Spatially Offset Raman Spectroscopy Research Articles

Rapid testing in the food industry: the potential of Fourier transform near-infrared (FT-NIR) spectroscopy and spatially offset Raman spectroscopy (SORS) to detect raw material defects in hazelnuts (Corylus avellana L.)

The detection of raw material defects, e.g., due to incorrect or excessively long storage, is an important issue in incoming goods inspections in the food industry. Fast and easy-to-use analytical methods for evaluating the usability of raw materials are particularly important. In this study, the applicability of Fourier transform near-infrared (FT-NIR) spectroscopy and spatially offset Raman spectroscopy (SORS) for the detection of raw material defects was evaluated. For this purpose, six hazelnut batches stored at different temperatures, humidity levels, and storage times were used as examples in this pilot study. Classification models of samples before and after the different physical treatments show that the resulting changes can be detected by FT-NIR spectroscopy and SORS at elevated temperature and humidity. When one of the storage parameters is increased, FT-NIR spectroscopy is also useful for detecting differences between sample groups. In contrast, SORS cannot distinguish between pre- and post-stored samples when only one of the storage parameters is increased, making SORS unsuitable for incoming inspection of nuts. FT-NIR spectroscopy analysis is also a fast application, because freeze-drying of the sample material prior to analysis is not required as the results before and after freeze-drying are comparable. Combining the FT-NIR spectroscopy and SORS data in a low-level data fusion improved the classification models for samples stored at low storage temperatures, suggesting that the two methods provide complementary information. In summary, analyzing nuts with FT-NIR spectroscopy and SORS, as shown for hazelnuts, has the potential to identify abnormal samples during incoming goods inspections.

In Vivo Assessment of Bone Quality Without X-rays.

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

Discrimination/Classification of Edible Vegetable Oils from Raman Spatially Solved Fingerprints Obtained on Portable Instrumentation.

Currently, the combination of fingerprinting methodology and environmentally friendly and economical analytical instrumentation is becoming increasingly relevant in the food sector. In this study, a highly versatile portable analyser based on Spatially Offset Raman Spectroscopy (SORS) obtained fingerprints of edible vegetable oils (sunflower and olive oils), and the capability of such fingerprints (obtained quickly, reliably and without any sample treatment) to discriminate/classify the analysed samples was evaluated. After data treatment, not only unsupervised pattern recognition techniques (as HCA and PCA), but also supervised pattern recognition techniques (such as SVM, kNN and SIMCA), showed that the main effect on discrimination/classification was associated with those regions of the Raman fingerprint related to free fatty acid content, especially oleic and linoleic acid. These facts allowed the discernment of the original raw material used in the oil's production. In all the models established, reliable qualimetric parameters were obtained.

Chemometric enhancement for blind signal resolution from non-invasive spatially offset Raman spectra

Spatially offset Raman spectroscopy (SORS) is a promising spectroscopic technique that enables the collection of Raman signals from deeper layers of materials compared to conventional Raman spectroscopy. SORS equipment allows acquiring Raman spectra of substances through packaging, making it a non-invasive analytical technique. Note that the acquired spectrum is always the result of mixing two contributions: (1) the spectrum of the container material, and (2) the spectrum of the substance inside the container. Nowadays, SORS equipment are supported by software capable of removing the surface contribution from the recorded spectral data. However, the optimal extraction of this contribution is not achieved in all cases. This study explored the potential of two chemometric methods, Multivariate Curve Resolution (MCR) and Independent Components Analysis (ICA), for resolving the mixed spectra acquired by Vaya Raman equipment (Agilent) of four standard substances (sucrose, anhydrous sodium sulphate, ethanol 96% and glycerol) analyzed through containers of two different plastic materials (polypropylene and polyethylene terephthalate). The two resulting resolved spectra (by MCR and by ICA) and the one resolved by the equipment software were compared by similarity analysis with the Raman spectra of the standard substances available in recognized databases intended as target spectra. Similarity analysis was performed by calculating four similarity indexes, namely: cosine, arccosine, coefficient of determination and nearness index. Both MCR and ICA methods successfully extracted pure Raman spectra of the test substances more similar to the standard substances than the spectra resolved by the equipment software. These results highlighted the potential of both methods for resolving complex mixed signals, such as those acquired through the SORS technique, thereby enhancing its utility.

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.

Time domain diffuse Raman spectroscopy using single pixel detection.

Diffuse Raman spectroscopy (DIRS) extends the high chemical specificity of Raman scattering to in-depth investigation of thick biological tissues. We present here a novel approach for time-domain diffuse Raman spectroscopy (TD-DIRS) based on a single-pixel detector and a digital micromirror device (DMD) within an imaging spectrometer for wavelength encoding. This overcomes the intrinsic complexity and high cost of detection arrays with ps-resolving time capability. Unlike spatially offset Raman spectroscopy (SORS) or frequency offset Raman spectroscopy (FORS), TD-DIRS exploits the time-of-flight distribution of photons to probe the depth of the Raman signal at a single wavelength with a single source-detector separation. We validated the system using a bilayer tissue-bone mimicking phantom composed of a 1 cm thick slab of silicone overlaying a calcium carbonate specimen and demonstrated a high differentiation of the two Raman signals. We reconstructed the Raman spectra of the two layers, offering the potential for improved and quantitative material analysis. Using a bilayer phantom made of porcine muscle and calcium carbonate, we proved that our system can retrieve Raman peaks even in the presence of autofluorescence typical of biomedical tissues. Overall, our novel TD-DIRS setup proposes a cost-effective and high-performance approach for in-depth Raman spectroscopy in diffusive media.

Multi-optical parameter simulation of spatially offset Raman spectroscopy

Spatially Offset Raman Spectroscopy (SORS) has extensively been studied as a non-invasive technique that can detect the internal composition below the surface of turbid samples. A critical application of SORS is to identify unknown materials behind a highly scattering obstacle, such as plastic containers. The mechanism of SORS can be generally demonstrated as the photon migration theory; the Raman photons inside deeper samples are more likely to propagate laterally. However, a comprehensive view of the SORS mechanism still needs to be improved. In this work, a numerical simulation was carried out by the Monte Carlo algorithm to investigate the characteristics of SORS being affected by the various optical properties of materials, such as scattering coefficients, absorption coefficients, and refractive index. The key characteristics of SORS, such as detection depth, optimal offset, and signal intensity, were systematically analyzed in a monolayer medium and a bilayer medium. These findings provide important additional information on SORS theory and have important implications for the development of SORS as a reliable analytical tool in various fields, including biomedical research and environmental monitoring.

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.

SORS Performance of Sublayer Materials with Different Optical Properties under Diffuse Scattering Media

Over the past few years, the utilization of spatially offset Raman spectroscopy (SORS) has significantly evolved in its ability to analyze layered turbid materials non-invasively. It is well known that SORS can effectively detect the deeper layer in a high scattering media, and the performance characteristics of SORS have been extensively studied. However, to date, there is a lack of detailed studies of SORS to detect materials with different optical properties. This study aims to fill this gap by constructing a simple bilayer model, in which a target material with different optical properties was covered with a diffuse scattering barrier. By analyzing the Raman intensity from both superficial barriers and underlying target materials, we investigated the SORS performance to probe three typical optical materials with distinct optical properties: strong absorption, high transparency, and strong scattering. It was found that SORS technology can readily detect the samples of different properties under turbid surface coverings, and the typical optical property of the sublayer materials provided a specific SORS feature. Our study demonstrates the great potential of SORS technology for the non-invasive detection of subcutaneous component applications and provides a comprehensive understanding of the SORS characteristic of various materials.

Spatially Offset Raman Spectroscopy Combined with Attention-Based LSTM for Freshness Evaluation of Shrimp

Optical detection of the freshness of intact in-shell shrimps is a well-known difficult task due to shell occlusion and its signal interference. The spatially offset Raman spectroscopy (SORS) is a workable technical solution for identifying and extracting subsurface shrimp meat information by collecting Raman scattering images at different distances from the offset laser incidence point. However, the SORS technology still suffers from physical information loss, difficulties in determining the optimum offset distance, and human operational errors. Thus, this paper presents a shrimp freshness detection method using spatially offset Raman spectroscopy combined with a targeted attention-based long short-term memory network (attention-based LSTM). The proposed attention-based LSTM model uses the LSTM module to extract physical and chemical composition information of tissue, weight the output of each module by an attention mechanism, and come together as a fully connected (FC) module for feature fusion and storage dates prediction. Modeling predictions by collecting Raman scattering images of 100 shrimps within 7 days. The R2, RMSE, and RPD of the attention-based LSTM model achieved 0.93, 0.48, and 4.06, respectively, which is superior to the conventional machine learning algorithm with manual selection of the optimal spatially offset distance. This method of automatically extracting information from SORS data by Attention-based LSTM eliminates human error and enables fast and non-destructive quality inspection of in-shell shrimp.

Development of a spatially offset Raman spectroscopy probe for monitoring pharmaceutical drying

Spatially offset Raman spectroscopy (SORS) is a subset of Raman spectroscopy devised for probing subsurface compositions in non-homogenous media. An example of such media is the wet filter cake during pharmaceutical drying. This non-homogeneity poses a challenge for process monitoring as it could render the determined solvent content during drying, and the end point, inaccurate. In this study, a SORS probe was developed for the monitoring of pharmaceutical drying. The probe includes a 45° illumination point and 0–5 mm equidistant collection offsets. Using the SORS probe, solvent signal detection through variable thicknesses of dry paracetamol was examined. The solvent content during the drying of paracetamol in anisole was then monitored using the SORS probe. Partial least squares regression (PLSR) analysis was applied to evaluate the performance of offset configurations in monitoring the solvent content during drying. The results showed that the solvent signal is detected through thicknesses beyond 6 mm of paracetamol from the larger offsets of 4–5 mm. A more accurate prediction of the solvent content was obtained from larger offsets. PLSR models using offset spectra showed a decrease in estimation error up to 50 % compared to backscattering spectra, with a further decrease upon using standard normal variate pre-processing. This suggests that SORS could offer improved monitoring of pharmaceutical drying processes.

Evaluating performance of SORS-based subsurface signal separation methods using statistical replication Monte Carlo simulation

Spatially offset Raman spectroscopy (SORS) is a depth-profiling technique with deep information enhancement. However, the interference of the surface layer cannot be eliminated without prior information. The signal separation method is an effective candidate for reconstructing pure subsurface Raman spectra, and there is still a lack of evaluation means for the signal separation method. Therefore, a method based on line-scan SORS combined with improved statistical replication Monte Carlo (SRMC) simulation was proposed to evaluate the effectiveness of food subsurface signal separation method. Firstly, SRMC simulates the photon flux in the sample, generates a corresponding number of Raman photons at each voxel of interest, and collects them by external map scanning. Then, 5625 groups of mixed signals with different optical characteristic parameters were convoluted with spectra of public database and application measurement and introduced into signal separation methods. The effectiveness and application range of the method were evaluated by the similarity between the separated signals and the source Raman spectra. Finally, the simulation results were verified by three packaged foods. FastICA method can effectively separate Raman signals from subsurface layer of food and thus promote deep quality evaluation of food.

Packaged butter adulteration evaluation based on spatially offset Raman spectroscopy coupled with FastICA

Optical detection technology has been widely used in unpackaged food adulteration detection. However, due to the interference of packaging materials on the internal food optical signal, including signal occlusion, mixing and overlap precluded the accurate detection of internal food quality. In this study, a method of packaged butter adulteration evaluation based on spatially offset Raman spectroscopy (SORS) combined with fast independent component analysis (FastICA) was proposed. The adulterated butter from 0% to 100% w/w margarine at 10% intervals was covered with packaging sheets as test samples. A line-scan Raman hyperspectral imaging system was used to obtain a scattering spectral image of the packaged butter samples. The region of interest of the scattering image is extracted as the input of FastICA model to separate the internal butter signals. The extracted butter Raman features were input into four quantitative analysis models to assess the content of butter adulteration. The results showed that the ensemble model Extra-tree has the best performance with RMSEp, Rp2, and RPD values of 0.6, 0.93, and 4.73, respectively. Additionally, the applicability of the method was validated with four types of packaging materials. This rapid non-destructive testing method is beneficial to the effective testing method of packaged butter and other products industry.

Stratigraphy of Fresco Paintings: A New Approach with Photoacoustic and SORS Imaging

Photoacoustic (PA) imaging is a novel, powerful diagnostic technique utilized in different research fields. In particular, during recent years it has found several applications in Cultural Heritage (CH) diagnostics. PA imaging can be realized in transmittance or epi-illumination (reflectance) modes, obtaining variable levels of contrast and spatial resolution. In this work, we confirmed the applicability of the PA technique as a powerful tool for the imaging of one of the most challenging artwork objects, namely fresco wall paints, to obtain precise stratigraphic profiles in different layered fresco samples. In this regard, we studied some multi-layered fragments of the vault of San Giuseppe Church in Cagliari (1870 AD) and some mock-ups realized specifically to test the potentiality of this technique. Due to complex structures of the frescoes, we used the Spatially Off-set Raman Spectroscopy (SORS) technique to provide complementary information. The experimental results were in agreement for both techniques, even for the three-layered complex structure, and were confirmed with Scanning Electron Microscopy (SEM) analysis of cross-sections. The combined use of these two techniques proved useful to investigate detailed hidden information on the fresco samples.

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.

Towards non-invasive monitoring of non-melanoma skin cancer using spatially offset Raman spectroscopy.

BCC (basal cell carcinoma) and SCC (squamous cell carcinoma) account for the vast majority of cases of non-melanoma skin cancer (NMSC). The gold standard for the diagnosis remains biopsy, which, however, is an invasive and time-consuming procedure. In this study, we employed spatially offset Raman spectroscopy (SORS), a non-invasive approach, allowing the assessment of deeper skin tissue levels and collection of Raman photons with a bias towards the different layers of epidermis, where the non-melanoma cancers are initially formed and expand. Ex vivo Raman measurements were acquired from 22 skin biopsies using conventional back-scattering and a defocused modality (with and without a spatial offset). The spectral data were assessed against corresponding histopathological data to determine potential prognostic factors for lesion detection. The results revealed a positive correlation of protein and lipid content with the SCC and BCC types, respectively. By further correlating with patient data, multiple factor analysis (MFA) demonstrated a strong clustering of variables based on sex and age in all modalities. Specifically for the defocused modality (zero and 2 mm offset), further clustering occurred based on pathology. This study demonstrates the utility of the SORS technology in NMSC diagnosis prior to histopathological examination on the same tissue.

Spatially Offset Raman Spectroscopy for Characterization of a Solid-State System

Solid-state batteries represent a promising technology in the field of high-energy-density and safe storage systems. Improving the understanding of how defects form within these cells would greatly facilitate future development, which would be best served by applying nondestructive analytical tools capable of characterization of the key components and their changes during cycling and/or aging. Spatially offset Raman spectroscopy (SORS) represents a potentially useful technique, but currently there is a lack of knowledge regarding its use in this field. To fill this gap, we present an investigation into the use of simple defocused micro-SORS on systems constructed using typical components found within solid-state cells. By analyzing the constituents and the assembled system, it was possible to obtain depth profiling spectra and show that spectra may be obtained from layers which are normally obscured, demonstrating the technique’s potential for nondestructive chemical analysis of the subsurface. In this way, the results presented validate the potential of micro-SORS as a technique to develop to support future solid-state battery development, as well as the nondestructive battery analytical field.

The potential of the spatially offset Raman spectroscopy (SORS) for implementing rapid and non-invasive in-situ authentication methods of plastic-packaged commodity foods – Application to sliced cheeses

Key points for the authentication of foodstuffs are the verification of the stated labelling, as well as checking its nature and verifying the absence of fraud. Portable spatially offset Raman spectroscopy (SORS) allows the rapid and straightforward acquisition of Raman spectra of packaged foods without the package opening and can therefore be considered as a non-invasive analytical technique par excellence since it enables in-situ analytical controls even directly on the grocery and supermarket shelves. Cheese is a dairy product available on the market which can be classified depending mainly on two characteristics features: (i) the animal origin of the milk from which it is produced and (ii) the manufacturing processes. This study aims to authenticate plastic-packaged sliced cheeses according to the origin of the milk stated by animal species (cow, sheep and/or goat). The NEAR similarity index was previously applied to verify that cheeses manufactured from the same animal species milk have a high similarity (NEAR >0.8), higher than those found among cheeses produced from milk of different animal species. From the adquired spectra, the one input-class SIMCA approach was used to develop multivariate classification models in order to distinguish the origin of the milk by animal species. The performance parameters of the developed classification models showed values close to 0.9 for sensitivity and precision among others. The PLSR method was used to develop quantification models capable of predicting the total protein and fat contents (major nutrients) in cheese samples having errors of less than 2%. These advances open the door to the development of new rapid and non-invasive analytical methods for in-situ packaged food authentication, demonstrating the potential of SORS for food authentication supported by proper chemometrics.

Deep learning-enabled soft tissue tumor localization using spatially offset Raman spectral analysis: in-silico investigations

Raman spectroscopy and its derivatives have gained wide acceptance among optical biopsy tools for tissue discrimination. However, the identification and localization of subsurface soft tissue tumors are still challenging. Several designs for the Raman probe have been proposed to this effect, among which spatially offset Raman spectroscopy (SORS) could offer a potential solution. This paper attempts to demonstrate the simultaneous identification of subsurface adenoma depth and thickness using Convolutional Neural Networks applied on Monte Carlo simulated SORS signals. The application of transfer learning model resulted in a better root mean square error (RMSE) of 4.40% for depth prediction as compared to the 7%–25% RMSE demonstrated by previous reports. Simultaneous thickness prediction is demonstrated for the first time with 8.42% RMSE.

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.