Subsurface probing in diffusely scattering media using spatially offset Raman spectroscopy

Surface-Enhanced Raman Spectroscopy for Biomedical Applications: Recent Advances and Future Challenges

The year 2024 marks the 50th anniversary of the discovery of surface-enhanced Raman spectroscopy (SERS). Over recent years, SERS has experienced rapid development and became a critical tool in biomedicine with its unparalleled sensitivity and molecular specificity. This review summarizes the advancements and challenges in SERS substrates, nanotags, instrumentation, and spectral analysis for biomedical applications. We highlight the key developments in colloidal and solid SERS substrates, with an emphasis on surface chemistry, hotspot design, and 3D hydrogel plasmonic architectures. Additionally, we introduce recent innovations in SERS nanotags, including those with interior gaps, orthogonal Raman reporters, and near-infrared-II-responsive properties, along with biomimetic coatings. Emerging technologies such as optical tweezers, plasmonic nanopores, and wearable sensors have expanded SERS capabilities for single-cell and single-molecule analysis. Advances in spectral analysis, including signal digitalization, denoising, and deep learning algorithms, have improved the quantification of complex biological data. Finally, this review discusses SERS biomedical applications in nucleic acid detection, protein characterization, metabolite analysis, single-cell monitoring, and in vivo deep Raman spectroscopy, emphasizing its potential for liquid biopsy, metabolic phenotyping, and extracellular vesicle diagnostics. The review concludes with a perspective on clinical translation of SERS, addressing commercialization potentials and the challenges in deep tissue in vivo sensing and imaging.

Shifted-excitation Raman difference spectroscopy and charge-shifting detection coupled with spatially offset Raman spectroscopy for heritage science

In situ measurements have great importance since in many scientific fields certain samples cannot be moved because of diverse reasons (excessive dimensions or weight, security, logistics etc.). In heritage science, this is a crucial requirement due to the high value of art objects, requiring non-invasive and in situ analyses. Therefore, it is important to have analytical methods capable of providing relevant information also outside laboratory environments. Such measurements face multiple challenges: for example, interference from ambient light or formation of artefacts due to undesired motions of the instruments. In Raman spectroscopy, a number of solutions have been demonstrated to mitigate these effects. For instance, Shifted Excitation Raman Difference Spectroscopy (SERDS) has proven efficient in removing the fluorescence of the sample and ambient light interference, and a charge-shifting detection approach was shown to be valuable in dealing with varying ambient light. In this study, we provide a comparison of conventional Raman spectroscopy, Shifted Excitation Raman Difference Spectroscopy (SERDS), charge-shifting detection technology and a combined SERDS and charge-shifting approach, in order to evaluate their effectiveness in mitigating fast evolving interfering backgrounds (e.g., varying ambient light). Further investigations were also carried out into the potential of coupling of these methods with Spatially Offset Raman Spectroscopy (SORS) to facilitate more effective non-invasive investigations of subsurface sample components (e.g. paint layers). The study was carried out using samples mimicking cultural heritage materials with different degrees of complexity and in the presence of fluorescence and ambient light interference. The results are, nevertheless, applicable more generally to other areas such as forensics or biomedical fields, where both dynamic and static interferences can hinder measurements.

Design and Validation of a Multimodal Diffuse Reflectance and Spatially Offset Raman Spectroscopy System for In Vivo Applications

We report on the development of a multimodal spectroscopy system, combining diffuse reflectance spectroscopy (DRS) and spatially offset Raman spectroscopy (SORS). A fiber optic probe was designed with spatially offset source-detector fibers to collect subsurface measurements for each modality, as well as ball lens-coupled fibers for superficial measurements. The system acquires DRS, zero-offset Raman spectroscopy (RS) and SORS with good signal-to-noise ratio. Measurements on chicken breast tissue demonstrate that both DRS and RS can acquire spectra from similar depths within tissue. Measurements acquired from the skin of a human volunteer demonstrate distinct Raman peaks at 937 and 1755 cm-1 that were unique to the zero-offset ball lens configuration and 718 and 1089 cm-1 for the spatially offset setting. We also identified Raman peaks corresponding to melanin that were prominent in the superficial measurements obtained with the ball lens-coupled fibers but not in the spatially offset fibers.

Nanomaterials and clinical SERS technology: broad applications in disease diagnosis

The critical need for rapid cancer diagnosis and related illnesses is growing alongside the current healthcare challenges, unfavorable prognosis, and constraints in diagnostic timing. As a result, emphasis on surface-enhanced Raman spectroscopy (SERS) diagnostic methods, including both label-free and labelled approaches, holds significant promise in fields such as analytical chemistry, biomedical science, and physics, due to the user-friendly nature of SERS. Over time, the SERS detection sensitivity and specificity with nanostructured materials for SERS applications (NMs-SERS) in different media have been remarkable. An investigation into electronic dynamics and interactions has revealed a seemingly fair result regarding the complementary effects of electromagnetic (EM) and chemical enhancements (CM), underscoring the operational principles of SERS. Nevertheless, the focus on translational SERS applications, especially beyond preliminary proof-of-concept research, remains limited. This review focuses on the advancements made in clinical SERS diagnostics and the essential role of NMs-SERS, ranging from plasmonic to non-plasmonic materials and other related advancements. Furthermore, it outlines the significant achievements of biomedical SERS in tumor diagnosis, particularly in identifying circulating tumor cells (CTCs), alongside a clear focus on NMs-SERS characteristics such as surface charge, shape, size, detection sensitivity, specificity, signal reproducibility, and recyclability. Finally, it underscores the use of microfluidic chips within the labelled SERS strategy for isolating CTCs, the concept of Ramanomics, and the integration of artificial intelligence (AI) to strengthen SERS data analysis. We hope that this review will help guide and expedite the potential for precise SERS diagnosis of key chronic diseases.

Surface-enhanced Raman spectroscopy: a half-century historical perspective

Surface-enhanced Raman spectroscopy (SERS) has evolved significantly over fifty years into a powerful analytical technique. This review aims to achieve five main goals. (1) Providing a comprehensive history of SERS's discovery, its experimental and theoretical foundations, its connections to advances in nanoscience and plasmonics, and highlighting collective contributions of key pioneers. (2) Classifying four pivotal phases from the view of innovative methodologies in the fifty-year progression: initial development (mid-1970s to mid-1980s), downturn (mid-1980s to mid-1990s), nano-driven transformation (mid-1990s to mid-2010s), and recent boom (mid-2010s onwards). (3) Illuminating the entire journey and framework of SERS and its family members such as tip-enhanced Raman spectroscopy (TERS) and shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS) and highlighting the trajectory. (4) Emphasizing the importance of innovative methods to overcome developmental bottlenecks, thereby expanding the material, morphology, and molecule generalities to leverage SERS as a versatile technique for broad applications. (5) Extracting the invaluable spirit of groundbreaking discovery and perseverant innovations from the pioneers and trailblazers. These key inspirations include proactively embracing and leveraging emerging scientific technologies, fostering interdisciplinary cooperation to transform the impossible into reality, and persistently searching to break bottlenecks even during low-tide periods, as luck is what happens when preparation meets opportunity.

Subcutaneous depth-selective spectral imaging with mμSORS enables noninvasive glucose monitoring

Noninvasive blood glucose monitoring offers substantial advantages for patients, but current technologies are often not sufficiently accurate for clinical applications or require personalized calibration. Here we report multiple μ-spatially offset Raman spectroscopy, which captures Raman signals at varying skin depths, and show that it accurately detects blood glucose levels in humans. In 35 individuals with or without type 2 diabetes, we first determine the optimal depth for glucose detection to be at or below the capillary-rich dermal-epidermal junction, where we observe a strong correlation between specific Raman bands and venous plasma glucose concentrations. In a second study, comprising 230 participants, we then improve accuracy of our regression model to reach a mean absolute relative difference of 14.6%, without personalized calibration, whereby 99.4% of calculated glucose values fall into clinically acceptable zones of the consensus error grid (zones A and B). These findings highlight the ability and robustness of multiple μ-spatially offset Raman spectroscopy for noninvasive blood glucose measurement in a clinical setting.

Spatially offset Raman spectroscopy (SORS) for sustainable olive oil authentication - Tackling the challenges in on-site food control

While olive oil production is facing many challenges and prices are rising dramatically, the demand for non-invasive analytical methods is high. Spatially offset Raman spectroscopy (SORS) can be a potential method for sustainable food analysis as it can penetrate different types of containers while providing a good spectrum of the food in question. In this study, we have developed a SORS-based method for the authentication of olive oils that is also suitable for the high variability of packaging found in practice. Based on a dataset of verified oils from four sample groups and mixtures of this samples, we developed an analytical strategy using plotting, principal component analysis, and a classification and regression model. This analysis strategy was tested first using different mixtures of olive and sunflower oils. We were able to recognize 80 % of adulterated olive oil samples with 30 % added sunflower oil and 60 % of the samples adulterated with 10 % sunflower oil. Afterwards the strategy was tested in food inspections of different companies. The results show that our strategy for on-site analysis was successful and was positively evaluated by the parties involved. In an additional validation step, we analysed 30 samples from online retail where we were able to distinguish between actual adulterated and genuine olive oil samples.

Active Surface-Enhanced Raman Spectroscopy (SERS): A Novel Concept for Enhancing Signal Contrast in Complex Matrices Using External Perturbation

Noninvasive detection of surface-enhanced Raman spectroscopy (SERS) signals from deep within tissue represents a common challenge in many biological and clinical applications including disease diagnosis and therapy monitoring. Such signals are typically weak and not readily discernible from often much larger Raman and fluorescence background signals (e.g., from surrounding tissue). Consequently, suboptimal sensitivity in the detection of SERS signals is often achieved in these situations. Similar issues can arise in SERS measurements in other diffusely scattering samples and complex matrices. Here, we propose a novel concept, active SERS, for the efficient retrieval of SERS signals from deep within complex matrices such as biological tissues that mitigates these issues. It relies on applying an external perturbation to the sample to alter the SERS signal from nanoparticles (NPs) deep inside the matrix. A measurement with and without, or before and after, such perturbation then can provide powerful contrasting data enabling an effective elimination of the matrix signals to reveal more clearly the desired SERS signal without the interfering background and associated artifacts. The concept is demonstrated using ultrasound (US) as an external source of perturbation and SERS NPs inserted deep within a heterogeneous tissue phantom mimicking a cluster of NPs accumulated within a small target lesion. The overall SERS signal intensity induced by the applied US perturbation decreased by ∼21% and the SERS signal contrast was considerably improved by eliminating subtraction artifacts present in a conventional measurement performed at a neighboring spatial location in a heterogeneous tissue sample. Although the technique was demonstrated with SERS gold NPs with a standard Raman label, it is envisaged that active SERS NPs (both the nanoscale metal geometry and Raman label) could be specifically designed to deliver an augmented response to the external stimulus to further enhance the achievable SERS signal contrast and yield even greater improvement in detection sensitivity. The method was demonstrated using transmission Raman spectroscopy; however, it is also applicable to other Raman implementations including spatially offset Raman spectroscopy and conventional Raman spectroscopy performed both at depth and at surfaces of complex matrices.

Sensing the Future-Frontiers in Biosensors: Exploring Classifications, Principles, and Recent Advances

Biosensors are transforming healthcare by delivering swift, precise, and economical diagnostic solutions. These analytical instruments combine biological indicators with physical transducers to identify and quantify biomarkers, thereby improving illness detection, management, and patient surveillance. Biosensors are widely utilized in healthcare for the diagnosis of chronic and infectious diseases, tailored treatment, and real-time health monitoring. This thorough overview examines several categories of biosensors and their uses in the detection of numerous biomarkers, including glucose, proteins, nucleic acids, and infections. Biosensors are commonly classified based on the type of transducer employed or the specific biorecognition element utilized. This review introduces a novel classification based on substrate morphology, offering a comprehensive perspective on biosensor categorization. Considerable emphasis is placed on the advancement of point-of-care biosensors, facilitating decentralized diagnostics and alleviating the strain on centralized healthcare systems. Recent advancements in nanotechnology have significantly improved the sensitivity, selectivity, and downsizing of biosensors, rendering them more efficient and accessible. The study examines problems such as stability, reproducibility, and regulatory approval that must be addressed to enable the widespread implementation of biosensors in clinical environments. The study examines the amalgamation of biosensors with wearable devices and smartphones, emphasizing the prospects for ongoing health surveillance and individualized medical care. This viewpoint clarifies the distinct types of biosensors and their particular roles, together with recent developments in the "smart biosensor" sector, facilitated by artificial intelligence and the Internet of Medical Things (IoMT). This novel approach seeks to deliver a comprehensive evaluation of the present condition of biosensor technology in healthcare, recent developments, and prospective paths, emphasizing their significance in influencing the future of medical diagnostics and patient care.

A Quantitative Chemometric Study of Pharmaceutical Tablet Formulations Using Multi-Spectroscopic Fibre Optic Probes

Background/Objectives: Two fibre optic probes were custom designed to perform Raman and near-infrared spectroscopic measurements. Our long-term objective is to develop a non-destructive tool able to collect data in hard-to-access locations for real-time analysis or diagnostic purposes. This study evaluated the quantitative performances of Probe A and Probe B using model pharmaceutical tablets. Methods: Measurements were performed using pharmaceutical tablets containing hydroxyl propylcellulose, titanium dioxide (anatase), lactose monohydrate, and indomethacin (γ form). Material content and thickness of bilayer samples (samples consisting of a top layer and a bottom layer of differing materials) were also assessed using Probe A to evaluate its capabilities to collect sub-surface information. Principal component analysis and partial least squares regression models were using individual and fused data to evaluate the performances of the different probe configurations. Results: Hydroxymethyl cellulose (RP2=0.98, RMSEP = 2.27% w/w) and lactose monohydrate (RP2=0.97, RMSEP = 2.96% w/w) content were most effectively estimated by near-infrared spectroscopy data collected using Probe A. Titanium dioxide (RP2=0.99, RMSEP = 0.21% w/w) content was most effectively estimated using a combination of 785 nm Raman spectroscopy and near-infrared spectroscopy using Probe B. Indomethacin (RP2=0.97, RMSEP = 1.01% w/w) was best estimated using a low-level fused dataset collected using 0 mm, 2.5 mm, and 5.0 mm lateral offsets of 785 nm spatially offset Raman spectroscopy using Probe A. Conclusions: The different probe configurations were able to reliably collect data and demonstrated robust quantitative performances. These results highlight the advantage of using multiple techniques for analysing different structures.

Assessment of Optical Attenuation and Skin Thickness in Type 2 Diabetes Mellitus Patients Using Optical Coherence Tomography

Diabetes management often involves invasive blood glucose monitoring, which can be uncomfortable for patients. Non-invasive techniques like multiple μ-spatially offset Raman spectroscopy (mμSORS) offer a promising alternative. To provide clinical data supporting mμSORS, we conducted a clinical trial with 198 participants to evaluate mμSORS for non-invasive blood glucose measurement. Using Optical Coherence Tomography, we studied skin thickness and optical attenuation in 172 diabetic and 26 healthy subjects. Results showed thicker stratum corneum and stratum spinosum (SS) in diabetics. Epidermal thickness increased with age and body mass index (BMI), decreased with skin brightness, and varied minimally with gender. Optical attenuation in SS was lower in diabetics, decreased with increasing a*, and was minimally affected by gender and BMI but increased with age in the upper dermis. These findings support mμSORS for accurate non-invasive glucose monitoring.

Spatially offset Raman scattering line-mapping as an adaptive tool ensuring accuracy for determination of component concentrations in tablets with different particle sizes

Spatially offset Raman scattering (SORS) line-mapping was explored as a versatile tool to examine accuracy variations in compositional analyses of tablets with different particle sizes. SORS spectra collected near the laser irradiation were less representative of tablet composition due to the limited spectroscopic sampling volume, while the signal-to-noise (S/N) ratios of corresponding spectra were higher. On the other hand, SORS spectra at longer offset distances were better representative of tablet composition, while their S/N ratios were decreased considerably. Therefore, the use of only a certain portion of sliced (line-mapped) spectra balanced with the sample representation and S/N ratio could be advantageous to enhance accuracy. Moreover, a group of optimal slice spectra is expected to vary when the particle size of the tablet changes since the characteristics of internal photon propagation also would change. For the overall examination, SORS spectra of 30 Anaprox tablets (composed of 4 constituents including naproxen sodium) with 2 particle sizes (88.4 ± 11.8 µm and 118.9 ± 38.8 µm) were analyzed, and the concentrations of three components in these tablets were determined. A total of 6 cases (3 components and 2 particle sizes) were examined. When the average optimal slice spectra were employed in each case, the errors were lower compared to those using the average of all slice spectra. The demonstrated scheme was versatile to study the offset distance-dependent accuracy variations according to particle size and target component.

Material-agnostic characterization of spatially offset Raman spectroscopy in turbid media via Monte Carlo simulations

Spatially offset Raman spectroscopy (SORS) is a transformative method for probing subsurface chemical compositions in turbid media. This systematic study of Monte Carlo simulations provides closed-form characterizations of key SORS parameters, such as the distribution of spatial origins of collected Raman photons and optimal SORS geometry to selectively interrogate a subsurface region of interest. These results are unified across an extensive range of material properties by multiplying spatial dimensions by the medium's effective attenuation coefficient, which can be calculated when the absorption and reduced scattering coefficients are known from the literature or experimentation. This method of spatial nondimensionalization is validated via goodness-of-fit analysis on the aggregate models and by training a subsurface sample localization model on a heterogeneous population of materials. The findings reported here advance the understanding of SORS phenomena while providing a quantitative and widely applicable foundation for designing and interpreting SORS experiments, facilitating its application in disciplines such as biomedical, materials science, and cultural heritage fields.

Fundamentals and Applications of Raman-Based Techniques for the Design and Development of Active Biomedical Materials

Raman spectroscopy is an analytical method based on light-matter interactions that can interrogate the vibrational modes of matter and provide representative molecular fingerprints. Mediated by its label-free, non-invasive nature, and high molecular specificity, Raman-based techniques have become ubiquitous tools for in situ characterization of materials. This review comprehensively describes the theoretical and practical background of Raman spectroscopy and its advanced variants. The numerous facets of material characterization that Raman scattering can reveal, including biomolecular identification, solid-to-solid phase transitions, and spatial mapping of biomolecular species in bioactive materials, are highlighted. The review illustrates the potential of these techniques in the context of active biomedical material design and development by highlighting representative studies from the literature. These studies cover the use of Raman spectroscopy for the characterization of both natural and synthetic biomaterials, including engineered tissue constructs, biopolymer systems, ceramics, and nanoparticle formulations, among others. To increase the accessibility and adoption of these techniques, the present review also provides the reader with practical recommendations on the integration of Raman techniques into the experimental laboratory toolbox. Finally, perspectives on how recent developments in plasmon- and coherently-enhanced Raman spectroscopy can propel Raman from underutilized to critical for biomaterial development are provided.

Label-free 3D molecular imaging of living tissues using Raman spectral projection tomography

The ability to image tissues in three dimensions (3D) with label-free molecular contrast at the mesoscale would be a valuable capability in biology and biomedicine. Here, we introduce Raman spectral projection tomography (RSPT) for volumetric molecular imaging with optical sub-millimeter spatial resolution. We have developed a RSPT imaging instrument capable of providing 3D molecular contrast in transparent and semi-transparent samples. We also created a computational pipeline for multivariate reconstruction to extract label-free spatial molecular information from Raman projection data. Using these tools, we demonstrate imaging and visualization of phantoms of various complex shapes with label-free molecular contrast. Finally, we apply RSPT as a tool for imaging of molecular gradients and extracellular matrix heterogeneities in fixed and living tissue-engineered constructs and explanted native cartilage tissues. We show that there exists a favorable balance wherein employing Raman spectroscopy, with its advantages in live cell imaging and label-free molecular contrast, outweighs the reduction in imaging resolution and blurring caused by diffuse photon propagation. Thus, RSPT imaging opens new possibilities for label-free molecular monitoring of tissues.

Surface-Enhanced Raman Scattering Nanosensing and Imaging in Neuroscience

Monitoring neurochemicals and imaging the molecular content of brain tissues in vitro, ex vivo, and in vivo is essential for enhancing our understanding of neurochemistry and the causes of brain disorders. This review explores the potential applications of surface-enhanced Raman scattering (SERS) nanosensors in neurosciences, where their adoption could lead to significant progress in the field. These applications encompass detecting neurotransmitters or brain disorders biomarkers in biofluids with SERS nanosensors, and imaging normal and pathological brain tissues with SERS labeling. Specific studies highlighting in vitro, ex vivo, and in vivo analysis of brain disorders using fit-for-purpose SERS nanosensors will be detailed, with an emphasis on the ability of SERS to detect clinically pertinent levels of neurochemicals. Recent advancements in designing SERS-active nanomaterials, improving experimentation in biofluids, and increasing the usage of machine learning for interpreting SERS spectra will also be discussed. Furthermore, we will address the tagging of tissues presenting pathologies with nanoparticles for SERS imaging, a burgeoning domain of neuroscience that has been demonstrated to be effective in guiding tumor removal during brain surgery. The review also explores future research applications for SERS nanosensors in neuroscience, including monitoring neurochemistry in vivo with greater penetration using surface-enhanced spatially offset Raman scattering (SESORS), near-infrared lasers, and 2-photon techniques. The article concludes by discussing the potential of SERS for investigating the effectiveness of therapies for brain disorders and for integrating conventional neurochemistry techniques with SERS sensing.

Theoretical and Practical Considerations of Spatially Offset Raman Spectroscopy (SORS) and Micro-SORS

Spatially offset Raman spectroscopy (SORS) is typically used to non-invasively investigate stratified samples that possess features on a millimeter scale, whereas micro-SORS usually deals with micrometer-thick layered samples. However, there are many instances where these boundaries are intertwined, sometimes indicating the possibility of using both techniques as well as circumstances that present mutual exclusion to their applicability. The aim of this study is to establish an application protocol that provides better insight into their suitability for deployment in various scenarios. The differences and similarities between the two approaches are investigated highlighting their strengths and limitations considering both theoretical and practical aspects. Diverse available parameters entail prospects and restrictions of both techniques and give rise to specific instrumental effects, namely, the overlap between the collection and excitation areas, the percentage of collected area for a given spatial offset, and the accuracy in the definition of the spatial offset (spread effect). These aspects are studied and exemplified on mockup samples relevant to the field of cultural heritage. The samples are characterized by high compositional complexity comprising features ranging from micrometer to millimeter scales. The conclusions reached are also relevant to other scientific areas such as biomedical, forensic, or energy harvest.

Blue nests: The use of plastics in the nests of the crested oropendola (Psarocolius decumanus) on the Brazilian Amazon coast

Birds have been impacted by plastic pollution via entanglement, accidental ingestion, and exposure to chemical contaminants. However, plastics were also observed as a nesting material for several species. For the first time, we describe the occurrence and composition of plastics in 36 nests of the crested oropendola (Psarocolius decumanus) in three different sites on the Amazon coast. Plastics were present in 67 % of abandoned, fallen nests. At the mangrove site, all nests contained plastics, while at the grassy clearing and the fishermen's village, plastics were present at 35.3 and 90 % of the nests, respectively. Blue fibers and ropes were the main plastics observed, probably derived from discarded fishing gear. Of 79 analyzed fibers, 97.5 % were composed of Polyethylene, and 83.5 % contained Cobalt Phthalocyanine as an additive. Our results showed a widespread use of plastics by P. decumanus, which may increase the exposure of both juveniles and adults to potential contaminants.

Raman spectroscopy with an improved support vector machine for discrimination of thyroid and parathyroid tissues

The objective of this study was to discriminate thyroid and parathyroid tissues using Raman spectroscopy combined with an improved support vector machine (SVM) algorithm. In thyroid surgery, there is a risk of inadvertently removing the parathyroid glands. At present, there is a lack of research on using Raman spectroscopy to discriminate parathyroid and thyroid tissues. In this article, samples were obtained from 43 individuals with thyroid and parathyroid tissues for Raman spectroscopy analysis. This study employed partial least squares (PLS) to reduce dimensions of data, and three optimization algorithms are used to improve the classification accuracy of SVM algorithm model in spectral analysis. The results show that PLS-GA-SVM algorithm has higher diagnostic accuracy and better reliability. The sensitivity of this algorithm is 94.67% and the accuracy is 94.44%. It can be concluded that Raman spectroscopy combined with the PLS-GA-SVM diagnostic algorithm has significant potential for discriminating thyroid and parathyroid tissues.

Application of Spatial Offset Raman Spectroscopy (SORS) and Machine Learning for Sugar Syrup Adulteration Detection in UK Honey

Honey authentication is a complex process which traditionally requires costly and time-consuming analytical techniques not readily available to the producers. This study aimed to develop non-invasive sensor methods coupled with a multivariate data analysis to detect the type and percentage of exogenous sugar adulteration in UK honeys. Through-container spatial offset Raman spectroscopy (SORS) was employed on 17 different types of natural honeys produced in the UK over a season. These samples were then spiked with rice and sugar beet syrups at the levels of 10%, 20%, 30%, and 50% w/w. The data acquired were used to construct prediction models for 14 types of honey with similar Raman fingerprints using different algorithms, namely PLS-DA, XGBoost, and Random Forest, with the aim to detect the level of adulteration per type of sugar syrup. The best-performing algorithm for classification was Random Forest, with only 1% of the pure honeys misclassified as adulterated and <3.5% of adulterated honey samples misclassified as pure. Random Forest was further employed to create a classification model which successfully classified samples according to the type of adulterant (rice or sugar beet) and the adulteration level. In addition, SORS spectra were collected from 27 samples of heather honey (24 Calluna vulgaris and 3 Erica cinerea) produced in the UK and corresponding subsamples spiked with high fructose sugar cane syrup, and an exploratory data analysis with PCA and a classification with Random Forest were performed, both showing clear separation between the pure and adulterated samples at medium (40%) and high (60%) adulteration levels and a 90% success at low adulteration levels (20%). The results of this study demonstrate the potential of SORS in combination with machine learning to be applied for the authentication of honey samples and the detection of exogenous sugars in the form of sugar syrups. A major advantage of the SORS technique is that it is a rapid, non-invasive method deployable in the field with potential application at all stages of the supply chain.

Shining Light on Osteoarthritis: Spatially Offset Raman Spectroscopy as a Window into Cartilage Health

Articular cartilage is a complex tissue, and early detection of osteoarthritis (OA) is crucial for effective treatment. However, current imaging modalities lack molecular specificity and primarily detect late-stage changes. In this study, we propose the use of spatially offset Raman spectroscopy (SORS) for noninvasive, depth-dependent, and molecular-specific diagnostics of articular cartilage. We demonstrate the potential of SORS to penetrate deep layers of cartilage, providing a comprehensive understanding of disease progression. Our SORS measurements were characterized and validated through mechanical and histological techniques, revealing strong correlations between spectroscopic measurements and both Young's modulus and depth of cartilage damage. By longitudinally monitoring enzymatically degraded condyles, we further developed a depth-dependent damage-tracking method. Our analysis revealed distinct components related to sample depth and glycosaminoglycan (GAG) changes, offering a comprehensive picture of cartilage health. Collectively, these findings highlight the potential of SORS as a valuable tool for enhancing OA management and improving patient outcomes.

Machine Learning-Driven SERS Nanoendoscopy and Optophysiology

A frontier of analytical sciences is centered on the continuous measurement of molecules in or near cells, tissues, or organs, within the biological context in situ, where the molecular-level information is indicative of health status, therapeutic efficacy, and fundamental biochemical function of the host. Following the completion of the Human Genome Project, current research aims to link genes to functions of an organism and investigate how the environment modulates functional properties of organisms. New analytical methods have been developed to detect chemical changes with high spatial and temporal resolution, including minimally invasive surface-enhanced Raman scattering (SERS) nanofibers using the principles of endoscopy (SERS nanoendoscopy) or optical physiology (SERS optophysiology). Given the large spectral data sets generated from these experiments, SERS nanoendoscopy and optophysiology benefit from advances in data science and machine learning to extract chemical information from complex vibrational spectra measured by SERS. This review highlights new opportunities for intracellular, extracellular, and in vivo chemical measurements arising from the combination of SERS nanosensing and machine learning.

Spatially Offset Raman Spectroscopy toward In Vivo Assessment of the Adipose Tissue in Cardiometabolic Pathologies

Spatially offset Raman spectroscopy (SORS) enhanced the capabilities of Raman spectroscopy for the depth-resolved analysis of biological and diffusely scattering samples. This technique offers selective probing of subsurface layers, providing molecular insights without invasive procedures. While SORS has found application in biomedical research, up to now, studies have focused mainly on the detection of mineralization of bones and tissues. Herein, for the first time, SORS is used to assess the soft, organic tissue beneath the skin's surface. In this study, we demonstrate the diagnostic utility of a hand-held SORS device for evaluating the chemical composition of the adipose tissue. We compared perigonadal white adipose tissue (gWAT) in a murine model of atherosclerosis, heart failure, and high-fat diet (HFD) induced obesity. Our results reveal distinct chemical differences in gWAT between HFD-fed and control mice, showcasing the potential of SORS for intravital adipose tissue phenotype characterization. Furthermore, our findings underscore the effectiveness of SORS as a valuable tool for noninvasive assessment of the adipose tissue composition, holding potential diagnostic significance for metabolic disorders.

Advanced portable micro-SORS prototype coupled with SERDS for heritage science

We investigate the subsurface composition of turbid materials at the micro scale by means of a portable non-invasive technique, micro-spatially offset Raman spectroscopy (micro-SORS), combined with shifted excitation Raman difference spectroscopy (SERDS). This combination enables the microscale layer analysis and allows to deal effectively with highly fluorescing samples as well as ambient light, all in a form of an in-house portable prototype device optimised for applications in heritage science. The instrument comprises ability to simultaneously collect multiple spectra by means of an optical fibre bundle, thus reducing the dead time and simplifying the ease of deployment of the technique. The performance of the synergy between micro-SORS and 785 nm SERDS dual-wavelength diode laser is demonstrated on a stratified mock-up painting samples including highly fluorescing painted layers. This instrumental approach could be ground-breaking in heritage science, due to the largely unmet need of analysing the molecular composition of subsurface of artworks non-invasively and in situ, and in the presence of fluorescent background and ambient light. Moreover, many other fields are expected to benefit from this technological advancement such as solar energy, forensic and food analytical areas.

In vivo imaging using surface enhanced spatially offset raman spectroscopy (SESORS): balancing sampling frequency to improve overall image acquisition

In the field of optical imaging, the ability to image tumors at depth with high selectivity and specificity remains a challenge. Surface enhanced resonance Raman scattering (SERRS) nanoparticles (NPs) can be employed as image contrast agents to specifically target cells in vivo; however, this technique typically requires time-intensive point-by-point acquisition of Raman spectra. Here, we combine the use of "spatially offset Raman spectroscopy" (SORS) with that of SERRS in a technique known as "surface enhanced spatially offset resonance Raman spectroscopy" (SESORRS) to image deep-seated tumors in vivo. Additionally, by accounting for the laser spot size, we report an experimental approach for detecting both the bulk tumor, subsequent delineation of tumor margins at high speed, and the identification of a deeper secondary region of interest with fewer measurements than are typically applied. To enhance light collection efficiency, four modifications were made to a previously described custom-built SORS system. Specifically, the following parameters were increased: (i) the numerical aperture (NA) of the lens, from 0.2 to 0.34; (ii) the working distance of the probe, from 9 mm to 40 mm; (iii) the NA of the fiber, from 0.2 to 0.34; and (iv) the fiber diameter, from 100 μm to 400 μm. To calculate the sampling frequency, which refers to the number of data point spectra obtained for each image, we considered the laser spot size of the elliptical beam (6 × 4 mm). Using SERRS contrast agents, we performed in vivo SESORRS imaging on a GL261-Luc mouse model of glioblastoma at four distinct sampling frequencies: par-sampling frequency (12 data points collected), and over-frequency sampling by factors of 2 (35 data points collected), 5 (176 data points collected), and 10 (651 data points collected). In comparison to the previously reported SORS system, the modified SORS instrument showed a 300% improvement in signal-to-noise ratios (SNR). The results demonstrate the ability to acquire distinct Raman spectra from deep-seated glioblastomas in mice through the skull using a low power density (6.5 mW/mm2) and 30-times shorter integration times than a previous report (0.5 s versus 15 s). The ability to map the whole head of the mouse and determine a specific region of interest using as few as 12 spectra (6 s total acquisition time) is achieved. Subsequent use of a higher sampling frequency demonstrates it is possible to delineate the tumor margins in the region of interest with greater certainty. In addition, SESORRS images indicate the emergence of a secondary tumor region deeper within the brain in agreement with MRI and H&E staining. In comparison to traditional Raman imaging approaches, this approach enables improvements in the detection of deep-seated tumors in vivo through depths of several millimeters due to improvements in SNR, spectral resolution, and depth acquisition. This approach offers an opportunity to navigate larger areas of tissues in shorter time frames than previously reported, identify regions of interest, and then image the same area with greater resolution using a higher sampling frequency. Moreover, using a SESORRS approach, we demonstrate that it is possible to detect secondary, deeper-seated lesions through the intact skull.

Advanced Microspatially Offset Raman Spectroscopy for Noninvasive Imaging of Concealed Texts and Figures Using Raman Signal, Fluorescence Emission, and Overall Spectral Intensity

This study explores the possibility of using microspatially offset Raman spectroscopy (micro-SORS) imaging to reconstruct noninvasively letters and figures hidden by opaque layers. Micro-SORS experiments were conducted on mockup samples that mimic real situations encountered in the cultural heritage field, such as sealed letters with inaccessible text and original documents. Subsurface images were obtained using both the characteristic Raman bands of the hidden compounds and their different optical properties from the remaining matrix. In the latter case, contrast obtained through observing a difference in the overall spectral intensity and fluorescence profile rather than any specific Raman bands were used to track the images within the hidden layer. This approach opens new prospects for the use of micro-SORS in heritage science, with applications in the field that include the study of objects covered by opaque overlayers not only through their Raman signatures but also through differences in their optical properties (e.g., fluorescence emission, absorption).

Surface-Enhanced Spatially Offset Raman Spectroscopy in Tissue

One aim of personalized medicine is to use continuous or on-demand monitoring of metabolites to adjust prescription dosages in real time. Surface-enhanced spatially offset Raman spectroscopy (SESORS) is an optical technique capable of detecting surface-enhanced Raman spectroscopy (SERS)-active targets under a barrier, which may enable frequent metabolite monitoring. Here we investigate how the intensity of the signal from SERS-active material varies spatially through tissue, both experimentally and in a computational model. Implant-sized, SERS-active hydrogel was placed under different thicknesses of contiguous tissue. Emission spectra were collected at the air-tissue boundary over a range of offsets from the excitation site. New features were added to the Monte Carlo light-tissue interaction model to modify the optical properties after inelastic scattering and to calculate the distribution of photons as they exit the model. The Raman signals were detectable through all barrier thicknesses, with strongest emission for the case of 0 mm offset between the excitation and detector. A steep decline in the signal intensities occurred for offsets greater than 2 mm. These results did not match published SORS work (where targets were much larger than an implant). However, the model and experimental results agree in showing the greatest intensities at 0 mm offset and a steep gradient in the intensities with increasing offset. Also, the model showed an increase in the number of photons when the new, longer wavelengths were used following the Stokes shift for scattering and the graphical display of the exiting photons was helpful in the determination and confirmation of the optimal offset.

In Vivo Assessment of Bone Quality Without X-rays

PURPOSE OF REVIEW

This review summarizes recent advances in the assessment of bone quality using non-X-ray techniques.

RECENT FINDINGS

Quantitative ultrasound (QUS) provides multiple measurements of bone characteristics based on the propagation of sound through bone, the attenuation of that sound, and different processing techniques. QUS parameters and model predictions based on backscattered signals can discriminate non-fracture from fracture cases with accuracy comparable to standard bone mineral density (BMD). With advances in magnetic resonance imaging (MRI), bound water and pore water, or a porosity index, can be quantified in several long bones in vivo. Since such imaging-derived measurements correlate with the fracture resistance of bone, they potentially provide new BMD-independent predictors of fracture risk. While numerous measurements of mineral, organic matrix, and bound water by Raman spectroscopy correlate with the strength and toughness of cortical bone, the clinical assessment of person's bone quality using spatially offset Raman spectroscopy (SORS) requires advanced spectral processing techniques that minimize contaminating signals from fat, skin, and blood. Limiting exposure of patients to ionizing radiation, QUS, MRI, and SORS has the potential to improve the assessment of fracture risk and track changes of new therapies that target bone matrix and micro-structure.

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

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

Optimization of diffuse Raman spectroscopy for in-vivo quantification of foreign body response in a small animal model

Diffuse Raman spectroscopy (DRS) allows subsurface molecular analysis of optically turbid samples. Numerical modeling of light propagation was used as a method for improving the design of an DRS instrument to maximize the signal to noise ratio (SNR) while ensuring safe laser exposure parameters required for in-vivo measurements. Experimental validation of the model was performed on both phantom samples and disks implanted postmortem to mimic the typical response to foreign bodies (formation of a fibrotic capsule around an implant). A reduction of laser exposure of over 1500-fold was achieved over previous studies whilst maintaining the same Raman collection rates and reaching the safe power density of 3 mW/mm2. The validation of this approach in a subcutaneous implant in a mouse cadaver showed a further improvement of 1.5-fold SNR, with a thickness limit of detection for the fibrotic layer of 23 µm, under the same acquisition times. In the animal body, a thickness limit of detection of 16 µm was achieved. These results demonstrate the feasibility of numerical model-based optimization for DRS, and that the technique can be improved sufficiently to be used for in-vivo measurement of collagenous capsule formation as a result of the foreign body response in murine models.

Non-Destructive Identification of Dyes on Fabric Using Near-Infrared Raman Spectroscopy

Fabric is a commonly found piece of physical evidence at most crime scenes. Forensic analysis of fabric is typically performed via microscopic examination. This subjective approach is primarily based on pattern recognition and, therefore, is often inconclusive. Most of the fabric material found at crime scenes is colored. One may expect that a confirmatory identification of dyes can be used to enhance the reliability of the forensic analysis of fabric. In this study, we investigated the potential of near-infrared Raman spectroscopy (NIRS) in the confirmatory, non-invasive, and non-destructive identification of 15 different dyes on cotton. We found that NIRS was able to resolve the vibrational fingerprints of all 15 colorants. Using partial-squared discriminant analysis (PLS-DA), we showed that NIRS enabled ~100% accurate identification of dyes based on their vibrational signatures. These findings open a new avenue for the robust and reliable forensic analysis of dyes on fabric directly at crime scenes. Main conclusion: a hand-held Raman spectrometer and partial least square discriminant analysis (PLS-DA) approaches enable highly accurate identification of dyes on fabric.

Two-layer reconstruction of Raman spectra in diffusive media based on an analytical model in the time domain

We derive and validate an analytical model that describes the migration of Raman scattered photons in two-layer diffusive media, based on the diffusion equation in the time domain. The model is derived under a heuristic approximation that background optical properties are identical on the excitation and Raman emission wavelengths. Methods for the reconstruction of two-layer Raman spectra have been developed, tested in computer simulations and validated on tissue-mimicking phantom measurements data. Effects of different parameters were studied in simulations, showing that the thickness of the top layer and number of detected photon counts have the most significant impact on the reconstruction. The concept of quantitative, mathematically rigorous reconstruction using the proposed model was finally proven on experimental measurements, by successfully separating the spectra of silicone and calcium carbonate (calcite) layers, showing the potential for further development and eventual application in clinical diagnostics.

Innovative method for rapid detection of falsified COVID-19 vaccines through unopened vials using handheld Spatially Offset Raman Spectroscopy (SORS)

Preventing, detecting, and responding to substandard and falsified vaccines is of critical importance for ensuring the safety, efficacy, and public trust in vaccines. This is of heightened importance in context of public health crisis, such as the COVID-19 pandemic, in which extreme world-wide shortages of vaccines provided a fertile ground for exploitation by falsifiers. Here, a proof-of-concept study explored the feasibility of using a handheld Spatially Offset Raman Spectroscopy (SORS) device to authenticate COVID-19 vaccines through rapid analysis of unopened vaccine vials. The results show that SORS can verify the chemical identity of dominant excipients non-invasively through vaccine vial walls. The ability of SORS to identify potentially falsified COVID-19 vaccines was demonstrated by measurement of surrogates for falsified vaccines contained in vaccine vials. In all cases studied, the SORS technique was able to differentiate between surrogate samples from the genuine COVISHIELD™ vaccine. The genuine vaccines tested included samples from six batches across two manufacturing sites to account for any potential variations between batches or manufacturing sites. Batch and manufacturing site variations were insignificant. In conjunction with existing security features, for example on labels and packaging, SORS provided an intrinsic molecular fingerprint of the dominant excipients of the vaccines. The technique could be extended to other COVID-19 and non-COVID-19 vaccines, as well as other liquid medicines. As handheld and portable SORS devices are commercially available and widely used for other purposes, such as airport security, they are rapidly deployable non-invasive screening tools for vaccine authentication.

Locating Three-Dimensional Position of Deep-Seated SERS Phantom Lesions in Thick Tissues Using Tomographic Transmission Raman Spectroscopy

Locating distinct objects within a thick scattering medium remains a long-standing challenge in the fields of materials science, health, and engineering. Transmission Raman spectroscopy (TRS) with the use of surface-enhanced Raman scattering (SERS) nanoparticles has proven to be an effective approach to detect deep-seated lesions inside thick biological tissues. However, it has not yet been proven to spatially locate deep lesions in three dimensions using optical modalities. Herein, we present the concept of tomographic TRS and report its successful use for accurately locating SERS nanoparticles in elongated rod-like thick tissues. Our work starts with theoretical simulations of Raman photon propagation in tissues. We discovered a linear relationship between the Raman spectral peak ratio and propagation distance of Raman photons in tissues, allowing us to predict the location of lesions tagged by SERS NPs. Based on this, we propose a two-step tomographic TRS strategy, which includes axial scanning and ring scanning. We demonstrate the robustness of our approach using ex vivo thick tissue (4.5 cm in thickness) and locate an embedded SERS phantom lesion, with a ring scanning step of 10-30°. We successfully locate multiple SERS phantom lesions in the ex vivo porcine muscle stack with high accuracy (absolute error of <2 mm). Our method is rapid, efficient, and of low cost compared to current tomographic medical imaging techniques. This work advances Raman techniques for three-dimensional positioning and offers new insights toward practical diagnosis applications.

Shining Light on Osteoarthritis: Spatially Offset Raman Spectroscopy as a Window into Cartilage Health

Articular cartilage is a complex tissue, and early detection of osteoarthritis (OA) is crucial for effective treatment. However, current imaging modalities lack molecular specificity and primarily detect late-stage changes. In this study, we propose the use of Spatially Offset Raman Spectroscopy (SORS) for non-invasive, depth-dependent, and molecular-specific diagnostics of articular cartilage. We demonstrate the potential of SORS to penetrate deep layers of cartilage, providing a comprehensive understanding of disease progression. Our SORS measurements were characterized and validated through mechanical and histological techniques, revealing strong correlations between spectroscopic measurements and both Young's modulus and depth of cartilage damage. By longitudinally monitoring enzymatically degraded condyles, we further developed a depth-dependent damage-tracking method. Our analysis revealed distinct components related to sample depth and glycosaminoglycan (GAG) changes, offering a comprehensive picture of cartilage health. Collectively, these findings highlight the potential of SORS as a valuable tool for enhancing OA management and improving patient outcomes.

Non-Invasive Detection, Precise Localization, and Perioperative Navigation of In Vivo Deep Lesions Using Transmission Raman Spectroscopy

Non-invasive detection and precise localization of deep lesions have attracted significant attention for both fundamental and clinical studies. Optical modality techniques are promising with high sensitivity and molecular specificity, but are limited by shallow tissue penetration and the failure to accurately determine lesion depth. Here the authors report in vivo ratiometric surface-enhanced transmission Raman spectroscopy (SETRS) for non-invasive localization and perioperative surgery navigation of deep sentinel lymph nodes in live rats. The SETRS system uses ultrabright surface-enhanced Raman spectroscopy (SERS) nanoparticles with a low detection limit of 10 pM and a home-built photosafe transmission Raman spectroscopy setup. The ratiometric SETRS strategy is proposed based on the ratio of multiple Raman spectral peaks for obtaining lesion depth. Via this strategy, the depth of the phantom lesions in ex vivo rat tissues is precisely determined with a mean-absolute-percentage-error of 11.8%, and the accurate localization of a 6-mm-deep rat popliteal lymph node is achieved. The feasibility of ratiometric SETRS allows the successful perioperative navigation of in vivo lymph node biopsy surgery in live rats under clinically safe laser irradiance. This study represents a significant step toward the clinical translation of TRS techniques, providing new insights for the design and implementation of in vivo SERS applications.

Point-of-care community drug checking technologies: an insider look at the scientific principles and practical considerations

Drug checking is increasingly being explored outside of festivals and events to be an ongoing service within communities, frequently integrated within responses to illicit drug overdose. The choice of instrumentation is a common question, and the demands on these chemical analytical instruments can be challenging as illicit substances may be more complex and include highly potent ingredients at trace levels. The answer remains nuanced as the instruments themselves are not directly comparable nor are the local demands on the service, meaning implementation factors heavily influence the assessment and effectiveness of instruments. In this perspective, we provide a technical but accessible introduction to the background of a few common drug checking methods aimed at current and potential drug checking service providers. We discuss the following tools that have been used as part of the Vancouver Island Drug Checking Project in Victoria, Canada: immunoassay test strips, attenuated total reflection IR-absorption spectroscopy, Raman spectroscopy from powder samples, surface-enhanced Raman scattering in a solution of colloidal gold nanoparticles, and gas chromatography-mass spectrometry. Using four different drug mixtures received and tested at the service, we illustrate the strengths, limitations, and capabilities of such instruments, and expose the scientific theory to give further insight into their analytical results. Each case study provides a walk-through-style analysis for a practical comparison between data from several different instruments acquired on the same sample. Ideally, a single instrument would be able to achieve all of the objectives of drug checking. However, there is no clear instrument that ticks every box; low cost, portable, rapid, easy-to-use and provides highly sensitive identification and accurate quantification. Multi-instrument approaches to drug checking may be required to effectively respond to increasingly complex and highly potent substances demanding trace level detection and the potential for quantification.

Open-sourced Raman spectroscopy data processing package implementing a baseline removal algorithm validated from multiple datasets acquired in human tissue and biofluids

SIGNIFICANCE

Standardized data processing approaches are required in the field of bio-Raman spectroscopy to ensure information associated with spectral data acquired by different research groups, and with different systems, can be compared on an equal footing.

AIM

An open-sourced data processing software package was developed, implementing algorithms associated with all steps required to isolate the inelastic scattering component from signals acquired using Raman spectroscopy devices. The package includes a novel morphological baseline removal technique (BubbleFill) that provides increased adaptability to complex baseline shapes compared to current gold standard techniques. Also incorporated in the package is a versatile tool simulating spectroscopic data with varying levels of Raman signal-to-background ratios, baselines with different morphologies, and varying levels of stochastic noise.

RESULTS

Application of the BubbleFill technique to simulated data demonstrated superior baseline removal performance compared to standard algorithms, including iModPoly and MorphBR. The data processing workflow of the open-sourced package was validated in four independent in-human datasets, demonstrating it leads to inter-systems data compatibility.

CONCLUSIONS

A new open-sourced spectroscopic data pre-processing package was validated on simulated and real-world in-human data and is now available to researchers and clinicians for the development of new clinical applications using Raman spectroscopy.

In Vivo Surface-Enhanced Transmission Raman Spectroscopy under Maximum Permissible Exposure: Toward Photosafe Detection of Deep-Seated Tumors

The detection of deep-seated lesions is of great significance for biomedical applications. However, due to the strong photon absorption and scattering of biological tissues, it is challenging to realize in vivo deep optical detections, particularly for those using the safe laser irradiance below clinical maximum permissible exposure (MPE). In this work, the combination of ultra-bright surface-enhanced Raman scattering (SERS) nanotags and transmission Raman spectroscopy (TRS) is reported to achieve the non-invasive and photosafe detection of "phantom" lesions deeply hidden in biological tissues, under the guidance of theoretical calculations showing the importance of SERS nanotags' brightness and the expansion of laser beam size. Using a home-built TRS system with a laser power density of 0.264 W cm^-2 (below the MPE criteria), we successfully demonstrated the detection of SERS nanotags through up to 14-cm-thick ex vivo porcine tissues, as well as in vivo imaging of "phantom" lesions labeled by SERS nanotags in a 1.5-cm-thick unshaved mouse under MPE. This work highlights the potential of transmission Raman-guided identification and non-invasive imaging toward clinically photosafe cancer diagnoses.

Optical recognition of constructs using hyperspectral imaging and detection (ORCHID)

Challenges to deep sample imaging have necessitated the development of special techniques such as spatially offset optical spectroscopy to collect signals that have travelled through several layers of tissue. However, these techniques provide only spectral information in one dimension (i.e., depth). Here, we describe a general and practical method, referred to as Optical Recognition of Constructs Using Hyperspectral Imaging and Detection (ORCHID). The sensing strategy integrates (1) the spatial offset detection concept by computationally binning 2D optical data associated with digital offsets based on selected radial pixel distances from the excitation source; (2) hyperspectral imaging using tunable filter; and (3) digital image binding and collation. ORCHID is a versatile modality that is designed to collect optical signals deep inside samples across three spatial (X, Y, Z) as well as spectral dimensions. The ORCHID method is applicable to various optical techniques that exhibit narrow-band structures, from Raman scattering to quantum dot luminescence. Samples containing surface-enhanced Raman scattering (SERS)-active gold nanostar probes and quantum dots embedded in gel were used to show a proof of principle for the ORCHID concept. The resulting hyperspectral data cube is shown to spatially locate target emitting nanoparticle volumes and provide spectral information for in-depth 3D imaging.

Evaluating Spatially Offset Low-Frequency Anti-Stokes Raman Spectroscopy (SOLFARS) for Detecting Subsurface Composition below an Emissive Layer: A Proof of Principle Study Using a Model Bilayer System

This work explores the potential use of spatially offset low-frequency anti-Stokes Raman spectroscopy (SOLFARS) to detect subsurface composition below an emissive surface. A range of bilayer tablets were used to evaluate this approach. Bilayer tablets differed in both the underlying layer composition (active pharmaceutical ingredient to excipient ratio, celecoxib: α-lactose monohydrate) and the upper layer thickness of the fluorescent coating (polyvinylpyrrolidone mixture with sunset yellow FCF dye). Two low- (<300 cm^-1) plus mid- (300 to 1800 cm^-1) frequency Raman instrumental setups, with lateral displacements for spatial analysis of solid dosage forms, using different excitation wavelengths were explored. The 532 nm system was used to illustrate how the low-frequency anti-Stokes Raman approach works with samples exhibiting extreme fluorescence/background emission interference, and the 785 nm system was used to demonstrate the performance when less extreme fluorescence/emission is present. Qualitative and quantitative chemometric analyses were performed to evaluate the performance of individual spectral domains and their combinations for the determination of the composition of the subsurface layer as well as the coating layer thickness. Overall, the commonly used midfrequency region (300-1800 cm^-1) proved superior when using 785 nm incident laser for quantifying the coating thickness (amorphous materials), whereas a combined Stokes and anti-Stokes low-frequency region was found to be superior for quantifying underlying crystalline materials. When exploring individual spectral regions for subsurface composition using spatially offset measurements, the anti-Stokes LFR spectral window performed best. The anti-Stokes low-frequency range also demonstrated an advantage for models composed of data exhibiting high levels of fluorescence (e.g., data collected using 532 nm incident laser), as the Stokes scattering was masked by fluorescence. Transmission measurements were also explored for comparison and showed the best applicability for both upper and lower layer analysis, attributed to the inherently larger bulk sampling volume of this setup. From a practical perspective, these results highlight the potential adjustments that can be made to already existing (in-line) Raman setups to facilitate similar analysis in pharmaceutical industry-based settings.

Assessing the quality of stored red blood cells using handheld Spatially Offset Raman spectroscopy with multisource correlation analysis

In this work we employ Spatially Offset Raman Spectroscopy (SORS) to non-invasively identify storage-related changes in red blood cell concentrate (RCC) in-situ within standard plastic transfusion bags. To validate the measurements, we set up a parallel study comparing both bioanalytical data (obtained by blood-gas analysis, hematology analysis and spectrophotometric assays), and Raman spectrometry data from the same blood samples. We then employ Multisource Correlation Analysis (MuSCA) to correlate the different types of data in RCC. Our analysis confirmed a strong correlation of glucose, methemoglobin and oxyhemoglobin with their respective bioassay values in RCC units. Finally, by combining MuSCA with k-means clustering, we assessed changes in all Raman wavenumbers during cold storage in both RCC Raman data from the current study and parallel RCC supernatant Raman data previously acquired from the same units. Direct RCC quality monitoring during storage, would help to establish a basis for improved inventory management of blood products in blood banks and hospitals based on analytical data.

Tomographic Imaging and Localization of Nanoparticles in Tissue Using Surface-Enhanced Spatially Offset Raman Spectroscopy

A fundamental question crucial to surface-enhanced spatially offset Raman spectroscopy (SESORS) imaging and implementing it in a clinical setting for in vivo diagnostic purposes is whether a SESORS image can be used to determine the exact location of an object within tissue? To address this question, multiple experimental factors pertaining to the optical setup in imaging experiments using an in-house-built point-collection-based spatially offset Raman spectroscopy (SORS) system were investigated to determine those critical to the three-dimensional (3D) positioning capability of SESORS. Here, we report the effects of the spatial offset magnitude and geometry on locating nanoparticles (NPs) mixed with silica powder as an imaging target through tissue and outline experimental techniques to allow for the correct interpretation of SESORS images to ascertain the correct location of NPs in the two-dimensional x, y-imaging plane at depth. More specifically, the effect of "linear offset-induced image drag" is presented, which refers to a spatial distortion in SESORS images caused by the magnitude and direction of the linear offset and highlight the need for an annular SORS collection geometry during imaging to neutralize these asymmetric effects. Additionally, building on these principles, the concept of "ratiometric SESORS imaging" is introduced for the location of buried inclusions in three dimensions. Together these principles are vital in developing a methodology for the location of surface-enhanced Raman scattering-active inclusions in three dimensions. This approach utilizes the relationship between the magnitude of the spatial offset, the probed depth, and ratiometric analysis of the NP and tissue Raman intensities to ultimately image and spatially discriminate between two distinct NP flavors buried at different depths within a 3D model for the first time. This research demonstrates how to accurately identify multiple objects at depth in tissue and their location using SESORS which addresses a key capability in moving SESORS closer to use in biomedical applications.

A packaged food internal Raman signal separation method based on spatially offset Raman spectroscopy combined with FastICA

Raman spectroscopy attempts to reflect food quality by characterizing molecular vibration and rotation. However, the blocking of optical signals by packaging materials and the interference of the optical signal generated by the packaging itself make the detection of internal food quality without destroying packaging highly difficult. In this regard, this paper proposes a novel packaged food internal signal separation based on spatially offset Raman spectroscopy (SORS) coupled with improved fast independent component analysis (FastICA). Firstly, the Raman scattering image of the packaged food with offset laser incident point was obtained. Then, the movable quadratic mean of information entropy was used to select the observation feature region of the image. Thirdly, the main independents decomposed by the optimized FastICA method were identified by spectral attenuation characteristics of the SORS peak signal. Finally, the non-negativity of the separated signal was ensured by baseline recognition and correction. The effectiveness of this method was verified by refactoring the similarity between the signal and the reference signal by testing three different packaging and four internal materials under standard experimental conditions. The applicability of the method was proved by the internal signal separation of three packaged foods on sale. The experimental results indicate that the proposed method can separate the Raman signal of packaged food and can be used as a pretreatment method and auxiliary analysis means for the detection of packaged food.