Raman Imaging Data Research Articles

Raman spectral analysis for non-invasive detection of external and internal parameters of fake eggs

Cases of imitation or fake food materials are sometimes produced and sold for purposes of economic fraud. However, while some imitation or fake food materials merely incorporate lower quality or cheaper alternative ingredients that are safe to eat, others fakes are produced using non-edible or hazardous ingredients that are unsafe for consumption. The latter group includes fake eggs which are often difficult to identify by eye. Such fakes have been found in various parts of Asia, made from harmful ingredients such as sodium alginate, tartrazine dye, gypsum powder, and paraffin wax. The objective of this study is to evaluate the use of Raman spectral analysis for nondestructive, noninvasive identification of fake eggs. In this study, fake eggs were prepared and then Raman spectroscopic and imaging data were collected from both the fake eggs and real chicken eggs. Classification of the fake and real eggs was tested using both Raman spectroscopy (1800–600 cm−1) with multivariate analysis methods and Raman hyperspectral imaging (1500–390 cm−1) with waveband optimization. The results demonstrated that both techniques are able to differentiate fake eggs from real eggs.

Applications of Raman spectroscopy in art and archaeology

Applications of Raman spectroscopy in art and archaeology

Raman spectroscopy-based insight into lipid droplets presence and contents in liver sinusoidal endothelial cells and hepatocytes.

Liver sinusoidal endothelial cells (LSECs), a type of endothelial cells with unique morphology and function, play an important role in the liver hemostasis, and LSECs dysfunction is involved in the development of nonalcoholic fatty liver disease (NAFLD). Here, we employed Raman imaging and chemometric data analysis in order to characterize the presence of lipid droplets (LDs) and their lipid content in primary murine LSECs, in comparison with hepatocytes, isolated from mice on high-fat diet. On NAFLD development, LDs content in LSECs changed toward more unsaturated lipids, and this response was associated with an increased expression of stearylo-CoA desaturase-1. To the best of our knowledge, this is a first report characterizing LDs in LSECs, where their chemical composition is analyzed along the progression of NAFLD at the level of single LD using Raman imaging.

The combination of Raman imaging and LIBS for quantification of original and degradation materials in Cultural Heritage

AbstractQuantification with nondestructive techniques is not very well developed in the field of Cultural Heritage despite its interest. In the last decades, several works have been published using Raman spectroscopy for quantifying. However, depending on the methodology used, the information provided by this technique is not complete, and the results could lead to misunderstanding when dealing with unknown samples. In this work, a novel double quantification using Raman imaging (its representativeness would be higher than point‐by‐point analysis) and Laser Induce Breakdown Spectroscopy (LIBS) analyses is presented for analysis of Cultural Heritage samples. Several dolomitic marble samples, with some calcite impurities, covered or not by a calcium oxalate (whewellite, CaC2O4·H2O) layer were analysed in order to optimize the novel methodology. The agreement between the quantitative results from the independent analyses of the Raman Image data and the LIBS data is consistent within the uncertainty arising from both techniques. Further, the same methodology was applied on the same samples but using point‐by‐point Raman analysis with portable instruments, and portable LIBS information, showing again a great agreement between them and with the results obtained using the laboratory instruments.

Investigation of carbon in the Allan Hills A77278 meteorite via 2D and 3D Raman microspectroscopy

AbstractThis paper reports the first Raman spectral and imaging data on the ordinary chondrite Allan Hills A77278 (ALHA77278) in two and three dimensions. Mineralogy in the ALHA77278 meteorite appears to be mainly silicates (olivine and pyroxene) with some carbonaceous material in the matrix. Metal phases are also present in the studied sample. Two‐dimensional Raman spectral distribution maps show the locations of these chemical constituents. Several diamond grains are observed to be buried in olivine and extend down to a ~7‐μm depth in the sample, which might be contamination captured during the sample preparation. Raman spectral peak parameters for D and G carbon bands are presented and trends among these parameters are discussed. These spectral parameters, when compared with those of other meteorites reported in the literature, show similarity with unequilibrated ordinary chondrites and primitive carbonaceous chondrites. Estimated peak metamorphic temperature for the parent asteroid is calculated to be around ~260°C, which is comparable with lower metamorphic grade ordinary chondrites, indicating that the metamorphic grade of ALHA77278 might be less than 3.7, as it has been officially assigned. Three‐dimensional Raman tomographic measurements present visualizations of the identified chemical components in high resolution as well as the epoxy used during the preparation of the sample. Spatial distribution of epoxy is used to avoid these regions during the Raman spectral data extraction from the carbonaceous matter from a depth of the sample, which could otherwise complicate the datasets used to infer the thermal history of the ALHA77278 meteorite.

Calibration and testing of a Raman hyperspectral imaging system to reveal powdered food adulteration.

The potential adulteration of foodstuffs has led to increasing concern regarding food safety and security, in particular for powdered food products where cheap ground materials or hazardous chemicals can be added to increase the quantity of powder or to obtain the desired aesthetic quality. Due to the resulting potential health threat to consumers, the development of a fast, label-free, and non-invasive technique for the detection of adulteration over a wide range of food products is necessary. We therefore report the development of a rapid Raman hyperspectral imaging technique for the detection of food adulteration and for authenticity analysis. The Raman hyperspectral imaging system comprises of a custom designed laser illumination system, sensing module, and a software interface. Laser illumination system generates a 785 nm laser line of high power, and the Gaussian like intensity distribution of laser beam is shaped by incorporating an engineered diffuser. The sensing module utilize Rayleigh filters, imaging spectrometer, and detector for collection of the Raman scattering signals along the laser line. A custom-built software to acquire Raman hyperspectral images which also facilitate the real time visualization of Raman chemical images of scanned samples. The developed system was employed for the simultaneous detection of Sudan dye and Congo red dye adulteration in paprika powder, and benzoyl peroxide and alloxan monohydrate adulteration in wheat flour at six different concentrations (w/w) from 0.05 to 1%. The collected Raman imaging data of the adulterated samples were analyzed to visualize and detect the adulterant concentrations by generating a binary image for each individual adulterant material. The results obtained based on the Raman chemical images of adulterants showed a strong correlation (R>0.98) between added and pixel based calculated concentration of adulterant materials. This developed Raman imaging system thus, can be considered as a powerful analytical technique for the quality and authenticity analysis of food products.

Raman Imaging for the Detection of Adulterants in Paprika Powder: A Comparison of Data Analysis Methods

Raman imaging requires the effective extraction of chemical information from the corresponding datasets, which can be achieved by a range of analytical methods. However, since each of these methods exhibits both strengths and weaknesses, we herein directly compare univariate, bivariate, and multivariate analyses of Raman imaging data by evaluating their performance in the quantitation of two adulterants in paprika powder. Univariate and bivariate models were developed based on the spectral features of the target adulterants, whereas spectral angle mapper (SAM), adopted as a multivariate analysis method, utilized the complete dataset. The obtained results demonstrate that despite being simple and easily implementable, the univariate method affords false positive pixels in the presence of background noise. Luckily, the above problem can be easily resolved using the bivariate method, which utilizes the multiplication of two band images wherein the same adulterant shows high-intensity peaks exhibiting the least overlap with those of other sample constituents. Finally, images produced by SAM contain abundant false negative pixels of adulterants, particularly for low-concentration samples. Notably, the bivariate method affords results closely matching the theoretical adulterant content, exhibiting the advantages of using non-complex data (only two bands are utilized) and being well suited to online applications of Raman imaging in the agro-food sector.

Development of a label-free Raman imaging technique for differentiation of malaria parasite infected from non-infected tissue.

During malarial infection, the host uses the spleen to clear the malaria parasites, however, the parasites have evolved the ability to bind to endothelial receptors in blood vessels of tissues to avoid removal, known as sequestration, and this is largely responsible for the symptoms and severity of infection. So a technique which could non-invasively diagnose tissue burden could be utilised as an aid for localised malaria diagnosis within tissue. Raman spectroscopy is a label-free imaging technique and can provide unique and chemically specific Raman 'fingerprint' spectrum of biological samples such as tissue. Within this study, Raman imaging was used to observe the changes to the molecular composition of mice spleen tissue under malarial infection, compared with non-infected samples. From analysis of the Raman imaging data, both tissue types showed very similar spectral profiles, which highlighted that their biochemical compositions were closely linked. Principal component analysis showed very clear separation of the two sample groups, with an associated increase in concentration of heme-based Raman vibrations within the infected dataset. This was indicative of the presence of hemozoin, the malaria pigment, being detected within the infected spleen. Separation also showed that as the hemozoin content within the tissue increased, there was a corresponding change to hemoglobin and some lipid/nucleic acid vibrations. These results demonstrate that Raman spectroscopy can be used to easily discriminate the subtle changes in tissue burden upon malarial infection.

Integrating spatial, morphological, and textural information for improved cell type differentiation using Raman microscopy

AbstractRaman microscopy is a well‐established tool for distinguishing different cell types in cell biological or cytopathological applications, since it can provide maps that show the specific distribution of biochemical components in the cell, with high lateral and spatial resolution. Currently, established data analysis approaches for differentiating cells of different types mostly rely on conventional chemometrics approaches, which tend to not systematically utilise the advantages provided by Raman microscopic data sets. To address this, we propose 2 approaches that explicitly exploit the large number of spectra as well as the morphological and textural information that are available in Raman microscopic data sets. Spatial bagging as our first approach is based on a statistical analysis of majority vote over classification results obtained from individual pixel spectra. Based on the Condorcet's Jury Theorem, this approach raises the accuracy of a relatively weak classifier for individual spectra to nearly perfect accuracy at the level of characterising whole cells. Our second approach extracts morphological and textural (morpho‐textural) features from Raman microscopic images to differentiate cell types. While using few wavenumbers of the Raman spectrum only, our results indicate on a quantitative basis that Raman microscopic images carry more morphological and textural information than haematoxylin and eosin (H&E) stained images as the current gold standard in cytopathology. Our 2 approaches promise improved protocols for the fast acquisition of Raman imaging data, for instance, for the morphological analysis of coherent anti‐Stokes Raman spectroscopy microscopic imaging data or for improving the accuracy of fibre optical probe systems by resampling spectra and utilising spatial bagging.

Obtaining pure spectra of hemicellulose and cellulose from poplar cell wall Raman imaging data

A comprehensive understanding of the structural and chemical nature of plant cell walls is important from both the perspectives of plant biotechnology and of commercial utilization. The Raman imaging technique is a preferred solution for its ability to offer spatial and spectral information simultaneously. However, the exact spectra of hemicellulose and cellulose are difficult to directly discern from each other due to the strong spectral overlap. In the present study, we report for the first time that the spectra of hemicellulose and cellulose in poplar cell wall Raman imaging can be discriminated by using multivariate analysis. The semi-quantitative concentrations of polysaccharides are identified based on the corresponding Raman images. Results show that cellulose is mostly concentrated in the secondary wall of poplar fibres, whilst the distribution of hemicellulose is almost uniform throughout the cell wall of fibres except for a higher concentration found in the S1 and the outer S2 layer. The xylem ray and the vessel have relatively high hemicellulose concentrations which is comparable to the outer S2 layer of fibres, but the cellulose concentration is relatively low in these two cell types. This work not only contributes to broadening our knowledge about the distribution of polysaccharides in plant cell walls, but also provides a potential strategy to trace their dynamic changes during the bioconversion at a micro-level.

Raman imaging and principal component analysis-based data processing on uranium oxide ceramics

A ceramic sample of uranium dioxide is probed by Raman imaging followed by a combined Lorentzian fitting – Principal Component Analysis process. This allows to evidence structural or chemical inhomogeneities of the material, which affect Raman line intensities but also line positions, evidencing local symmetry lowering. The inhomogeneities were observed not only between grain cores and boundaries, but also inside grain cores themselves. Only a part of these intensity inhomogeneities is as expected due to different orientations of the ceramic grains. Besides, a zone noticeably differing from others has been distinguished, presumably due to local strains or to chemical nature (oxygen stoichiometry), showing the sensitivity of the analysis. This underlines the importance to perform Raman analysis in such ceramic materials at least on several points and better, in imaging mode.

Method for Removing Spectral Contaminants to Improve Analysis of Raman Imaging Data

The spectral contaminants are inevitable during micro-Raman measurements. A key challenge is how to remove them from the original imaging data, since they can distort further results of data analysis. Here, we propose a method named “automatic pre-processing method for Raman imaging data set (APRI)”, which includes the adaptive iteratively reweighted penalized least-squares (airPLS) algorithm and the principal component analysis (PCA). It eliminates the baseline drifts and cosmic spikes by using the spectral features themselves. The utility of APRI is illustrated by removing the spectral contaminants from a Raman imaging data set of a wood sample. In addition, APRI is computationally efficient, conceptually simple and potential to be extended to other methods of spectroscopy, such as infrared (IR), nuclear magnetic resonance (NMR), X-Ray Diffraction (XRD). With the help of our approach, a typical spectral analysis can be performed by a non-specialist user to obtain useful information from a spectroscopic imaging data set.

Biological and Medical Applications of Multivariate Curve Resolution Assisted Raman Spectroscopy.

Biological specimens such as cells, tissues and biofluids (urine, blood) contain mixtures of many different biomolecules, all of which contribute to a Raman spectrum at any given point. The separation and identification of pure biochemical components remains one of the biggest challenges in Raman spectroscopy. Multivariate curve resolution, a matrix factorization method, is a powerful, yet flexible, method that can be used with constraints, such as non-negativity, to decompose a complex spectroscopic data matrix into a small number of physically meaningful pure spectral components along with their relative abundances. This paper reviews recent applications of multivariate curve resolution by alternating least squares analysis to Raman spectroscopic and imaging data obtained either in vivo or in vitro from biological and medical samples.

Removing Cosmic Spikes Using a Hyperspectral Upper-Bound Spectrum Method.

Cosmic ray spikes are especially problematic for hyperspectral imaging because of the large number of spikes often present and their negative effects upon subsequent chemometric analysis. Fortunately, while the large number of spectra acquired in a hyperspectral imaging data set increases the probability and number of cosmic spikes observed, the multitude of spectra can also aid in the effective recognition and removal of the cosmic spikes. Zhang and Ben-Amotz were perhaps the first to leverage the additional spatial dimension of hyperspectral data matrices (DM). They integrated principal component analysis (PCA) into the upper bound spectrum method (UBS), resulting in a hybrid method (UBS-DM) for hyperspectral images. Here, we expand upon their use of PCA, recognizing that principal components primarily present in only a few pixels most likely correspond to cosmic spikes. Eliminating the contribution of those principal components in those pixels improves the cosmic spike removal. Both simulated and experimental hyperspectral Raman image data sets are used to test the newly developed UBS-DM-hyperspectral (UBS-DM-HS) method which extends the UBS-DM method by leveraging characteristics of hyperspectral data sets. A comparison is provided between the performance of the UBS-DM-HS method and other methods suitable for despiking hyperspectral images, evaluating both their ability to remove cosmic ray spikes and the extent to which they introduce spectral bias.

Advanced Applications of Raman Imaging for Deeper Understanding and Better Quality Control of Formulations.

The importance of using the Raman imaging technique is increasing in pharmaceutical sciences, particularly in the quality control of active pharmaceutical ingredients, formulation design, and manufacturing development. Formulation design based on Raman imaging data is important for achieving quality by design. Recently, several novel Raman imaging measurement and analytical techniques have been reported. It is undoubtedly essential for pharmaceutical researchers and manufacturing engineers to use modern Raman imaging technology to produce the best quality pharmaceutical products. This short review seeks to inform researchers and engineers about recent developments in Raman imaging techniques applicable to formulation design and manufacturing.

Optical Diagnostics of Porous Silicon Nanoparticles Biodegradation

SUMMARY Optical methods such as Raman micro-spectroscopy and photoluminescence were employed for diagnostics of biodegradation of porous silicon nanoparticles (SiNPs) in phantom suspensions and breast cancer cells (MCF-7 cell line) in-vitro.Two types of SiNPs, with and without photoluminescence in the visible spectral range, were investigated. The biodistribution of photoluminescent SiNPs within the cells obtained by Raman imaging was additionally verified by high-resolution structured-illumination optical microscopy (HR-SIM). Almost complete degradation of SiNPs inside the living cells and aqueous suspensions was observed after 2 weeks of storage. The results reveal new prospects of multi-modal optical characterization of SiNPs during their dissolution in vitro. 1. INTRODUCTION SiNPs are well-known to be degradable in physiological fluids and in living tissues due to dissolution process resulting in formation of orthosilicic acid [1]. Usually several week are sufficient for complete release of SiNPs from the body [1]. Nonetheless, the SiNP degradation rate depends on the particle size, porosity, surface coverage, pH value and ambience and can vary from a few days up to several months [2]. The SiNP biodegradation is associated usually with the disappearance of their PL property. It can also be assessed by histological methods or by the analysis of silicon content in the tissues by inductively coupled plasma optical emission spectrometry. However, these methods of analysis are not very precise, fast or/and straightforward. Mesoporous SiNPs do not possess efficient PL, therefore we used two optical methods: PL and Raman spectroscopy for characterization of different types of PSiNPs. 2. EXPERIMENTAL RESULTS We have prepared 100 nm SiNPs by grinding of meso- and microporous silicon films (PSi). Both types of the films were obtained by electrochemical etching of Si wafers in HF-based solutions. Micro PSiNPs have demonstrated efficient PL properties, whereas meso PSiNPs have not, but they provided better contrast in Raman imaging. Degradation of micro PSiNPs in suspensions was investigated during 2 week in order to determine influence of size, concentration, pH level influence on degradation rate. We used centrifugation to obtain micro PSiNPs samples with different size of NPs in order to find size dependency of degradation rate. Meso PSiNPs were tested in the same regime by Raman spectroscopy. Dialysis was applied to enhance degradation. Both methods showed size shrinking of nanoparticles, since PL peak positions is determined by quantum confinement in nanocrystals and Raman peak shifts in small nanocryslals due to the confinement of phonons. Then degradation was examined in vitro. The uptake of silicon nanoparticles by MCF-7 breast cancer cells was observed for time intervals between 6 hours and 2 weeks. The 3D HR-SIM revealed the localization of PL-SiNPs inside cells. Staining of the cell organelles proved the accumulation of nanoparticles mostly in the cytoplasm and in the nucleus periphery (see left Fig.). The Raman images for SiNPs are shown in central Fig. SiNPs exhibited a characteristic peak of Raman scattering at 520 cm-1, which is due to the presence of nanocrystalline silicon in the cells (see right Fig.). The strongly highlighted signal from SiNPs shows presence of SiNPs inside the cell structure without being overlapped by the surrounding signals from the cell organelles. To investigate time-depending modifications of SiNPs the detailed analyses of Raman imaging data for the timeframe of 2 weeks were performed in-vitro. The chemical changes of SiNPs in MCF-7 cell interior determining the biodegradation processes of nanoparticles were evaluated from the corresponding shifts and changes of the Raman spectra and intensity during increased incubation time. Based on the performed investigations the model of SiNPs uptake by cancer cells and biodegradation is proposed. 3. CONCLUSIONS The photolumescent and Raman spectroscopies were used to visualize the intracellular localization and subsequent biodegradation of SiNPs in cancer cells for the first time. The obtained results are important for the development of new methods of ex-vivoexpress-diagnosis of early stages of cancer. REFERENCES Park J.-H., Gu L., von Maltzahn G., Ruoslahti E., Bhatia S.N., Sailor M.J. Nat. Mater. 8(4), 331–336 (2009).Shabir Q. Biodegradability of Porous Silicon. In: Handbook of Porous Silicon. Canham L. (Ed.). Springer Int. Publ., 395–401 (2014). Figure 1

Detection of herbicide effects on pigment composition and PSII photochemistry in Helianthus annuus by Raman spectroscopy and chlorophyll a fluorescence

The effects of herbicides from three mode-of-action groups – inhibitors of protoporphyrinogen oxidase (carfentrazone-ethyl), inhibitors of carotenoid biosynthesis (mesotrione, clomazone, and diflufenican), and inhibitors of acetolactate synthase (amidosulfuron) – were studied in sunflower plants (Helianthus annuus). Raman spectroscopy, chlorophyll fluorescence (ChlF) imaging, and UV screening of ChlF were combined to evaluate changes in pigment composition, photosystem II (PSII) photochemistry, and non-photochemical quenching in plant leaves 6d after herbicide application. The Raman signals of phenolic compounds, carotenoids, and chlorophyll were evaluated and differences in their intensity ratios were observed. Strongly augmented relative content of phenolic compounds was observed in the case of amidosulfuron-treated plants, with a simultaneous decrease in the chlorophyll/carotenoid intensity ratio. The results were confirmed by in vivo measurement of flavonols using UV screening of ChlF. Herbicides from the group of carotenoid biosynthesis inhibitors significantly decreased both the maximum quantum efficiency of PSII and non-photochemical quenching as determined by ChlF. Resonance Raman imaging (mapping) data with high resolution (150,000–200,000 spectra) are presented, showing the distribution of carotenoids in H. annuus leaves treated by two of the herbicides acting as inhibitors of carotenoid biosynthesis (clomazone or diflufenican). Clear signs were observed that the treatment induced carotenoid depletion within sunflower leaves. The depletion spatial pattern registered differed depending on the type of herbicide applied.

Label-Free Neurosurgical Pathology with Stimulated Raman Imaging.

The goal of brain tumor surgery is to maximize tumor removal without injuring critical brain structures. Achieving this goal is challenging as it can be difficult to distinguish tumor from nontumor tissue. While standard histopathology provides information that could assist tumor delineation, it cannot be performed iteratively during surgery as freezing, sectioning, and staining of the tissue require too much time. Stimulated Raman scattering (SRS) microscopy is a powerful label-free chemical imaging technology that enables rapid mapping of lipids and proteins within a fresh specimen. This information can be rendered into pathology-like images. Although this approach has been used to assess the density of glioma cells in murine orthotopic xenografts models and human brain tumors, tissue heterogeneity in clinical brain tumors has not yet been fully evaluated with SRS imaging. Here we profile 41 specimens resected from 12 patients with a range of brain tumors. By evaluating large-scale stimulated Raman imaging data and correlating this data with current clinical gold standard of histopathology for 4,422 fields of view, we capture many essential diagnostic hallmarks for glioma classification. Notably, in fresh tumor samples, we observe additional features, not seen by conventional methods, including extensive lipid droplets within glioma cells, collagen deposition in gliosarcoma, and irregularity and disruption of myelinated fibers in areas infiltrated by oligodendroglioma cells. The data are freely available in a public resource to foster diagnostic training and to permit additional interrogation. Our work establishes the methodology and provides a significant collection of reference images for label-free neurosurgical pathology. Cancer Res; 76(12); 3451-62. ©2016 AACR.

Applying Chemometrics for Analysis of SiC Raman Imaging Data

New ultrafast Raman imaging methods allow high definition data to be collected from a whole wafer scale down to individual defects in 2D and 3D, on a time scale suitable for routine quality control. On the other hand, just one hour of data collection can result in datasets containing 105~106 spectra, and attempts to manually analyze such big data with traditional univariate methods can take days, without guarantee that all important information is revealed. Such problem can be easily overcome with fast and automated multivariate analysis methods. Here we introduce the techniques and demonstrate applications to SiC.

Method for automatically identifying spectra of different wood cell wall layers in Raman imaging data set.

The technique of Raman spectroscopic imaging is finding ever-increasing applications in the field of wood science for its ability to provide spatial and spectral information about the sample. On the basis of the acquired Raman imaging data set, it is possible to determine the distribution of chemical components in various wood cell wall layers. However, the Raman imaging data set often contains thousands of spectra measured at hundreds or even thousands of individual frequencies, which results in difficulties accurately and quickly extracting all of the spectra within a specific morphological region of wood cell walls. To address this issue, the authors propose a new method to automatically identify Raman spectra of different cell wall layers on the basis of principal component analysis (PCA) and cluster analysis. A Raman imaging data set collected from a 55.5 μm × 47.5 μm cross-section of poplar tension wood was analyzed. Several thousand spectra were successfully classified into five groups in accordance with different morphological regions, namely, cell corner (CC), compound middle lamella (CML), secondary wall (SW), gelatinous layer (G-layer), and cell lumen. Their corresponding average spectra were also calculated. In addition, the relationship between different characteristic peaks in the obtained Raman spectra was estimated and it was found that the peak at 1331 cm(-1) is more related to lignin rather than cellulose. Not only can this novel method provide a convenient and accurate procedure for identifying the spectra of different cell wall layers in a Raman imaging data set, but it also can bring new insights into studying the morphology and topochemistry in wood cell walls.