Raman Chemical Imaging Research Articles

Line-scan Raman imaging and spectroscopy platform for surface and subsurface evaluation of food safety and quality

Both surface and subsurface food inspection is important since interesting safety and quality attributes can be at different sample locations. This paper presents a multipurpose line-scan Raman platform for food safety and quality research, which can be configured for Raman chemical imaging (RCI) mode for surface inspection and spatially offset Raman spectroscopy (SORS) mode for subsurface inspection. In the RCI mode, macro-scale imaging was achieved using a 785 nm line laser up to 24 cm long with a push-broom method. In the SORS mode, a 785 nm point laser was used and a complete set of SORS data was collected in an offset range of 0–36 mm with a spatial interval of 0.07 mm using one CCD exposure. The RCI and SORS modes share a common detection module including a dispersive imaging spectrograph and a CCD camera, covering a Raman shift range from −674 to 2865 cm−1. A pork shoulder and an orange carrot were used to test large-field-of-view (230 mm wide) and high-spatial-resolution (0.07 mm/pixel) settings of the RCI mode for food surface evaluation. Fluorescence-corrected images at selected Raman peak wavenumbers were used to view Raman-active analytes on the whole sample surfaces (e.g., fat on the pork shoulder and carotenoids over the carrot cross section). Also, three layered samples, which were created by placing carrot slices with thicknesses of 2, 5, and 8 mm on top of melamine powder, were used to test the SORS mode for subsurface food evaluation. Raman spectra from carrot and melamine were successfully resolved for all three layered samples using self-modeling mixture analysis. The line-scan Raman imaging and spectroscopy platform provides a new tool for surface and subsurface inspection for food safety and quality.

Raman imaging of changes in the polysaccharides distribution in the cell wall during apple fruit development and senescence

Main conclusionDuring on-tree ripening, the pectin distribution changed from polydispersed in cell wall to cumulated in cell wall corners. During apple storage, the pectin distribution returned to evenly dispersed along the cell wall.The plant cell wall influences the texture properties of fruit tissue for example apples become softer during ripening and postharvest storage. This softening process is believed to be mainly connected with changes in the cell wall composition due to polysaccharides undergoing an enzymatic degradation. These changes in polysaccharides are currently mainly investigated via chemical analysis or monoclonal labeling. Here, we propose the application of Raman microscopy for evaluating the changes in the polysaccharide distribution in the cell wall of apples during both ripening and postharvest storage. The apples were harvested 1 month and 2 weeks before optimal harvest date as well as at the optimal harvest date. The apples harvested at optimal harvest date were stored for 3 months. The Raman maps, as well as the chemical analysis were obtained for each harvest date and after 1, 2 and 3 months of storage, respectively. The analysis of the Raman maps showed that the pectins in the middle lamella and primary cell wall undergo a degradation. The changes in cellulose and hemicellulose were less pronounced. These findings were confirmed by the chemical analysis results. During development changes of pectins from a polydispersed form in the cell walls to a cumulated form in cell wall corners could be observed. In contrast after 3 months of apple storage we could observe an substantial pectin decrease. The obtained results demonstrate that Raman chemical imaging might be a very useful tool for a first identification of compositional changes in plant tissue during their development. The great advantage Raman microspectroscopy offers is the simultaneous localization and identification of polysaccharides within the cell wall and plant tissue.Electronic supplementary materialThe online version of this article (doi:10.1007/s00425-015-2456-4) contains supplementary material, which is available to authorized users.

Raman chemical imaging of the rhizosphere bacterium Pantoea sp. YR343 and its co-culture with Arabidopsis thaliana.

Chemical imaging of plant-bacteria co-cultures makes it possible to characterize bacterial populations and behaviors and their interactions with proximal organisms, under conditions closest to the environment in the rhizosphere. Here Raman micro-spectroscopy and confocal Raman imaging are used as minimally invasive probes to study the rhizosphere bacterial isolate, Pantoea sp. YR343, and its co-culture with model plant Arabidopsis thaliana by combining enhanced Raman spectroscopies with electron microscopy and principal component analysis (PCA). The presence of carotenoid pigments in the wild type Pantoea sp. YR343 was characterized using resonance Raman scattering, which was also used to confirm successful disruption of the crtB gene in an engineered carotenoid mutant strain. Other components of the Pantoea sp. YR343 cells were imaged in the presence of resonantly enhanced pigments using a combination of surface enhanced Raman imaging and PCA. Pantoea sp. YR343 cells decorated with Ag colloid synthesized ex situ gave spectra dominated by carotenoid scattering, whereas colloids synthesized in situ produced spectral signatures characteristic of flavins in the cell membrane. Scanning electron microscopy (SEM) of whole cells and transmission electron microscopy (TEM) images of thinly sliced cross-sections were used to assess structural integrity of the coated cells and to establish the origin of spectral signatures based on the position of Ag nanoparticles in the cells. Raman imaging was also used to characterize senescent green Arabidopsis thaliana plant roots inoculated with Pantoea sp. YR343, and PCA was used to distinguish spectral contributions from plant and bacterial cells, thereby establishing the potential of Raman imaging to visualize the distribution of rhizobacteria on plant roots.

An iterative approach for compound detection in an unknown pharmaceutical drug product: Application on Raman microscopy

Raman chemical imaging provides both spectral and spatial information on a pharmaceutical drug product. Even if the main objective of chemical imaging is to obtain distribution maps of each formulation compound, identification of pure signals in a mixture dataset remains of huge interest. In this work, an iterative approach is proposed to identify the compounds in a pharmaceutical drug product, assuming that the chemical composition of the product is not known by the analyst and that a low dose compound can be present in the studied medicine. The proposed approach uses a spectral library, spectral distances and orthogonal projections to iteratively detect pure compounds of a tablet. Since the proposed method is not based on variance decomposition, it should be well adapted for a drug product which contains a low dose product, interpreted as a compound located in few pixels and with low spectral contributions. The method is tested on a tablet specifically manufactured for this study with one active pharmaceutical ingredient and five excipients. A spectral library, constituted of 24 pure pharmaceutical compounds, is used as a reference spectral database. Pure spectra of active and excipients, including a modification of the crystalline form and a low dose compound, are iteratively detected. Once the pure spectra are identified, multivariate curve resolution-alternating least squares process is performed on the data to provide distribution maps of each compound in the studied sample. Distributions of the two crystalline forms of active and the five excipients were in accordance with the theoretical formulation.

Vapor Phase Alkyne Coating of Pharmaceutical Excipients: Discrimination Enhancement of Raman Chemical Imaging for Tablets

Raman chemical imaging has become a powerful analytical tool to investigate the crystallographic characteristics of pharmaceutical ingredients in tablet. However, it is often difficult to discriminate some pharmaceutical excipients from each other by Raman spectrum because of broad and overlapping signals, limiting their detailed assessments. To overcome this difficulty, we developed a vapor phase coating method of excipients by an alkyne, which exhibits a distinctive Raman signal in the range of 2100-2300 cm(-1) . We found that the combination of two volatile reagents, propargyl bromide and triethylamine, formed a thin and nonvolatile coating on the excipient and observed the Raman signal of the alkyne at the surface. We prepared alkyne-coated cellulose by this method and formed a tablet. The Raman chemical imaging of the tablet cross-section using the alkyne peak area intensity of 2120 cm(-1) as the index showed a much clearer particle image of cellulose than using the peak area intensity of 1370 cm(-1) , which originated from the cellulose itself. Our method provides an innovative technique to analyze the solid-state characteristics of pharmaceutical excipients in tablets.

Line-Scan Macro-scale Raman Chemical Imaging for Authentication of Powdered Foods and Ingredients

Adulteration and fraud for powdered foods and ingredients are rising food safety risks that threaten consumers’ health. In this study, a newly developed line-scan macro-scale Raman imaging system using a 5-W 785-nm line laser as excitation source was used to authenticate food powders. The system was used to collect hyperspectral Raman images in a wave number range of 102–2865 cm−1 from three representative food powders mixed with selected chemical adulterants with a concentration of 0.5 %, including milk powder and melamine, wheat flour and benzoyl peroxide, and corn starch and maleic anhydride. An acoustic mixer that generates high-intensity acoustic waves was used to create food-adulterant mixtures. Maximum penetration depths of the line laser through powders of milk, flour, and starch were all estimated at 3 mm using layered food-on-adulterant samples. All the mixed samples were placed in sample holders with a surface area of 50 mm × 50 mm and a depth of 3 mm to ensure the adulterant particles at the very bottom can still be detected. Raman spectral and image processing algorithms were developed based on fluorescence correction and single-band images at unique Raman peaks of the individual adulterants. Chemical images were created to show identification, spatial distribution, and morphological features of the adulterant particles mixed in the food powders. The potential of estimating mass concentrations of the adulterants using the percentages of the adulterant pixels in the chemical images (i.e., volume concentrations) was also demonstrated.

Fiber array based hyperspectral Raman imaging for chemical selective analysis of malaria-infected red blood cells

A new setup for Raman spectroscopic wide-field imaging is presented. It combines the advantages of a fiber array based spectral translator with a tailor-made laser illumination system for high-quality Raman chemical imaging of sensitive biological samples.The Gaussian-like intensity distribution of the illuminating laser beam is shaped by a square-core optical multimode fiber to a top-hat profile with very homogeneous intensity distribution to fulfill the conditions of Koehler. The 30 m long optical fiber and an additional vibrator efficiently destroy the polarization and coherence of the illuminating light. This homogeneous, incoherent illumination is an essential prerequisite for stable quantitative imaging of complex biological samples. The fiber array translates the two-dimensional lateral information of the Raman stray light into separated spectral channels with very high contrast. The Raman image can be correlated with a corresponding white light microscopic image of the sample. The new setup enables simultaneous quantification of all Raman spectra across the whole spatial area with very good spectral resolution and thus outperforms other Raman imaging approaches based on scanning and tunable filters.The unique capabilities of the setup for fast, gentle, sensitive, and selective chemical imaging of biological samples were applied for automated hemozoin analysis. A special algorithm was developed to generate Raman images based on the hemozoin distribution in red blood cells without any influence from other Raman scattering. The new imaging setup in combination with the robust algorithm provides a novel, elegant way for chemical selective analysis of the malaria pigment hemozoin in early ring stages of Plasmodium falciparum infected erythrocytes.

Setting local rank constraints by orthogonal projections for image resolution analysis: Application to the determination of a low dose pharmaceutical compound

Raman chemical imaging provides chemical and spatial information about pharmaceutical drug product. By using resolution methods on acquired spectra, the objective is to calculate pure spectra and distribution maps of image compounds. With multivariate curve resolution-alternating least squares, constraints are used to improve the performance of the resolution and to decrease the ambiguity linked to the final solution. Non negativity and spatial local rank constraints have been identified as the most powerful constraints to be used.In this work, an alternative method to set local rank constraints is proposed. The method is based on orthogonal projections pretreatment. For each drug product compound, raw Raman spectra are orthogonally projected to a basis including all the variability from the formulation compounds other than the product of interest. Presence or absence of the compound of interest is obtained by observing the correlations between the orthogonal projected spectra and a pure spectrum orthogonally projected to the same basis. By selecting an appropriate threshold, maps of presence/absence of compounds can be set up for all the product compounds. This method appears as a powerful approach to identify a low dose compound within a pharmaceutical drug product. The maps of presence/absence of compounds can be used as local rank constraints in resolution methods, such as multivariate curve resolution-alternating least squares process in order to improve the resolution of the system. The method proposed is particularly suited for pharmaceutical systems, where the identity of all compounds in the formulations is known and, therefore, the space of interferences can be well defined.

Abstracts from The Aerosol Society Drug Delivery to the Lungs 25Edinburgh International Conference Centre Edinburgh, Scotland, UK December 10–12, 2014

Abstracts from The Aerosol Society Drug Delivery to the Lungs 25Edinburgh International Conference Centre Edinburgh, Scotland, UK December 10–12, 2014

Investigation of the composition of anabolic tablets using near infrared spectroscopy and Raman chemical imaging

The use of performance enhancing drugs is a widespread phenomenon in professional and leisure sports. A spectroscopic study was carried out on anabolic tablets labelled as 5 mg methandienone tablets provided by police departments. The analytical approach was based on a two-step methodology: a fast analysis of tablets using near infrared (NIR) spectroscopy to assess sample homogeneity based on their global composition, followed by Raman chemical imaging of one sample per NIR profile to obtain information on sample formulation. NIR spectroscopy assisted by a principal components analysis (PCA) enabled fast discrimination of different profiles based on the excipient formulation. Raman hyperspectral imaging and multivariate curve resolution - alternating least square (MCR-ALS) provided chemical images of the distribution of the active substance and excipients within tablets and facilitated identification of the active compounds. The combination of NIR spectroscopy and Raman chemical imaging highlighted dose-to-dose variations and succeeded in the discrimination of four different formulations out of eight similar samples of anabolic tablets. Some samples contained either methandienone or methyltestosterone whereas one sample did not contain an active substance. Other ingredients were sucrose, lactose, starch or talc. Both techniques were fast and non-destructive and therefore can be carried out as exploratory methods prior to destructive screening methods. Copyright © 2015 John Wiley & Sons, Ltd.

Thorough characterization of a Self-Emulsifying Drug Delivery System with Raman hyperspectral imaging: A case study

Newly developed drugs often have poor bioavailability due to their poor water solubility (BCS class 2 drugs). It is therefore necessary to develop new strategies to enhance their solubility and their activity, among which, Self-Emulsifying Drug Delivery System (SEDDS). The efficacy of the drugs contained in these preparations is mainly affected by the solid state and the particle size of the active pharmaceutical ingredient (API).However, it is quite complex, long and expensive to characterize these parameters with classical techniques such as X-ray powder diffraction, differential scanning calorimetry or hot stage microscopy.The present article presents, through a case study, the advantages of the Raman hyperspectral imaging in the characterization of such formulations. Indeed, Raman chemical imaging may fully characterize SEDDS with single equipment and operator in a non-destructive way allowing the follow-up of the formulation during stability studies. Raman imaging is therefore a tool of choice in the PAT framework since it increases the knowledge of the formulation and the process.A quantitative multivariate method using Raman hyperspectral imaging to assay the API in the lipid based formulation has been developed and fully validated following the “total error” approach.

Quantification of low drug concentration in model formulations with multivariate analysis using surface enhanced Raman chemical imaging

This paper reports the application of surface enhanced Raman chemical imaging (SER-CI) as a potentially non-destructive quantitative analytical method for the investigation of model pharmaceutical formulations containing the active pharmaceutical ingredient (API) in low concentrations (0.5–2%). The application of chemometric techniques for processing the spectra enables the determination of API distribution in products of different concentrations. In addition, the applied multivariate curve resolution can be proper method to identify unexpected contaminants in illicit drugs. The drastic Raman signal enhancement in the presence of silver nanoparticles provides significantly improved calibration accuracy and, at the same time, radically decreased image acquisition time compared to conventional Raman chemical imaging.

High-Throughput Raman Chemical Imaging for Rapid Evaluation of Food Safety and Quality

Abstract. High-throughput macro-scale Raman chemical imaging was realized on a newly developed line-scan hyperspectral system. The system utilizes a custom-designed 785 nm line laser with maximum power of 5 W as an excitation source. A 24 cm × 1 mm excitation line is normally projected on the sample surface using a 45° dichroic beamsplitter. A detection module consisting of a dispersive imaging spectrograph and a CCD camera is used to acquire Raman signals along the laser line. A motorized positioning table moves the samples transversely through the laser line, and hyperspectral data are obtained using a pushbroom method. System software was developed using LabVIEW to fulfill various functions such as parameter setting, data transfer, and image processing. The system covers a Raman shift range of -691.5 to 2841.2 cm-1 with a spectral resolution of 14 cm-1. The system can collect spatial information requiring either large instantaneous field of view (e.g., 23 cm) or high spatial resolution (e.g., 0.07 mm). The system performance was demonstrated by an example application for authenticating flour. The system acquired a 320 × 250 × 1024 hypercube (80,000 spectra) from three powder samples placed in three Petri dishes (two with a diameter of 47 mm and the other 90 mm) in 9 min. Azodicarbonamide particles mixed in unbleached flour can be detected based on the chemical image generated using a simple image classification method. The method and system for high-throughput macro-scale Raman chemical imaging is promising to inspect other food and agricultural products for safety and quality evaluation.

Development of a Raman chemical imaging detection method for authenticating skim milk powder

This research developed a Raman chemical imaging method for detecting multiple adulterants in skim milk powder. Ammonium sulfate, dicyandiamide, melamine, and urea were mixed into the milk powder as chemical adulterants in the concentration range of 0.1–5.0 %. A Raman imaging system with a 785-nm laser was used to acquire hyperspectral images in the wavenumber range of 102–2,538 cm−1 for a 25 × 25 mm2 area of each mixture. A polynomial curve-fitting method was used to correct for the fluorescence background in the Raman images. An image classification method was developed based on single-band fluorescence-free images at unique Raman peaks of the adulterants. Raman chemical images were created to visualize identification and distribution of the multiple adulterant particles in the milk powder. A linear relationship was found between adulterant pixel number and adulterant concentration, demonstrating the potential of this Raman chemical imaging method for quantitative analysis of adulterants in the milk powder.

Assessment of Active Pharmaceutical Ingredient Particle Size in Tablets by Raman Chemical Imaging Validated using Polystyrene Microsphere Size Standards

Particle size is a critical parameter for controlling pharmaceutical quality. The aim of this study was to assess the size of the micrometer-scale active pharmaceutical ingredients (API) in tablets using Raman chemical imaging and to understand the effects of formulation on particle size. Model tablets containing National Institute of Standards and Technology traceable polystyrene microsphere size standards were developed to determine the binarization threshold value of Raman chemical images for API particle sizing in specific formulations and processes. Three sets of model tablets containing 5, 10, and 15 μm polystyrene microspheres, used to mimic API, were prepared using a commercial tablet formulation (Ebastel tablets, mean API particle size was about 5 μm). Raman mapping with a 50× objective (NA, 0.75) was applied to tablet cross-sections, and particle size of polystyrene microspheres was estimated from binary images using several binarization thresholds. Mean particle size for three sets of polystyrene microspheres showed good agreement between pre- and postformulation (the slope = 1.024, R = 1.000) at the specific threshold value ((mean + 0.5σ) of the polystyrene-specific peak intensity histogram), regardless of particle agglomeration, tablet surface roughness, and laser penetration depth. The binarization threshold value showed good applicability to Ebastel tablets, where the API-specific peak intensity histogram showed a pattern similar to that of polystyrene microspheres in model tablets. The model tablets enabled determination of an appropriate binarization threshold for assessing the mean particle size of micrometer-scale API in tablets by utilizing the unique physicochemical properties of polystyrene microspheres.

Non-destructive Localization and Identification of Active Pharmaceutical Compounds by Raman Chemical Imaging

Raman spectroscopy is a powerful and non-destructive technique for chemical and structural identification. Based on inelastic scattering of laser light by molecular vibrations, the analysis can be localized on a microscopic area when combined with a microscope. Thus, by moving the sample under the microscope objective and recording a Raman spectrum at each point, a map of the intensity of specific Raman bands can be generated, effectively creating a chemical image of the sample at the microscale. Here, we present an application of this technique for identifying and localizing active pharmaceutical ingredients in a polymer matrix.

Development of a quantitative approach using surface-enhanced Raman chemical imaging: First step for the determination of an impurity in a pharmaceutical model

This publication reports, for the first time, the development of a quantitative approach using surface-enhanced Raman chemical imaging (SER-CI). A pharmaceutical model presented as tablets based on paracetamol, which is the most sold drug around the world, was used to develop this approach. 4-Aminophenol is the main impurity of paracetamol and is actively researched in pharmaceutical formulations because of its toxicity. As its concentration is generally very low (<0.1%, w/w), conventional Raman chemical imaging cannot be used.In this context, a SER-CI method was developed to quantify 4-aminophenol assessing a limit of quantification below its limit of specification of 1000ppm. Citrate-reduced silver nanoparticles were used as SERS substrate and these nanoparticles were functionalized using 1-butanethiol. Different ways to cover the tablets surface by butanethiol-functionalized silver nanoparticles were tested and a homogeneity study of the silver nanoparticles covering was realized. This homogeneity study was performed in order to choose the best way to cover the surface of tablets by silver colloid. Afterwards, the optimization of the SER-CI approach was necessary and different spectral intensity normalizations were tested. Finally, a quantitative approach using SER-CI was developed enabling to quantify 4-aminophenol from 0.025% to 0.2% in paracetamol tablets. This quantitative approach was tested on two different series of tablets using different batches of silver nanoparticles.

Multicomponent chemical imaging of pharmaceutical solid dosage forms with broadband CARS microscopy.

We compare a coherent Raman imaging modality, broadband coherent anti-Stokes Raman scattering (BCARS) microscopy, with spontaneous Raman microscopy for quantitative and qualitative assessment of multicomponent pharmaceuticals. Indomethacin was used as a model active pharmaceutical ingredient (API) and was analyzed in a tabulated solid dosage form, embedded within commonly used excipients. In comparison with wide-field spontaneous Raman chemical imaging, BCARS acquired images 10× faster, at higher spatiochemical resolution and with spectra of much higher SNR, eliminating the need for multivariate methods to identify chemical components. The significant increase in spatiochemical resolution allowed identification of an unanticipated API phase that was missed by the spontaneous wide-field method and bulk Raman spectroscopy. We confirmed the presence of the unanticipated API phase using confocal spontaneous Raman, which provided spatiochemical resolution similar to BCARS but at 100× slower acquisition times.

Investigation of drug distribution in tablets using surface enhanced Raman chemical imaging

This paper reports the first application of surface enhanced Raman chemical imaging on pharmaceutical tablets containing the active ingredient (API) in very low concentrations. Taking advantage of the extremely intensive Raman signals in the presence of silver colloids, image aquisition time was radically decreased. Moreover, the investigation of drug distribution below the detection limit of regular micro-Raman spectrometry was made feasible. The characteristics of different manufacturing technologies could be revealed at very low API concentrations by using chemometric methods for processing and evaluating the large number of varying spectra provided with this imaging method.

Simultaneous detection of multiple adulterants in dry milk using macro-scale Raman chemical imaging

The potential of Raman chemical imaging for simultaneously detecting multiple adulterants in milk powder was investigated. Potential chemical adulterants, including ammonium sulphate, dicyandiamide, melamine, and urea, were mixed together into skim dry milk in the concentration range of 0.1–5.0% for each adulterant. Using a 785-nm laser, a Raman imaging system acquired hyperspectral images in the wavenumber range of 102–2538cm−1 for a 25×25mm2 area of each mixture sample, with a spatial resolution of 0.25mm. Self-modelling mixture analysis (SMA) was used to extract pure component spectra, by which the four types of the adulterants were identified at all concentration levels based on their spectral information divergence values to the reference spectra. Raman chemical images were created using the contribution images from SMA, and their use to effectively visualise identification and spatial distribution of the multiple adulterant particles in the dry milk was demonstrated.