This data set contains the spectral data associated with the collection of EC-SERS spectra using mainly a nontargeted drug identification approach, with several samples using a targeted fentanyl identification approach. The data set contains the replicate measurements and averaged Raman spectra used in the characterization of the analytes (drugs of abuse and adulterant compounds) to allow for forensic library formation. The data set also contains spectra of analytes collected at varying concentrations and additional fentanyl analog data collected using a targeted method.

Raman spectroscopy is a rapid, non-invasive, and non-destructive method, featuring high chemical specificity for different biological materials and low sensitivity to water. This makes it ideal for natural medicines, as it offers a relatively objective and comprehensive characterization for their complicated material basis. Therefore, Raman spectroscopy plays a crucial role in the identification of medicinal properties, authentication of authenticity, and quality control of TCMs. At present, TCMRSD stands as the only downloadable, comprehensive Raman spectral database for TCMs, which encompasses spectra of 327 Chinese medicines collected through rigorous methodological validation. The selection of TCMs for database development is based on the considerations of diversity of medicines, medicinal importance and variety of medicinal properties, in order to guarantee a comprehensive range and representation of substances used in Chinese medicine.

Biosignature detection is one of the most important goals in Mars missions. Since the Curiosity mission, the laser-induced breakdown spectrometer (LIBS) becomes an essential payload due to its convenience and versatility in profiling elemental chemistry. To test whether LIBS alone could filter potential biosignatures, a clastic quartz stone collected from a Mars analog setting, the western Qaidam Basin, was selected for LIBS analysis. Raman spectroscopy was used as an indicator of organic signals to support the presence of potential hypolithic communities and the dearth of epilithic biomass on the rock. A total of 344 LIBS spectra were determined and statistically analyzed using principal component analysis (PCA). Our results indicate that, with a sufficient sample size, PCA analysis can partially differentiate biotic and abiotic signals based on LIBS measures. This finding is significant since it indicates that multivariate analysis of LIBS data can be useful for biosignatures filtering on Mars exploration. Methods Located on “the roof of the world” Tibetan Plateau, the western Qaidam Basin is a cold, dry, and irradiative environment that shapes itself with landforms (e.g., dunes, yardangs, playas, wind streaks, polygonal terrains, and gullies) commonly found on Mars. A clastic quartz stone was sampled from a Cenozoic gravel deposit (38°35′44″ N, 90°59′6″ E, 3245.17 m altitude) from the hyperarid Dalangtan Playa, western Qaidam Basin, on 29 July 2021. The Cenozoic gravel deposit was likely derived from the weathering of Mesozoic (Pre-Jurassic and Jurassic) rocks, and quartz stones were common in the deposit. Visible light greenish color could be observed at the bottom of the quartz stone. Multiple spots of four vertical lines (11 spots for line 1 Qz-l1, 6 spots for line 2 Qz-l2, 9 spots for line 3 Qz-l3, and 8 spots for line 4 Qz-l4) of the Qaidam quartz stone were selected to stereoscopically investigate the spatial distribution of Raman spectra-based mineralogical or organic/biotic signals. An alpha 300R confocal Raman imaging system (WITec, Germany) incorporated with a 50x objective lens of numerical aperture = 0.55 and an excitation laser source of 532 nm was used for Raman spectral measurements. The laser wavelength was corrected using the Raman peak of a Si wafer. All spectra were acquired in a spectral range of 0-4000 cm−1 with a spectral resolution of 4.8 cm-1. To retain the resolutions of both minerals and organic matter as much as possible, laser power was kept at 3.1 mW for an integration time of 3 s with the number of accumulations of 30. To understand the elemental compositions and spectral features of chosen samples, the SciAps Z-300 Handheld LIBS Analyzer (SciAps Inc, Woburn, MA, USA) was employed for LIBS analysis (excitation source: 5-6 mJ·pulse-1, 50 Hz repetition rate, 1064 nm laser source, argon purge). Z-300 LIBS Analyzer measured the signal intensity every 0.0333 nm from 200 to 900 nm. LIBS Analyzer was equilibrated with an internal standard prior to determining the peak patterns of respective target samples. LIBS was employed to construct a pseudo-three-dimensional geochemical profile of spots on the four vertical lines identical to Raman spectroscopic measurements. The LIBS spectrum of each spot was generated by the LIBS Analyzer in quadruplicate. In addition, 140 spots from 9 oval outlines of the top, side, and bottom faces of the Qaidam quartz were measured using LIBS: 8 spots of inner circle, 16 spots of middle circle, and 15 spots of outer circle on the top face; 16 spots of upper circle, 23 spots of middle circle, and 23 spots of lower circle on the side face; and 8 spots of inner circle, 12 spots of middle circle, and 19 spots of outer circle on the bottom face. Moreover, 68 random spots (20 from the top, 28 from the side, and 20 from the bottom) of the Qaidam quartz were chosen for singlicate LIBS measurements and the downstream statistical analysis.

Raman spectra of solidified agarose and Cy 7.5-infused agarose samples

Collection of curated Raman spectra of art-related materials relevant to heritage and conservation science, part of the INFRA-ART Spectral Library.

The development of uniform, consistent spectroscopic databases of Raman spectra is important for the community to maximize the value of emerging machine learning techniques. This dataset contains processed and augmented Raman spectra acquired on a variety of common plastics, with variations in manufacturer and properties such as plastic color. The Raman spectra span the frequency window from 300 to 3900 cm-1, were collected using variations in instrumentation settings, were interpolated to 1 cm-1 wavenumber spacing to ensure compatibility, and were augmented 5X by random scaling and artificial noise introduction. Three different versions of the data are provided, each enabling exploration of a different strategy for training machine learning classification models. This data was used to train microplastic classification models using K-nearest neighbor algorithm of the sklearn package in python, as published in the associated manuscript. Python pickle files are included in the dataset, which contain the optimized models and supporting information for the models. The data are being posted in support of this research. The data was created by the authors.

Included in this data release are eight files: one metadata file, 6 comma separated value (.csv) datafiles and one data dictionary file defining the entities and attributes that constitute this dataset. The 6 datafiles support the publication, Deep syntectonic burial of the Anthracite belt, Eastern Pennsylvania, by providing in csv format the data from Figure 11 – Representative Raman spectra for selected individual CH4 ± CO2 fluid inclusions, Figure 13 – Representative Raman spectra pairs for selected individual High-ThA fluid inclusions, Figure 15 – Representative Raman spectra pairs for selected individual Low-ThA fluid inclusions, Table 1 – Comparison of composition by Raman and microthermometry for Single-Phase inclusions, Table 2 – Comparison of inclusion density by Raman and microthermometry for single-phase Inclusions, and Table 3 – Two-phase inclusion vapor bubble composition determined by Raman spectroscopy. These files contain Raman data from fluid inclusions contained in rocks from the Anthracite belt region, Pennsylvania.

Data contain Raman spectra, calibration and data alalysis results for all experiments conducted in the manuscript

A mass spectral database for organic compounds. The spectra included in the database are: electron impact Mass spectrum (EI-MS), Fourier transform infrared spectrum (FT-IR), 1H nuclear magnetic resonance (NMR) spectrum, 13C NMR spectrum, laser Raman spectrum, and electron spin resonance (ESR) spectrum.

The minerals used in this study were supplied by the Australian Museum (ASM). The minerals have been characterized by both X-ray diffraction (XRD) and by chemical analysis using ICP-AES (inductively coupled plasma atomic emission spectroscopy) techniques.

The following samples were used: (a) sample ASM-D49056 boléite from the Amelia Mine, Santa Rosalia, Baja, California, Mexico; (b) sample ASM-D 27575 cumengéite, Beleo, Baja California, Mexico; (c) sample ASM D36845 diaboléite from Mannoth mine, Tiger, Arizona, USA; and (d) sample ASM D191881 phosgenite from Consols mine, Broken Hill, South Australia.

Crystals of the minerals were placed and orientated on a polished metal surface on the stage of an Olympus BHSM microscope, which is equipped with 10 × and 50 × objectives. The microscope is part of a Renishaw 1000 Raman microscope system, which also includes a monochromator, a filter system and a Charge Coupled Device (CCD). Raman spectra were excited by a Spectra-Physics model 127 He-Ne laser (633 nm) at a resolution of 2 cm−1 in the range between 100 and 4000 cm−1. Repeated acquisition using the highest magnification was accumulated to improve the signal to noise ratio in the spectra. Spectra were calibrated using the 520.5 cm−1 line of a silicon wafer.

Infrared (IR) spectra were obtained using a Nicolet Nexus 870 FTIR spectrometer with a smart endurance single bounce diamond ATR cell. Spectra over the 4000 to 525 cm−1 range were obtained by the co-addition of 64 scans with a resolution of 4 cm−1 and a mirror velocity of 0.6329 cm/s.

Spectroscopic manipulation such as baseline adjustment, smoothing and normalization were performed using the Spectracalc software package GRAMS (Galactic Industries Corporation, New Hampshire, USA). Band component analysis was undertaken using the Jandel ‘Peakfit’ software package, which enabled the type of fitting function to be selected and allows specific parameters to be fixed or varied accordingly. Band fitting was done using a Gauss-Lorentz cross-product function with the minimum number of component bands used for the fitting process. The Gauss-Lorentz ratio was maintained at values >0.7 and fitting was undertaken until reproducible results were obtained with squared correlations of r2 >0.995.

Figure 1 is Raman spectra of the hydroxyl-stretching region of (a) phosgenite, (b) boléite, (c) diaboléite and (d) cumengéite. Figure 2 shows band component analysis of the hydroxyl-stretching region of the Raman spectrum of (a) diaboléite and (b) cumengéite. Figure 3 is Raman spectra of the 600–1000 cm−1 region of (a) boléite, (b) diaboléite and (c) cumengéite. Figure 4 is Raman spectra of the carbonate region of phosgenite. Figure 5 is Raman spectra of the 100–500 cm−1 region of (a) phosgenite, (b) boléite, (c) diaboléite and (d) cumengéite.

Precompiled and ready to go databases from the RRUFF, SLOPP/E, and WURM public databases fro Raman spectra. These datasets are already formatted for the RamanLab software.

Download them, and put them in the top level of the RamanLab directory.

Background: The NHS spends £4.3 billion annually to address musculoskeletal conditions, encompassing age-related bone disorders like osteoarthritis and osteoporosis. Traditional X-ray diagnostic methods are commonly employed for bone disorder diagnosis, primarily assessing gross anatomical bone structure changes. However, these methods are unable to identify subtle biochemical alterations within the bone. More detailed information, particularly about protein changes, may lead to enhanced diagnostics and treatment. Raman spectroscopy is a non-invasive, laser-based technique capable of detecting changes in the collagen component of bone. Despite its long-standing application in discerning mineral and protein changes within bone, there is limited evidence on Raman spectral signatures of purified human collagens and their differentiation. This study aimed to test the hypothesis that Raman spectroscopy could detect different types of collagen in the human body. Results: A Raman microspectrometer with a 785nm laser was used to measure unmineralized human collagens types I – VI and collagenous extracellular matrix (ECM) secreted by MG63 osteoblast-like cells. The results demonstrated the efficacy of Raman spectroscopy and subsequent multivariate analysis in distinguishing human collagen types I – VI. This implies that Raman spectroscopy, coupled with multivariate analysis, can identify pure human collagens and offers reference spectra similar to natural human collagen in the bone extracellular matrix. Significance: This study establishes Raman spectroscopy as a tool for identifying and characterizing human collagens, aiding in the diagnosis of connective tissue disorders. The creation of a spectral reference library for pure human collagen types I – VI holds potential for medical diagnostics, analytical chemistry, and materials science applications.

Raman spectroscopy is an innovative tool with tremendous potential, serving as a fundamental complement to a variety of provenance methods including heavy-mineral analysis and detrital geochronology. Because of its accuracy, efficiency and versatility, the results of the Raman technique are indispensable for fully reliable identification of heavy minerals in grain mounts or thin sections. Thorny long-standing problems that cannot be solved confidently with a polarizing microscope alone, such as the determination of opaque and altered heavy minerals, of detrital grains as small as a few microns, or of colourless crystals with uncertain orientation and rounded morphology, can finally be addressed. Although the method can be highly automatized, the full ability and experience of the operator is required to combine Raman data with the optical information obtained under the microscope on the same grains, which is essential for the efficient application of the method in provenance studies. This article provides exemplary Raman spectra useful for the comparison and determination of over 70 different opaque and transparent heavy-mineral species commonly found in sediments, conveying specific information on the genesis of their source rocks, and thus is particularly useful in provenance diagnoses and palaeotectonic reconstructions.

A Raman spectral dataset comprising 3,510 spectra from 32 chemical substances. This dataset includes organic solvents and reagents commonly used in API development, along with information regarding the products in the XLSX, and code to visualise and perform technical validation on the data.

X-ray diffraction (XRD) pattern of the iron substrate sample after electrodeposition.

X-ray photoelectron spectroscopy (XPS) of the synthetic mackinawite deposit for (a) Fe 2p spectrum and (b) S 2p spectrum, confirming the deposited sulfide is mackinawite and not stoichiometric pyrite.

Raman spectrum of iron pyrite nano-particles synthesized by the hot injection method at 200 deg C.

This data is presented in the publication "Hyperspectral image analysis for CARS, SRS, and Raman data", J. Raman. Spectroscopy (2015): (http://dx.doi.org/10.1002/jrs.4729). It contains coherent anti-Stokes Raman scattering hyperspectral images and their analysis in terms of concentrations of chemical components and their spectra using the hyperspectral image analysis (HIA) software developed by ourselves. Additional data is shown to exemplify the functionality of HIA to filter motion artefacts.

This study presents a simplified method and empirical relationships for determining organic matter thermal maturity using a portable Raman system equipped with a 785 nm laser, for analysis of crushed, whole-rock samples. Several sets of rocks comprised of shale and coal samples with various mineralogical composition, thermal maturity, total organic carbon (TOC), and age were used to test the method and build correlations between Raman band separation (RBS) values and traditional thermal maturity indicators; organic matter reflectance (Ro) and programmed temperature pyrolysis (Tmax) values. Several sample preparation methods were tested on cuttings material and standard deviation values for RBS were minimized by washing, drying, and hand crushing the material to pass through a 40-mesh sieve, although less preparation can still yield reliable results. For the coal data set, Ro values range from 1.21-4.08% and correlated RBS values plateau at ~250 cm-1 above Ro=3.0% suggesting its correlative application below this maturity level. The second data set, comprised of disparate shale samples where both vitrinite and solid bitumen reflectance values were reported, have Ro values that range from 0.40-4.62%. Above 3.35% Ro, the corresponding RBS values plateau at ~290 cm-1, thus correlations were evaluated with a linear equation (R2= 0.96) between 0.40-3.35% Ro. Shale samples with Ro <2% and Tmax <551 were also used to correlate Tmax and RBS, yielding a linear correlation with an R2 of 0.94. The high degrees of correlation between whole rock RBS data and two thermal maturity indicators demonstrate the utility of this approach for generating source rock thermal maturity data from minimally processed whole rock samples which could easily be applied in field or laboratory settings. These datasets also highlight the utility of whole-rock thermal maturity techniques like programmed temperature pyrolysis and portable Raman spectroscopy versus microscopic maceral specific methods where analyst error (e.g., incorrect maceral identification) can yield potentially erroneous maturity correlations.

Raman spectra of Graphene oxide with different oxidation degrees were measured at 20°C with a spectrometer Horiba Scientific - LabRAM HR Evolution using laser wavelength of 514.5 nm in the range from 700 to 3500 cm-1.

This dataset contains Raman spectra, each acquired from an individual, living, cell entrapped within a soft or stiff gelatin methacrylate hydrogel or from a cell-free region of the hydrogel sample. Spectra were acquired from the following cell types: Madin-Darby Canine Kidney cell (MDCK); Chinese hamster ovary cell (CHO-K1); transfected CHO-K1 cell that expressed the SNAP-tag and HaloTag reporter proteins fused to an organelle-specific protein (CHO-T); human monocyte-like cell (THP-1); inactive macrophage-like (M0-like); active anti-inflammatory macrophage-like (M2-like), pro/anti-inflammatory macrophage-like (M1/M2-like). These spectra are useful for identifying whether the hydrogel matrix obscures the Raman spectral signatures that are characteristic of each of these cell types.

This dataset contains Raman spectra of silicon recorded at room temperature in labspec6 (.ls6) and text (.txt) formats and the corresponding microscope image. Raman spectra are raw (no processing were applied).