Spatially Offset Raman Spectroscopy Measurements Research Articles

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.

Reconstructing in vivo spatially offset Raman spectroscopy of human skin tissue using a GPU-accelerated Monte Carlo platform

As one type of spatially offset Raman spectroscopy (SORS), inverse SORS is particularly suited to in vivo biomedical measurements due to its ring-shaped illumination scheme. To explain inhomogeneous Raman scattering during in vivo inverse SORS measurements, the light–tissue interactions when excitation and regenerated Raman photons propagate in skin tissue were studied using Monte Carlo simulation. An eight-layered skin model was first built based on the latest transmission parameters. Then, an open-source platform, Monte Carlo eXtreme (MCX), was adapted to study the distribution of 785 nm excitation photons inside the model with an inverse spatially shifted annular beam. The excitation photons were converted to emission photons by an inverse distribution method based on excitation flux with spatial offsets Δs of 1 mm, 2 mm, 3 mm and 5 mm. The intrinsic Raman spectra from separated skin layers were measured by continuous linear scanning to improve the simulation accuracy. The obtained results explain why the spectral detection depth gradually increases with increasing spatial offset, and address how the intrinsic Raman spectrum from deep skin layers is distorted by the reabsorption and scattering of the superficial tissue constituents. Meanwhile, it is demonstrated that the spectral contribution from subcutaneous fat will be improved when the offset increases to 5 mm, and the highest detection efficiency for dermal layer spectral detection could be achieved when Δs = 2 mm. Reasonably good matching between the calculated spectrum and the measured in vivo inverse SORS was achieved, thus demonstrating great utility of our modeling method and an approach to help understand the clinical measurements.

Sensitivity of the amide I band to matrix manipulation in bone: a Raman micro-spectroscopy and spatially offset Raman spectroscopy study.

The fracture resistance of bone arises from the hierarchical arrangement of minerals, collagen fibrils (i.e., cross-linked triple helices of α1 and α2 collagen I chains), non-collagenous proteins, and water. Raman spectroscopy (RS) is not only sensitive to the relative fractions of these constituents, but also to the secondary structure of bone proteins. To assess the ability of RS to detect differences in the protein structure, we quantified the effect of sequentially autoclaving (AC) human cortical bone at 100 °C (∼34.47 kPa) and then at 120 °C (∼117.21 kPa) on the amide I band using a commercial Raman micro-spectroscopy (μRS) instrument and custom spatially offset RS (SORS) instrument in which rings of collection fiber optics are offset from the central excitation fiber optics within a hand-held, cylindrical probe. Being clinically viable, measurements by SORS involved collecting Raman spectra of cadaveric femur mid-shafts (5 male & 5 female donors) through layers of a tissue mimic. Otherwise, μRS and SORS measurements were acquired directly from each bone. AC-related changes in the helical status of collagen I were assessed using amide I sub-peak ratios (intensity, I, at ∼1670 cm-1 relative to intensities at ∼1610 cm-1 and ∼1640 cm-1). The autoclaving manipulation significantly decreased the selected amide I sub-peak ratios as well as shifted peaks at ∼1605 cm-1 (μRS), ∼1636 cm-1 (SORS) and ∼1667 cm-1 in both μRS and SORS. Compared to μRS, SORS detected more significant differences in the amide I sub-peak ratios when the fiber optic probe was directly applied to bone. SORS also detected AC-related decreases in I1670/I1610 and I1670/I1640 when spectra were acquired through layers of the tissue mimic with a thickness ≤2 mm by the 7 mm offset ring, but not with the 5 mm or 6 mm offset ring. Overall, the SORS instrument was more sensitive than the conventional μRS instrument to pressure- and temperature-related changes in the organic matrix that affect the fracture resistance of bone, but SORS analysis of the amide I band is limited to an overlying thickness layer of 2 mm.

Spatially Offset Raman Spectroscopy-How Deep?

Spatially offset Raman spectroscopy (SORS) is a technique for interrogating the subsurface composition of turbid samples noninvasively. This study generically addresses a fundamental question relevant to a wide range of SORS studies, which is how deep SORS probes for any specific spatial offset when analyzing a turbid sample or, in turn, what magnitude of spatial offset one should select to probe a specific depth. This issue is addressed by using Monte Carlo simulations, under the assumption of negligible absorption, which establishes that the key parameter governing the extent of the probed zone for a point-like illumination and point-like collection SORS geometry is the reduced scattering coefficient of the medium. This can either be deduced from literature data or directly estimated from a SORS measurement by evaluating the Raman intensity profile from multiple spatial offsets. Once this is known, the extent of the probed zone can be determined for any specific SORS spatial offset using the Monte Carlo simulation results presented here. The proposed method was tested using experimental data on stratified samples by analyzing the signal detected from a thin layer that was moved through a stack of layers using both non-absorbing and absorbing samples. The proposed simple methodology provides important additional information on SORS measurements with direct relevance to a wide range of SORS applications including biomedical, pharmaceutical, security, forensics, and cultural heritage.

Estimating the Reduced Scattering Coefficient of Turbid Media Using Spatially Offset Raman Spectroscopy.

We propose a new method for estimating the reduced scattering coefficient, μs', of turbid homogeneous samples using Spatially Offset Raman Spectroscopy (SORS). The concept is based around the variation of Raman signal with SORS spatial offset that is strongly μs'-dependent, as such, permitting the determination of μs'. The evaluation is carried out under the assumptions that absorption is negligible at the laser and Raman wavelengths and μs' is approximately the same for those two wavelengths. These conditions are often satisfied for samples analyzed in the NIR region of the spectrum where SORS is traditionally deployed. Through a calibration procedure on a PTFE model sample, it was possible to estimate the μs' coefficient of different turbid samples with an error (RMSEP) below 18%. The knowledge of μs' in the NIR range is highly valuable for facilitating accurate numerical simulations to optimize illumination and collection geometries in SORS, to derive in-depth information about the properties of SORS measurements or in other photon applications, dependent on photon propagation in turbid media with general impact across fields such as biomedical, pharmaceutical, security, forensic, and cultural sciences.

Detection of Biomarkers Relating to Quality and Differentiation of Some Commercially Significant Whole Fish Using Spatially Off-Set Raman Spectroscopy.

Aquaculture represents a major part of the world’s food supply. This area of food production is developing rapidly, and as such the tools and analytical techniques used to monitor and assess the quality of fish need to also develop and improve. The use of spatially off-set Raman spectroscopy (SORS) is particularly well-suited for these applications, given the ability of this technique to take subsurface measurements as well as being rapid, non-destructive and label-free compared to classical chemical analysis techniques. To explore this technique for analysing fish, SORS measurements were taken on commercially significant whole fish through the skin in different locations. The resulting spectra were of high quality with subsurface components such as lipids, carotenoids, proteins and guanine from iridophore cells clearly visible in the spectra. These spectral features were characterised and major bands identified. Chemometric analysis additionally showed that clear differences are present in spectra not only from different sections of a fish but also between different species. These results highlight the potential application for SORS analysis for rapid quality assessment and species identification in the aquaculture industry by taking through-skin measurements.

A Spatially Offset Raman Spectroscopy Method for Non-Destructive Detection of Gelatin-Encapsulated Powders.

Non-destructive subsurface detection of encapsulated, coated, or seal-packaged foods and pharmaceuticals can help prevent distribution and consumption of counterfeit or hazardous products. This study used a Spatially Offset Raman Spectroscopy (SORS) method to detect and identify urea, ibuprofen, and acetaminophen powders contained within one or more (up to eight) layers of gelatin capsules to demonstrate subsurface chemical detection and identification. A 785-nm point-scan Raman spectroscopy system was used to acquire spatially offset Raman spectra for an offset range of 0 to 10 mm from the surfaces of 24 encapsulated samples, using a step size of 0.1 mm to obtain 101 spectral measurements per sample. As the offset distance was increased, the spectral contribution from the subsurface powder gradually outweighed that of the surface capsule layers, allowing for detection of the encapsulated powders. Containing mixed contributions from the powder and capsule, the SORS spectra for each sample were resolved into pure component spectra using self-modeling mixture analysis (SMA) and the corresponding components were identified using spectral information divergence values. As demonstrated here for detecting chemicals contained inside thick capsule layers, this SORS measurement technique coupled with SMA has the potential to be a reliable non-destructive method for subsurface inspection and authentication of foods, health supplements, and pharmaceutical products that are prepared or packaged with semi-transparent materials.

Subsurface inspection of food safety and quality using line-scan spatially offset Raman spectroscopy technique

Subsurface inspection of food and agricultural products is challenging for optical-based sensing techniques due to complex interactions between light and heterogeneous or layered samples. In this study, a method for subsurface food inspection was presented based on a newly developed line-scan spatially offset Raman spectroscopy (SORS) technique. A 785 nm point laser was used as a Raman excitation source. The line-shape SORS data from the sample was collected in a wavenumber range of 0–2815 cm−1 using a detection module consisting of an imaging spectrograph and a CCD camera. Two layered samples, one by placing a 1 mm thick plastic sheet cut from original container on top of cane sugar and the other by placing a 5 mm thick carrot slice on top of melamine powder, were created to test the subsurface food inspection method. For each sample, a whole set of SORS data was acquired using one CCD exposure in an offset range of 0–36 mm (two sides of the incident laser point) with a spatial interval of 0.07 mm. Raman spectra from the cane sugar under the plastic sheet and the melamine powder under the carrot slice were successfully resolved using self-modeling mixture analysis (SMA) algorithms, demonstrating the potential of the technique for authenticating foods and ingredients through packaging and evaluating internal food safety and quality attributes. The line-scan SORS measurement technique provides a rapid and nondestructive method for subsurface inspection of food safety and quality.

A line‐scan hyperspectral Raman system for spatially offset Raman spectroscopy

Spatially offset Raman spectroscopy (SORS) is a technique that can obtain subsurface layered information by collecting Raman spectra from a series of surface positions laterally offset from the excitation laser. Currently optical fiber probes are used as major tools in SORS measurement, which are either slow (single fiber probe with mechanical movement) or restricted in selecting offset range and interval (fiber probe array). This study proposes a new method to conduct SORS measurement based on a newly developed line‐scan hyperspectral Raman imaging system. A 785‐nm point laser was used as an excitation source. A detection module consisting of an imaging spectrograph and a charge‐coupled device camera was used to acquire line‐shape SORS data in a spectral region of −592 to 3015 cm−1. Using a single scan, the system allowed simultaneous collection of a series of Raman spectra in a broad offset range (e.g. 0–36 mm in two sides of the incident laser) with a narrow interval (e.g. 0.07 mm). Four layered samples were created by placing butter slices with thicknesses of 1, 4, 7, and 10 mm on top of melamine powder, providing different individual Raman characteristics to test the line‐scan SORS technique. Self‐modeling mixture analysis (SMA) was used to analyze the SORS data. Raman spectra from butter and melamine were successfully retrieved for all four butter‐on‐melamine samples using the SMA method. The line‐scan SORS measurement technique provides a flexible and efficient method for subsurface evaluation, which has potential to be used for food safety and quality inspection. Copyright © 2015 John Wiley & Sons, Ltd.

Towards the in vivo prediction of fragility fractures with Raman spectroscopy.

Fragility fractures, those fractures which result from low level trauma, have a large and growing socio‐economic cost in countries with aging populations. Bone‐density‐based assessment techniques are vital for identifying populations that are at higher risk of fracture, but do not have high sensitivity when it comes to identifying individuals who will go on to have their first fragility fracture. We are developing Spatially Offset Raman Spectroscopy (SORS) as a tool for retrieving chemical information from bone non‐invasively in vivo. Unlike X‐ray‐based techniques SORS can retrieve chemical information from both the mineral and protein phases of the bone. This may enable better discrimination between those who will or will not go on to have a fragility fracture because both phases contribute to bone's mechanical properties. In this study we analyse excised bone with Raman spectroscopy and multivariate analysis, and then attempt to look for similar Raman signals in vivo using SORS. We show in the excised work that on average, bone fragments from the necks of fractured femora are more mineralised (by 5–10%) than (cadaveric) non‐fractured controls, but the mineralisation distributions of the two cohorts are largely overlapped. In our in vivo measurements, we observe similar, but as yet statistically underpowered, differences. After the SORS data (the first SORS measurements reported of healthy and diseased human cohorts), we identify methodological developments which will be used to improve the statistical significance of future experiments and may eventually lead to more sensitive prediction of fragility fractures. © 2015 The Authors. Journal of Raman Spectroscopy Published by John Wiley & Sons, Ltd.

A Novel Approach for Subsurface Through-Skin Analysis of Salmon Using Spatially Offset Raman Spectroscopy (SORS)

In the present study, the possibility of employing spatially offset Raman spectroscopy (SORS) in the qualitative and quantitative characterization of quality parameters of salmon through the skin has been explored. A laboratory-based SORS setup comprising an 830 nm laser was employed, and intact samples and model samples made of salmon tissue constituents were used to investigate the penetration of Raman signals through the dark and light part of salmon skin. Intact salmon samples with both dark and light skin were measured at different spatial offsets. When using spatial offsets in the range of 5-6 mm, the results clearly show that information regarding fatty acid composition and carotenoid content could be obtained from both dark and light parts of the skin. Similar information could not be obtained using conventional backscattering Raman spectroscopy. Model samples of ground salmon spiked with either solutions of carotenoids or a range of vegetable oils were also measured, and at a spatial offset of 5 mm, a clear relationship between Raman carotenoid band intensities and carotenoid concentrations in the model samples was revealed. In addition, high correlations for the estimation of iodine values (i.e., fatty acid unsaturation) could be obtained for SORS measurements through light and dark parts of the salmon skin. A crude estimate suggested that information from around 5 mm beneath the surface area of the salmon skin could be obtained. The choice of a laser line in the near-infrared region is a major prerequisite for successful through-skin analysis of salmon. This feasibility study could pave the way for future Raman analysis of intact salmon.

Nondestructive evaluation of internal maturity of tomatoes using spatially offset Raman spectroscopy

This research explored the use of spatially offset Raman spectroscopy (SORS) for nondestructive evaluation of internal maturity of tomatoes. A Raman system using a 785-nm laser was developed to collect spatially offset spectra in the wavenumber range of 200–2500cm−1. The SORS measurements were conducted using a source-detector distance ranging from 0 to 5mm with a step size of 0.2mm. One hundred and sixty tomatoes at seven ripeness stages (i.e., immature green, mature green, breaker, turning, pink, light red, and red) were tested. The feasibility of the SORS for subsurface detection was examined by using a Teflon slab placed under outer pericarp slices of 5-mm and 10-mm thicknesses cut from green and red tomatoes. Raman signals from the outer pericarp layer and the Teflon layer were effectively separated by self-modeling mixture analysis of the offset spectra after fluorescence correction. Three Raman peaks due to carotenoids inside the tomatoes started showing at the mature green stage. Two peaks appeared consistently at 1001 and 1151cm−1, and the third peak was gradually shifted from 1525cm−1 (lutein at mature green stage) to 1513cm−1 (lycopene at red stage) owing to the loss of lutein and β-carotene and the accumulation of lycopene during tomato ripening. The Raman peak changes were evaluated by spectral information divergence (SID) with pure lycopene as the reference. The SID values decreased as the tomatoes ripened, and thus these values can be used to evaluate the internal ripeness of tomatoes.

Investigation of Raman chemical imaging for detection of lycopene changes in tomatoes during postharvest ripening

Lycopene is a major carotenoid in tomatoes and detecting changes in its content can be used to monitor the ripening of tomatoes. Raman chemical imaging is a new technique that shows promise for mapping constituents of interest in complex food matrices. In this study, a benchtop point-scan Raman chemical imaging system was developed to detect and visualize internal lycopene distribution during postharvest ripening of tomatoes. Tomato samples at different ripeness stages (i.e., green, breaker, turning, pink, light red, and red) were selected and cut open for imaging. Hyperspectral Raman images were acquired from fruit cross-sections in the wavenumber range of 200–2500 cm −1 with a spatial resolution of 1 mm. A polynomial curve-fitting method was used to correct for the underlying fluorescence background in the original spectra. A hyperspectral image classification method was developed based on spectral information divergence to identify lycopene in the tomato cross-sections. Raman chemical images were created to visualize the spatial distribution of the lycopene for different ripeness stages. The system was also configured to test the feasibility of utilizing spatially offset Raman spectroscopy (SORS) technique for subsurface detection of a Teflon slab placed under samples of outer pericarp cut in 5-mm and 10-mm thick slices from green and breaker tomatoes. The results showed that the Raman spectrum of Teflon can be extracted from the SORS measurements of the pericarps placed over the Teflon, demonstrating the potential of the future development of a Raman-based nondestructive approach for subsurface detection of lycopene as an indicator of tomato maturity.

Monte Carlo Model of Spatially Offset Raman Spectroscopy for Breast Tumor Margin Analysis

We have previously demonstrated the discrimination of two layers of soft tissue, specifically normal breast tissue overlying breast tumor, using spatially offset Raman spectroscopy (SORS). In this report, a Monte Carlo code for evaluating SORS in soft tissues has been developed and compared to experimental results. The model was employed to investigate the effects of tissue and probe geometry on SORS measurements and therefore to develop the design strategies of applying SORS for breast tumor surgical margin evaluation. The model was used to predict SORS signals for different tissue geometries difficult to precisely control experimentally, such as varying normal and tumor layer sizes and the addition of a third layer. The results from the model suggest that, using source-detector separations of up to 3.75 mm, SORS can detect sub-millimeter-thick tumors under a 1 mm normal layer, and tumors at least 1 mm thick can be detected under a 2 mm normal layer.

Determination of Ideal Offset for Spatially Offset Raman Spectroscopy

A key design parameter in spatially offset Raman spectroscopy (SORS) is the choice of offset distance between the illumination and collection areas. To investigate this choice, we performed SORS measurements on a simple two-layer chemical phantom. We show that while the SORS ratio, or the ratio of signal from the bottom layer to the top layer, monotonically increases with spatial offset, the signal-to-noise ratio (SNR) does not. Specifically, we show that there exists a specific spatial offset that yields the best SNR for signal originating in the bottom layer of a two-layer sample. We also show that this SNR-optimal offset depends upon the strength of the particular Raman band. This work presents the considerations that should be taken into account when designing optical probes for use in SORS.