Raman Spectral Region Research Articles

Adaptive Gap-Tunable Surface-Enhanced Raman Spectroscopy.

Gap plasmon (GP) resonance in static surface-enhanced Raman spectroscopy (SERS) structures is generally too narrow and not tunable. Here, we present an adaptive gap-tunable SERS device to selectively enhance and modulate different vibrational modes via active flexible Au nanogaps, with adaptive optical control. The tunability of GP resonance is up to ∼1200 cm-1 by engineering gap width, facilitated by mechanical bending of a polyethylene terephthalate substrate. We confirm that the tuned GP resonance selectively enhances different Raman spectral regions of the molecules. Additionally, we dynamically control the SERS intensity through the wavefront shaping of excitation beams. Furthermore, we demonstrate simulation results, exhibiting the mechanical and optical properties of a one-dimensional flexible nanogap and their advantage in high-speed biomedical sensing. Our work provides a unique approach for observing and controlling the enhanced chemical responses with dynamic tunability.

Dynamic in situ microstructure evaluation of cheese melting: Understanding structure-rheological relationships under controlled temperature sweeps

This study assessed the changes in processed and natural cheese during heating from 5 to 95 °C, at 2 °C per min, using confocal laser scanning microscopy (CLSM), Raman micro-spectroscopy and rheological analysis. The CLSM images showed that the cheese samples varied in fat droplet size and distribution. CLSM micrographs of processed cheese with lower fat content (<10%) showed no substantial structural changes during heating, which were consistent with the absence of cross-over in temperature rheology sweep curves. Cheese containing starch showed uniformly distributed fat and cohesive protein network, leading to reduced cheese flow. At temperatures > 70 °C, cheeses with a fat content ≥ 25% showed greater coalescence of fat globules, which finally merged into large fat pools. The results of this study demonstrate the potential of dynamic in situ CLSM and rheological analysis for evaluating the changes in cheese matrices during heating. The Raman spectral region corresponding to C-H stretching vibrations (2800–3000 cm-1) could be used to track fat melting in cheese samples with minimum 10% fat.

Decoding PFAS contamination via Raman spectroscopy: A combined DFT and machine learning investigation

In this study, density function theory (DFT) is employed to compute Raman spectra of 40 important Perfluoroalkyl substances (PFASs) as listed in Draft Method 1633 by U.S. Environmental Protection Agent. A systematic comparison of their spectral features is conducted, and Raman peaks and vibrational modes are identified. The Raman spectral regions for the main chemical bonds (such as C-C, CF2 & CF3, O-H) and main functional groups (such as -COOH, -SO3H, -C2H4SO3H, and -SO2NH2) are identified and compared. The impacts of branching location in isomer, molecular chain length, and functional groups on the Raman spectra are analyzed. Particularly, the isomers of PFOA alter the peak locations slightly in wavenumber regions of 200 – 800 and 1000 – 1400 cm−1, while for PFOS, spectral features in the 230 – 360, 470 – 680, and 1030 – 1290 cm−1 regions exhibit significant difference. The carbon chain length can significantly increase the number of Raman peaks, while different functional groups give significantly different peak locations. To facilitate differentiation, a spectral database is constructed by introducing controlled noise into the DFT-computed Raman spectra. Subsequently, two chemometric techniques, principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), are applied to effectively distinguish among these spectra, both for 40 PFAS compounds and the isomers. The findings demonstrate the promising potential of combining Raman spectroscopy with advanced spectral analysis methods to discriminate between distinct PFAS compounds, holding significant implications for improved PFAS detection and characterization methodologies.

In Situ Monitoring of Drug Precipitation from Digesting Lipid Formulations Using Low-Frequency Raman Scattering Spectroscopy.

Low-frequency Raman spectroscopy (LFRS) is a valuable tool to detect the solid state of amorphous and crystalline drugs in solid dosage forms and the transformation of drugs between different polymorphic forms. It has also been applied to track the solubilisation of solid drugs as suspensions in milk and infant formula during in vitro digestion. This study reports the use of LFRS as an approach to probe drug precipitation from a lipid-based drug delivery system (medium-chain self-nanoemulsifying drug delivery system, MC-SNEDDS) during in vitro digestion. Upon lipolysis of the digestible components in MC-SNEDDS containing fenofibrate as a model drug, sharp phonon peaks appeared at the low-frequency Raman spectral region (<200 cm-1), indicating the precipitation of fenofibrate in a crystalline form from the formulation. Two multivariate data analysis approaches (principal component analysis and partial least squares discriminant analysis) and one univariate analysis approach (band ratios) were explored to track these spectral changes over time. The low-frequency Raman data produces results in good agreement with in situ small angle X-ray scattering (SAXS) measurements with all data analysis approaches used, whereas the mid-frequency Raman requires the use of PLS-DA to gain similar results. This suggests that LFRS can be used as a complementary, and potentially more accessible, technique to SAXS to determine the kinetics of drug precipitation from lipid-based formulations.

Simultaneous Dual-Band Hyperspectral Stimulated Raman Scattering Microscopy with Femtosecond Optical Parametric Oscillators.

Stimulated Raman scattering (SRS) microscopy is a label-free quantitative optical technique for imaging molecular distributions in cells and tissues by probing their intrinsic vibrational frequencies. Despite its usefulness, existing SRS imaging techniques have limited spectral coverage due to either a wavelength tuning constraint or narrow spectral bandwidth. High-wavenumber SRS imaging is commonly used to map lipid and protein distribution in biological cells and visualize cell morphology. However, to detect small molecules or Raman tags, imaging in the fingerprint region or "silent" region, respectively, is often required. For many applications, it is desirable to collect SRS images in two Raman spectral regions simultaneously for visualizing the distribution of specific molecules in cellular compartments or providing accurate ratiometric analysis. In this work, we present an SRS microscopy system using three beams generated by a femtosecond oscillator to acquire hyperspectral SRS image stacks in two arbitrary vibrational frequency bands, between 650-3280 cm-1, simultaneously. We demonstrate potential biomedical applications of the system in investigating fatty acid metabolism, cellular drug uptake and accumulation, and lipid unsaturation level in tissues. We also show that the dual-band hyperspectral SRS imaging system can be adapted for the broadband fingerprint region hyperspectral imaging (1100-1800 cm-1) by simply adding a modulator.

P263 Raman spectroscopy analysis of saliva combined with an artificial neural network algorithm could discriminate between Ulcerative Colitis and Crohn’s disease

Abstract Background Early and accurate detection of inflammatory bowel disease (IBD) is crucial for proper intervention.The IBD diagnostic process can be both time-consuming and invasive and lacks a gold standard in disease differentiation. Saliva analysis has gained interest as a potential non-invasive source of disease biomarkers due to its positive correlation with blood-based constituents (e.g. interleukins) at a molecular level. We systematically studied the molecular fingerprint of saliva and tissue biopsy, including how they compare as well as changes associated with disease state-IBD vs non-IBD and Ulcerative colitis (UC) vs Crohn’s disease (CD). Methods After obtaining informed consent Saliva and colon biopsies were collected from patients undergoing endoscopy as a standard of care procedure at University Hospitals Birmingham, NHS Foundation Trust, Birmingham, UK. The samples were correlated with their tissue pathology markers and findings of the endoscopic evaluation. By combining Raman spectroscopy and an artificial neural network algorithm, Self-Optimising Kohonen Index Network (SKiNET), we have developed a non-destructive molecular profiling tool that assesses both salivary and colonic biopsy changes, which helps to determine the diseased state of the sample source. The collected Raman spectra were analysed using SKiNET as a decision support tool to identify spectral markers and discriminate between healthy and IBD disease classes [1]. Results This pilot study characterised the spectral signatures of 101 saliva samples (healthy = 50, UC = 27 and CD =24) and 44 tissue biopsy samples (healthy = 12, UC = 17 and CD = 15). The algorithm reached 87.5% sensitivity and 89% specificity when classifying saliva and 83.9% sensitivity and 84.6% specificity when classifying tissue biopsy. When comparing the spectral fingerprints of saliva to tissue biopsy, the UC spectra are of similar forms with comparable intensities at each of the primary peaks; 1003 cm1(phenylalanine), 1440 cm-1 (Lipids) and 1656 cm-1 (Amide I). In contrast, CD saliva and tissue biopsy indicate significant variance throughout the Raman spectral fingerprint region, with intensity changes observed at each of the primary peaks and the presence of additional shoulder peaks associated with proteins and lipids. In identifying markers of disease IL-4, IL-8 and IL-10 were identified as having important and designatory Raman bands which could serve in the discrimination of IBD in individuals. Conclusion This unique combination of Raman spectroscopy and advanced machine learning represents significant progress towards improved, non-invasive and rapid classification of IBD.This technique also lays the foundation for future applications in disease diagnosis, monitoring and therapeutics.

Understanding Raman Spectral Based Classifications with Convolutional Neural Networks Using Practical Examples of Fungal Spores and Carotenoid-Pigmented Microorganisms

Numerous publications showing that robust prediction models for microorganisms based on Raman micro-spectroscopy in combination with chemometric methods are feasible, often with very precise predictions. Advances in machine learning and easier accessibility to software make it increasingly easy for users to generate predictive models from complex data. However, the question regarding why those predictions are so accurate receives much less attention. In our work, we use Raman spectroscopic data of fungal spores and carotenoid-containing microorganisms to show that it is often not the position of the peaks or the subtle differences in the band ratios of the spectra, due to small differences in the chemical composition of the organisms, that allow accurate classification. Rather, it can be characteristic effects on the baselines of Raman spectra in biochemically similar microorganisms that can be enhanced by certain data pretreatment methods or even neutral-looking spectral regions can be of great importance for a convolutional neural network. Using a method called Gradient-weighted Class Activation Mapping, we attempt to peer into the black box of convolutional neural networks in microbiological applications and show which Raman spectral regions are responsible for accurate classification.

Dual phenomenon of surface plasmon and laser optics induced EM enhancement for rapid DNA detection with semiconductor nanostructures (SiNWs/AgNPs)

Recently exploring bioanalytical instrumentation principles emphasized with combination of optics and EM fields in surface enhanced Raman spectroscopy (SERS). The principles favour the rapid detection of DNA with proper fabrication of SERS sensor. 1D semiconductor nanostructures (SiNWs) functionalization with plasmonic silver nanoparticles are fabricated by PLD process and exploited for SERS detection of DNA. The DNA nucleotide bands are indexed with reference to their Raman shift as Adenine (A), Guanine (G), Cytosine (C), Thymine (T) at Raman spectral regions of 1352 cm-1, 676 cm-1, 1625 cm-1, 1441 cm-1 respectively. The finest conditions for sensitive detection of DNA were investigated and their interaction mechanism are presented. The point-to-point reproducibility measurements imprinted higher degree of concordancy. The designed SERS sensor will be definitely helpful almost in all application fields with portable handheld Raman spectrophotometer in near future.

Identification of structural inhomogeneity on spray pyrolyzed Cu2ZnSnS4 thin film using micro-Raman spectroscopy

The study on the structural inhomogeneity of Cu2ZnSnS4 (CZTS) thin film is imperative to enhance the absorber layer performance in photovoltaic devices (PV). Herein, we have examined the structural inhomogeneity of spray pyrolyzed CZTS thin film using micro-Raman spectroscopy. The shift of major A-mode i.e., 337 cm−1, 334 cm−1 and 332 cm−1, in Raman spectra recorded at different spots, corroborated the presence of ordered and disordered kesterite structure. The observed shift of A-mode in different spots is ascribed to phase segregation and local inhomogeneity that existed on the surface of CZTS film. The respective distribution of the different acquired Raman spectral regions has been presented using the Raman mapping. Scanning electron microscopy was employed to confirm the phase segregation in the surface of CZTS film. The attained composition ratio of CZTS film i.e., Cu/(Zn+Sn)=0.89 and Zn/Sn=1.49, in the elemental analysis are expedient.

A Fingerprint in a Fingerprint: A Raman Spectral Analysis of Pharmaceutical Ingredients

The fingerprint region in mid-infrared (IR) and Raman spectroscopy, typically between 300 to 1900 cm-1, is used to characterize molecules by utilizing their vibrational and rotational changes in this region. In this study, a smaller subset of this Raman spectral region was evaluated for its significance in characterizing pharmaceutical products. This narrower region from 1550 to 1900 cm-1 in the Raman spectrum will further be defined as the “fingerprint in the fingerprint” region. A spectral evaluation was conducted on active pharmaceutical ingredients (APIs) in prescription drugs, over-the-counter drugs, and pharmaceutical drug products. Qualitative and quantitative analysis was performed on these products to gain an understanding of this spectral region. The qualitative results showed no Raman spectral signals in this region for any of the common excipients. Additionally, all APIs evaluated had unique Raman vibrations and spectral signals in this region. This data indicates that the 1550–1900 cm-1 Raman spectral region is ideal for API identity testing. Furthermore, the data indicate the potential of this spectral region for differentiation and classification of API content in different strengths of a given drug product.

Ultrafast impulsive Raman spectroscopy across the terahertz–fingerprint region

Broadband Raman spectroscopy (detection bandwidth >1000 cm − 1) is a valuable and widely used tool for understanding samples via label-free measurements of their molecular vibrations. Two important Raman spectral regions are the chemically specific “fingerprint” (200 to 1800 cm − 1) and “low-frequency” or “terahertz” (THz) (<200 cm − 1; <6 THz) regions, which mostly contain intramolecular and intermolecular vibrations, respectively. These two regions are highly complementary; broadband simultaneous measurement of both regions can provide a big picture comprising information about molecular structures and interactions. Although techniques for acquiring broadband Raman spectra covering both regions have been demonstrated, these methods tend to have spectral acquisition rates <10 spectra / s, prohibiting high-speed applications, such as Raman imaging or vibrational detection of transient phenomena. Here, we demonstrate a single-laser method for ultrafast (24,000 spectra / s) broadband Raman spectroscopy covering both THz and fingerprint regions. This is achieved by simultaneous detection of Sagnac-enhanced impulsive stimulated Raman scattering (SE-ISRS; THz-sensitive) and Fourier-transform coherent anti-Stokes Raman scattering (FT-CARS; fingerprint-sensitive). With dual-detection impulsive vibrational spectroscopy, the SE-ISRS signal shows a >500 × enhancement of <6.5 THz sensitivity compared with that of FT-CARS, and the FT-CARS signal shows a >10 × enhancement of fingerprint sensitivity above 1000 cm − 1 compared with that of SE-ISRS.

Calibration of the SHERLOC Deep Ultraviolet Fluorescence-Raman Spectrometer on the Perseverance Rover.

We describe the wavelength calibration of the spectrometer for the scanning of habitable environments with Raman and luminescence for organics and chemicals (SHERLOC) instrument onboard NASA's Perseverance Rover. SHERLOC utilizes deep ultraviolet Raman and fluorescence (DUV R/F) spectroscopy to enable analysis of samples from the Martian surface. SHERLOC employs a 248.6 nm deep ultraviolet laser to generate Raman-scattered photons and native fluorescence emission photons from near-surface material to detect and classify chemical and mineralogical compositions. The collected photons are focused on a charge-coupled device and the data are returned to Earth for analysis. The compact DUV R/F spectrometer has a spectral range from 249.9 nm to 353.6 nm (∼200 cm-1 to 12 000 cm-1) (with a spectral resolution of 0.296 nm (∼40 cm-1)). The compact spectrometer uses a custom design to project a high-resolution Raman spectrum and a low-resolution fluorescence spectrum on a single charge-coupled device. The natural spectral separation enabled by deep ultraviolet excitation enables wavelength separation of the Raman/fluorescence spectra. The SHERLOC spectrometer was designed to optimize the resolution of the Raman spectral region and the wavelength range of the fluorescence region. The resulting illumination on the charge-coupled device is curved, requiring a segmented, nonlinear wavelength calibration in order to understand the mineralogy and chemistry of Martian materials.

Raman spectroscopy for the discrimination and quantification of fuel blends

AbstractBiodiesel is an alternative energy source to replace fossil fuels and reduce the environmental pollution. Adding biodiesel in fossil diesel can increase the oxygen content (from fatty acid) and promote fuel to be burned more quickly and thoroughly. However, the biodiesel content criterion of different countries was diverse from each other. In this study, Raman spectroscopy was used as a tool in classifying fuel blends and quantifying biodiesel contents. For classifying the fuel blends, principal component analysis (PCA) method was employed, where 87.22% of spectral variation was characterized by the first two components PCA scores shows a clear discrimination between the pure fuels and mixture fuels. Meanwhile, for identifying and quantifying the blends of diesel and biodiesel, Raman spectroscopy analysis based on partial least squares (PLS) regression was conducted. Biodiesel mainly present three characteristic Raman regions corresponding to the spectroscopy of diesel. The C─H Raman region presents the better quantitative capacity than the C═C and C═O spectral regions. And the PLS regression built from C─H Raman spectral region in quantifying biodiesel contents presents a higher correlation coefficient and lower root mean square error for prediction. Furthermore, employing only C─H Raman region coupled with PLS regression for predicting concentration of biodiesel can reduce an order of magnitude of root mean square error compared with using three characteristic Raman spectral regions together. Our result show that Raman spectroscopy combined with PCA and PLS can identify fuels and biofuels for discrimination and quantitation.

4-Nonylphenol effects on rat testis and sertoli cells determined by spectrochemical techniques coupled with chemometric analysis

Herein, vibrational spectroscopy has been applied for qualitative identification of biomolecular alterations that occur in cells and tissues following chemical treatment. Towards this end, we combined attenuated total reflection Fourier-transform infrared (ATR-FTIR) and Raman spectroscopy to assess testicular toxicology after 4-nonylphenol (NP) exposure, an estrogenic endocrine disruptor affecting testicular function in rats and other species. Rats aged 21, 35 or 50 days received NP at intra-peritoneal doses of 0, 25, 50 or 100 mg/kg for 20 consecutive days. Primary Sertoli cells (SCs) were treated with NP at various concentrations (0, 2.5, 5, 10 or 20 μM) for 12 h. Post-exposure, testicular cells, interstitial tissue and SCs were interrogated respectively using spectrochemical techniques coupled with multivariate analysis. Distinct biomolecular segregation between the NP-exposed samples vs. control were observed based on infrared (IR) spectral regions of 3200–2800 cm−1 and 1800-900 cm−1, and the Raman spectral region of 1800–900 cm−1. For in vivo experiments, the main wavenumbers responsible for segregation varied significantly among the three age classes. The main IR and Raman band differences between NP-exposed and control groups were observed for Amide (proteins), lipids and DNA/RNA. An interesting finding was that the peptide aggregation level, Amide Ӏ-to-Amide II ratio, and phosphate-to-carbohydrate ratio were considerably reduced in ex vivo NP-exposed testicular cells or SCs in vitro. This study demonstrates that ATR-FTIR and Raman spectroscopy techniques can be applied towards analysing NP-induced testicular biomolecular alterations.

Multivariate analysis of Raman spectra of carbonaceous black drawing media for the in situ identification of historic artist materials

AbstractThe identification of black drawing materials in works of art on paper is often important for their conservation and crucial to gain insight about artists' methodologies and the historic use of drawing materials. Traditionally, conservators and curators have had to rely upon visual observation with multiple wavelengths of light and magnification to characterize the materials in drawings, a challenging task further complicated if the media have been manipulated by the artist. The use of carbon‐based pigments in black drawing materials, ranging from charcoals and chalks to pastels and crayons, can be verified in situ using Raman microspectroscopy based on the presence of the two broad spectral bands characteristic of carbonaceous materials. However, the subtle differences in these spectral features between pigments can be difficult to discern, especially in cases with high background fluorescence. Here, we utilized principal component analysis to distinguish a group of 14 reference black drawing materials, both contemporary and historic, in the Raman spectral region of 800–2,000 cm−1. The model generated successfully differentiates between charcoal, chalk, and fabricated stick media, with sensitivity to both the Raman bands of carbon‐based materials as well as the fluorescent background profile. We validated the model using Raman spectra of black media on 19th‐century French drawings from the J. Paul Getty Museum collection that were previously classified visually by microscopic evaluation.

Contribution of High-Pressure-Induced Protein Modifications to the Microenvironment and Functional Properties of Rabbit Meat Sausages.

Rabbit meat batters were subjected to high pressure (HP, 100 to 300 MPa for 3, 9, or 15 min) to elucidate their effects on proteins structures, the microenvironment, and the resulting functionalities of the subsequently heated products. To determine these effects, we investigated structural and microenvironmental changes using Raman spectroscopy and also expressible moisture content, textural characteristics, and dynamic rheological properties of batters during heating (20 to 80 °C). Untreated samples served as controls. Analysis of specific Raman spectral regions demonstrated that applications of HP to rabbit meat batters tended to induce the transformation of the all-gauche S-S conformation to gauche-gauche-trans in the batter system. HP treatment higher than 100 MPa for 9 min promoted secondary structural rearrangements, and molecular polarity enhancement in the proteins prior to cooking. Also, increases of O-H stretching intensities of rabbit meat sausages were obtained by HP treatment, denoting the strengthening of water-holding capacity. These HP-induced alterations resulted in improved texture and, perhaps, improved juiciness of rabbit meat sausages (P < 0.05), however they had relatively poorer rheological properties than the controls. Nevertheless, HP treatment, especially 200 MPa for 9 or 15 min, was an effective technique for improving the functionalities of gel-type products through modification of meat proteins.

Ultraviolet Raman Wide-Field Hyperspectral Imaging Spectrometer for Standoff Trace Explosive Detection.

We constructed the first deep ultraviolet (UV) Raman standoff wide-field imaging spectrometer. Our novel deep UV imaging spectrometer utilizes a photonic crystal to select Raman spectral regions for detection. The photonic crystal is composed of highly charged, monodisperse 35.5 ± 2.9 nm silica nanoparticles that self-assemble in solution to produce a face centered cubic crystalline colloidal array that Bragg diffracts a narrow ∼1.0 nm full width at half-maximum (FWHM) UV spectral region. We utilize this photonic crystal to select and image two different spectral regions containing resonance Raman bands of pentaerythritol tetranitrate (PETN) and NH4NO3 (AN). These two deep UV Raman spectral regions diffracted were selected by angle tuning the photonic crystal. We utilized this imaging spectrometer to measure 229 nm excited UV Raman images containing ∼10-1000 µg/cm2 samples of solid PETN and AN on aluminum surfaces at 2.3 m standoff distances. We estimate detection limits of ∼1 µg/cm2 for PETN and AN films under these experimental conditions.

Structural and thermal stability of graphene oxide-silica nanoparticles nanocomposites

The investigation of the thermal stability up to 400 °C of Graphene Oxide (GO) and GO-silica nanoparticles (n-SiO2) composites prepared by direct mixture of GO and n-SiO2 is reported. Using Scanning Electron Microscopy, X-ray Photoelectron Spectroscopy, Energy Dispersive X-ray analysis, Atomic Force Microscopy, Raman and Infrared absorption measurements a thorough characterization of the prepared materials is carried out. By deepening the changes induced in the 2D Raman spectral region of GO at about 2900 cm−1 the comprehension of an interplay, driven by the thermally induced changes of the material, between inter-valley and intra-valley vibrational transitions is elucidated. This result enables to find out that the major thermally induced changes are related to the GO counterpart of the nanocomposite and shows that the deposited silica nanoparticles are not interfering with the GO principal features exploiting the potentiality of nanocomposites preparation. Furthermore, silica nanoparticles are found to act as solid exfoliant agents, thus inhibiting GO lamellae re-aggregation even at high temperatures.

Raman spectroscopy, X-ray diffraction and electrical properties of fullerene-based poly (methylmethacrylate) polymers

Fullerene grafted poly (methylmethacrylate) polymers have been studied using X-ray diffraction and raman spectroscopy to analyze their polymeric structure. The electrical properties of prepared polymers have also been studied. The X-ray diffraction and raman spectroscopy pattern of pure buckminster fullerene ( C 60) has been compared with prepared polymeric samples. Raman spectroscopy indicates that a structural change occurs due to grafting. The vibrations in the relative intensity of peaks and shifts in energy are observed. The lower scattering efficiency of pure fullerene ( C 60) is observed at 1468 cm-1.The values for poly (methylmethacrylate) are observed at 3000 cm-1. The result show a large shift in the intense raman spectral region of fullerene-based poly (methylmethacrylate) which lies between 2500 cm-1 and 3000 cm-1. X-ray diffraction shows an unusual variation in crystallite size of polymeric samples. Generally it has been observed that polymers are insulators, but noticeably fullerene-based poly (methylmethacrylate) show conducting behavior.

手持式拉曼光谱仪探头系统的杂光抑制新方法

Handheld Raman spectrometer probe system shares the same optical configuration for illumination and light receiving. Thus, stray light caused by multiple reflections of laser, expect for Rayleigh scattering, is a problem to be solved for this kind of probe system. By real ray tracing method, setting up reasonable stray light analysis model and theoretical calculation, we get the total stray light information, together with our actual prototype structural data, such as the size and location of focus, emergent direction, energy in the slit and so on. Then a new approach of stray light suppression for spectrometer probe system that uses black board to block the stray light is proposed. The size and location of the black board are determined according to the analysis of the stray light. The optical simulation analysis results show that the black board suppresses stray light to the level of 10-13 in Raman spectral region. This satisfies the requirements of the handheld Raman spectrometer. With black board method, almost 50% of the laser reflection with incidence angle larger than 2 on the notch filter can be filtered.