments on a mixture of pharmaceutical powders and on a layered plastic foil sample were conducted to underscore the distinctions between MCR and EX algorithms (in particular Vertex Component Analysis, VCA) and their outputs. Both methods were found to perform well if the dataset contains pure pixels for each of the individual components. However, in cases where such pure pixels do not exist, only MCR was found to be capable of extracting the pure component spectra.

Macroscopic inelastic scattering imaging using a hyperspectral line-scanning system identifies invasive breast cancer in lumpectomy and mastectomy specimens.

Of patients with early-stage breast cancer, 60% to 75% undergo breast-conserving surgery. Of those, 20% or more need a second surgery because of an incomplete tumor resection only discovered days after surgery. An intraoperative imaging technology allowing cancer detection on the margins of breast specimens could reduce re-excision procedure rates and improve patient survival. We aimed to develop an experimental protocol using hyperspectral line-scanning Raman spectroscopy to image fresh breast specimens from cancer patients. Our objective was to determine whether macroscopic specimen images could be produced to distinguish invasive breast cancer from normal tissue structures. A hyperspectral inelastic scattering imaging instrument was used to interrogate eight specimens from six patients undergoing breast cancer surgery. Machine learning models trained with a different system to distinguish cancer from normal breast structures were used to produce tissue maps with a field-of-view of classifying each pixel as either cancer, adipose, or other normal tissues. The predictive model results were compared with spatially correlated histology maps of the specimens. A total of eight specimens from six patients were imaged. Four of the hyperspectral images were associated with specimens containing cancer cells that were correctly identified by the new ex vivo pathology technique. The images associated with the remaining four specimens had no histologically detectable cancer cells, and this was also correctly predicted by the instrument. We showed the potential of hyperspectral Raman imaging as an intraoperative breast cancer margin assessment technique that could help surgeons improve cosmesis and reduce the number of repeat procedures in breast cancer surgery.

Adipose-derived mesenchymal stem cells' adipogenesis chemistry analyzed by FTIR and Raman metrics

The full understanding of molecular mechanisms of cell differentiation requires a holistic view. Here we combine label-free FTIR and Raman hyperspectral imaging with data mining to detect the molecular cell composition enabling noninvasive monitoring of cell differentiation and identifying biochemical heterogeneity. Mouse adipose-derived mesenchymal stem cells (AD-MSCs) undergoing adipogenesis were followed by Raman and FT-IR imaging, Oil Red, and immunofluorescence. A workflow of the data analysis (IRRSmetrics4stem) was designed to identify spectral predictors of adipogenesis and test machine-learning (ML) methods (hierarchical clustering, PCA, PLSR) for the control of the AD-MSCs differentiation degree. IRRSmetrics4stem provided insights into the chemism of adipogenesis. With single-cell tracking, we established IRRS metrics for lipids, proteins, and DNA variations during AD-MSCs differentiation. The over 90% predictive efficiency of the selected ML methods proved the high sensitivity of the IRRS metrics. Importantly, the IRRS metrics unequivocally recognize a switch from proliferation to differentiation. This study introduced a new bioassay identifying molecular markers indicating molecular transformations and delivering rapid and machine learning-based monitoring of adipogenesis that can be relevant to other differentiation processes. Thus, we introduce a novel, rapid, machine learning-based bioassay to identify molecular markers of adipogenesis. It can be relevant to identification of differentiation-related molecular processes in other cell types, and beyond the cell differentiation including progression of different cellular pathophysiologies reconstituted in vitro.

Single-Shot Standoff Hyperspectral Raman Imaging of a Chemical Warfare Agent Simulant.

We demonstrate single-shot standoff hyperspectral Raman imaging of liquid diisopropyl methylphosphonate at a standoff distance of 1 m using two different techniques: multi-bandpass filter imaging (MBFI) and fiber-bundle imaging spectroscopy (FBIS). We find that MBFI has good spatial resolution, but poor spectral resolution, due to the limitations of commercially available bandpass filters. On the other hand, we find FBIS to have excellent spectral resolution, but limited spatial resolution due to the relatively small number of fibers in a bundle. For FBIS, we also determine, for a 1 m standoff distance, a minimum pump fluence of 10 mJ/cm to obtain good single-shot spectra.

Collagen organization and structure in FBLN5-/- mice using label-free microscopy: implications for pelvic organ prolapse.

Pelvic organ prolapse (POP) is a gynecological disorder described by the descent of superior pelvic organs into or out of the vagina as a consequence of disrupted muscles and tissue. A thorough understanding of the etiology of POP is limited by the availability of clinically relevant samples, restricting longitudinal POP studies on soft-tissue biomechanics and structure to POP-induced models such as fibulin-5 knockout (FBLN5-/- ) mice. Despite being a principal constituent in the extracellular matrix, little is known about structural perturbations to collagen networks in the FBLN5-/- mouse cervix. We identify significantly different collagen network populations in normal and prolapsed cervical cross-sections using two label-free, nonlinear microscopy techniques. Collagen in the prolapsed mouse cervix tends to be more isotropic, and displays reduced alignment persistence via 2-D Fourier transform analysis of images acquired using second harmonic generation microscopy. Furthermore, coherent Raman hyperspectral imaging revealed elevated disorder in the secondary structure of collagen in prolapsed tissues. Our results underscore the need for in situ multimodal monitoring of collagen organization to improve POP predictive capabilities.

Raman-based mapping and depth-profiling of the relaxation state in amorphous silicon

We show that the micro-scale variations in the relaxation state of amorphous silicon (a-Si) can be well-identified by Raman mapping over hundreds or thousands of μm2 in 1–2 h. Pure and relaxed a-Si is obtained by self-implantation in crystalline silicon (c-Si) followed by anneal at 500 °C. It is then locally re-implanted over micro-sized patterns to produce unrelaxed a-Si zones. Raman mappings are obtained by pointwise confocal μ-Raman and hyperspectral Raman imaging. We also measure the depth profiles of the relaxation state in re-implanted a-Si by scanning the edge of a re-implanted sample. We infer from the depth profiles that the minimal damage dose to fully de-relax a-Si is 0.04 displacements per atoms, which is an order of magnitude smaller than the fluence needed to fully amorphize c-Si.

Label-Free, Noninvasive Bone Cell Classification by Hyperspectral Confocal Raman Microscopy.

Characterizing and identifying cells in multicellular in vitro models remain a substantial challenge. Here, we utilize hyperspectral confocal Raman microscopy and principal component analysis coupled with linear discriminant analysis to form a label-free, noninvasive approach for classifying bone cells and osteosarcoma cells. Through the development of a library of hyperspectral Raman images of the K7M2-wt osteosarcoma cell lines, 7F2 osteoblast cell lines, RAW 264.7 macrophage cell line, and osteoclasts induced from RAW 264.7 macrophages, we built a linear discriminant model capable of correctly identifying each of these cell types. The model was cross-validated using a k-fold cross validation scheme. The results show a minimum of 72% accuracy in predicting cell type. We also utilize the model to reconstruct the spectra of K7M2 and 7F2 to determine whether osteosarcoma cancer cells and normal osteoblasts have any prominent differences that can be captured by Raman. We find that the main differences between these two cell types are the prominence of the β-sheet protein secondary structure in K7M2 versus the α-helix protein secondary structure in 7F2. Additionally, differences in the CH2 deformation Raman feature highlight that the membrane lipid structure is different between these cells, which may affect the overall signaling and functional contrasts. Overall, we show that hyperspectral confocal Raman microscopy can serve as an effective tool for label-free, nondestructive cellular classification and that the spectral reconstructions can be used to gain deeper insight into the differences that drive different functional outcomes of different cells.

Assessment of unmixing approaches for the quantitation of SERS nanoparticles in highly multiplexed spectral images

AbstractSurface‐enhanced Raman scattering nanoparticles (SERS NPs) offer powerful optical contrast features for imaging assays. Their gold core enhances the inelastic scattering cross section, allowing highly sensitive and rapid detection, and their characteristic sets of narrow spectral bands give them unsurpassed multiplexing capabilities. Multiplexed hyperspectral images are commonly unmixed using a compensation matrix of reference spectra to produce quantitative image channels illustrating the distribution of each material. It is these unmixed channels that are fit for interpretation from assays utilizing SERS NP contrast agents. Some factors that may impact SERS NP quantitative and dynamic range capabilities may include endogenous background heterogeneity, the ability of unmixing algorithms to account for signal variances, and linear system conditioning imposed by contrast agent signals. We report on hyperspectral Raman imaging of mixtures of SERS NPs from an expanded library of contrast agents. We study increasing plexity and varying degrees of system conditioning as inputs to a diverse set of classical, non‐negatively constrained, and regularized regression algorithms to investigate which signal features and unmixing methods deliver the most promising quantitation performance with the least error. Raman imaging of SERS NP mixtures is performed on controlled substrates and representative biological specimens, and experimental results are compared against ground truth data. We evaluate spectral fitting fidelity, quantitation, and specificity correlations with system conditioning. Spectral unmixing with a regularized hybrid of least squares regression with principal component analysis (HLP) algorithm approximated spectra with 3.5× better fitting fidelity and 3× better quantitation robustness with tissue background compared with simpler unmixing routines.

Compressive Raman imaging by combining scattering-projection interleaving with context-aware excitation.

Exciting an object with a laser-focus array and randomly interleaving its scattering projection has been proved to be an effective strategy for speeding up Raman imaging. The so-called scattering interleaved Raman imaging (SIRI) method allows Raman hyperspectral imaging with a single snapshot and exhibits excellent reconstruction fidelity and signal-to-noise ratios (SNRs). Here, we show that the performance of SIRI is significantly improved when combined with context-aware excitation. The experiments on micro-plastics demonstrate that the restriction of Raman excitation within a smaller region of interest as guided by bright-field microscopy improves the signal intensity and the SNR, and it is surprising that the spectral resolution is also significantly improved. The context-aware SIRI method is successfully used for imaging of lipid-producing yeast cells, suggesting that it is a promising analytical tool for studying live cells or tissues.

In situ monitoring of the shikimate pathway: a combinatorial approach of Raman reverse stable isotope probing and hyperspectral imaging.

Sensing and visualization of metabolites and metabolic pathways in situ are significant requirements for tracking their spatiotemporal dynamics in a non-destructive manner. The shikimate pathway is an important cellular mechanism that leads to the de novo synthesis of many compounds containing aromatic rings of high importance such as phenylalanine, tyrosine, and tryptophan. In this work, we present a cost-effective and extraction-free method based on the principles of stable isotope-coupled Raman spectroscopy and hyperspectral Raman imaging to monitor and visualize the activity of the shikimate pathway. We also demonstrated the applicability of this approach for nascent aromatic amino acid localization and tracking turnover dynamics in both prokaryotic and eukaryotic model systems. This method can emerge as a promising tool for both qualitative and semi-quantitative in situ metabolomics, contributing to a better understanding of aromatic ring-containing metabolite dynamics across various organisms.