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.     

Constraining shape smoothness in multivariate curve resolution–alternating least squares

The resolution of complex multicomponent hyperspectral images with multivariate curve resolution–alternating least squares is mainly performed by using a limited number of constraints on the pure constituent distribution maps, such as non‐negativity or local constraints. This work proposes a constraint that works with the spatial information of the whole image and has been given the name shape smoothness constraint. Contrary to local constraints, shape smoothness constraint imposes a global character on the distribution map pattern. It uses two‐dimensional P‐splines to enforce smoothness of the global spatial features of the distribution maps generated within the alternating least squares procedure. This allows revealing main pattern(s) in the spatial data leaving high‐frequency signals, corresponding to fine‐scale structures in the image. This approach has been successfully applied on several simulated examples where imposing shape smoothness resulted in the recovery of the correct pattern for the image objects, which in turn provided more accurate distribution maps and spectral profiles. It was also shown that when information about the smoothness of the pattern(s) of the image constituents holds, the constraint can be a flexible and robust alternative for the resolution of real hyperspectral imaging data. Copyright © 2015 John Wiley & Sons, Ltd.     

Image fusion combining SEM and ToF‐SIMS images

Image fusion allows for the combination of an image containing chemical information but low spatial resolution with a high‐spatial resolution image having little to no chemical information. The resulting hybrid image retains all the information from the chemically relevant original image, with improved spatial resolution allowing for visual inspection of the spatial correlations. In this research, images were obtained from two sample test grids: one of a copper electron microscope grid with a letter ‘A’ in the center (referred to below as the ‘A‐grid’), and the other a Tantalum and Silicon test grid from Cameca that had an inscribed letter ‘C’ (referred to below as the ‘Cameca grid’). These were obtained using scanning electron microscopy (SEM) and time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS). Image fusion was implemented with the Munechika algorithm. The edge resolution of the resulting hybrid image was calculated compared with the edge resolution obtained for both the individual ToF‐SIMS and SEM images. The challenges of combining complimentary datasets from different instrumental analytical methods are discussed as well as the advantages of having a hybrid image. The distance across the edge for hybrid images of the A‐Grid and the Cameca grid were determined to be 21 µm and 8 µm, respectively. When these values were compared to the original ToF‐SIMS, SEM and optical microscopy measurements, the fused image had a spatial resolution nearly equal to that obtained in the SEM image for both samples. Copyright © 2014 John Wiley & Sons, Ltd.     

Infrared chemical imaging: Spatial resolution evaluation and super-resolution concept

Chemical imaging systems help to solve many challenges in various scientific fields. Able to deliver rapid spatial and chemical information, modern infrared spectrometers using Focal Plane Array detectors (FPA) are of great interest. Considering conventional infrared spectrometers with a single element detector, we can consider that the diffraction-limited spatial resolution is more or less equal to the wavelength of the light (i.e. 2.5-25 μm). Unfortunately, the spatial resolution of FPA spectroscopic setup is even lower due to the detector pixel size. This becomes a real constraint when micron-sized samples are analysed. New chemometrics methods are thus of great interest to overcome such resolution drawback, while keeping our far-field infrared imaging spectrometers. The aim of the present work is to evaluate the super-resolution concept in order to increase the spatial resolution of infrared imaging spectrometers using FPA detectors. The main idea of super-resolution is the fusion of several low-resolution images of the same sample to obtain a higher-resolution image. Applying the super-resolution concept on a relatively low number of FPA acquisitions, it was possible to observe a 30% decrease in spatial resolution.     

Super-resolution and Raman chemical imaging: from multiple low resolution images to a high resolution image

Imaging in Raman spectroscopy is a valuable tool for analytical chemistry. Although molecular characterization at micron level is achieved for many applications, it usually fails producing chemical images of micron size samples as expected in chemical, environmental and biological analysis. The aim of the work is to introduce the potential of super-resolution in vibrational spectroscopic imaging. This original chemometrics approach uses several low resolution images of the same sample in order to retrieve a higher resolution chemical image. It is thus possible to overcome in a certain way some physical and instrumentals limitations. To illustrate the methodology, sub-micronic details of a Si/Au sample are retrieved from low resolution images with different super-resolution algorithms. The better results are obtained with Iterative L2/Bilateral Total Variation regularization method. The use of a regularization procedure gives also better results since its first property is to preserve edges during the reconstruction of the super-resolved image. This concept of chemical image data processing should open new analytical opportunities.     

Multivariate curve resolution-particle swarm optimization: A high-throughput approach to exploit pure information from multi-component hyphenated chromatographic signals

Multivariate curve resolution-particle swarm optimization (MCR-PSO) algorithm is proposed to exploit pure chromatographic and spectroscopic information from multi-component hyphenated chromatographic signals. This new MCR method is based on rotation of mathematically unique PCA solutions into the chemically meaningful MCR solutions. To obtain a proper rotation matrix, an objective function based on non-fulfillment of constraints is defined and is optimized using particle swarm optimization (PSO) algorithm. Initial values of rotation matrix are calculated using local rank analysis and heuristic evolving latent projection (HELP) method. The ability of MCR-PSO in resolving the chromatographic data is evaluated using simulated gas chromatography–mass spectrometry (GC–MS) and high-performance liquid chromatography–diode array detection (HPLC–DAD) data. To present a comprehensive study, different number of components and various levels of noise under proper constraints of non-negativity, unimodality and spectral normalization are considered. Calculation of the extent of rotational ambiguity in MCR solutions for different chromatographic systems using MCR-BANDS method showed that MCR-PSO solutions are always in the range of feasible solutions like true solutions. In addition, the performance of MCR-PSO is compared with other popular MCR methods of multivariate curve resolution-objective function minimization (MCR-FMIN) and multivariate curve resolution-alternating least squares (MCR-ALS). The results showed that MCR-PSO solutions are rather similar or better (in some cases) than other MCR methods in terms of statistical parameters. Finally MCR-PSO is successfully applied in the resolution of real GC–MS data. It should be pointed out that in addition to multivariate resolution of hyphenated chromatographic signals, MCR-PSO algorithm can be straightforwardly applied to other types of separation, spectroscopic and electrochemical data.     

Remotely detected MRI velocimetry in microporous bead packs

Many NMR and MRI methods probe fluid dynamics within macro- and mesoporous materials, but with few exceptions, they report on its macroscopically averaged properties. MRI methods are generally unable to localize microscopic features of flow within macroscopic samples because the fraction of the enclosing detector volume occupied by these features is so small. We have recently overcome this problem using remotely detected MRI velocimetry, a technique in which spatial, chemical, and velocity information about elements of the flow is encoded with a conventional NMR coil and detected sensitively at the sample outflow by a volume-matched microdetector. Here, we apply this method to microporous model systems, recording MRI images that correlate local velocity, spin relaxation, and time-of-flight in microscopic resolution and three spatial dimensions. Our results illustrate that remotely detected MRI is an effective approach to elucidate flow dynamics in porous materials including bead pack microreactors and chromatography columns.     

A modified fuzzy C‐means algorithm using scale control spatial information for MRI image segmentation in the presence of noise

The fuzzy C‐means (FCM) algorithm does not fully utilize the spatial information for image segmentation and is sensitive to the presence of noise and intensity inhomogeneity in magnetic resonance imaging (MRI) images. The underlying reason is that using a single fuzzy membership function the FCM algorithm cannot properly represent pattern associations to all clusters. In this paper, we present a modified FCM (mFCM) algorithm by incorporating scale control spatial information for segmentation of MRI images in the presence of high levels of noise and intensity inhomogeneity. The algorithm utilizes scale controlled spatial information from the neighbourhood of each pixel under consideration in the form of a probability function. Using this probability function, a local membership function is introduced for each pixel. Finally, new clustering centre and weighted joint membership functions are introduced based on the local membership and global membership functions. The resulting mFCM algorithm is robust to the noise and intensity inhomogeneity in MRI image data and thereby improves the segmentation results. The experimental results on a synthetic image, four volumes of simulated and one volume of real‐patient MRI brain images show that the mFCM algorithm outperforms k‐means, FCM and some other recently proposed FCM‐based algorithms for image segmentation in terms of qualitative and quantitative studies such as cluster validity functions, segmentation accuracy and tissue segmentation accuracy. Copyright © 2015 John Wiley & Sons, Ltd.     

Denoising and multivariate analysis of time‐of‐flight SIMS images

Time‐of‐flight SIMS (ToF‐SIMS) imaging offers a modality for simultaneously visualizing the spatial distribution of different surface species. However, the utility of ToF‐SIMS datasets may be limited by their large size, degraded mass resolution and low ion counts per pixel. Through denoising and multivariate image analysis, regions of similar chemistries may be differentiated more readily in ToF‐SIMS image data. Three established denoising algorithms—down‐binning, boxcar and wavelet filtering—were applied to ToF‐SIMS images of different surface geometries and chemistries. The effect of these filters on the performance of principal component analysis (PCA) was evaluated in terms of the capture of important chemical image features in the principal component score images, the quality of the principal component score images and the ability of the principal components to explain the chemistries responsible for the image contrast. All filtering methods were found to improve the performance of PCA for all image datasets studied by improving capture of image features and producing principal component score images of higher quality than the unfiltered ion images. The loadings for filtered and unfiltered PCA models described the regions of chemical contrast by identifying peaks defining the regions of different surface chemistry. Down‐binning the images to increase pixel size and signal was the most effective technique to improve PCA performance. Copyright © 2003 John Wiley & Sons, Ltd.     

A new sample substrate for imaging and correlating organic and trace metal composition in biological cells and tissues

Many disease processes involve alterations in the chemical makeup of tissue. Synchrotron-based infrared (IR) and X-ray fluorescence (XRF) microscopes are becoming increasingly popular tools for imaging the organic and trace metal compositions of biological materials, respectively, without the need for extrinsic labels or stains. Fourier transform infrared microspectroscopy (FTIRM) provides chemical information on the organic components of a material at a diffraction-limited spatial resolution of 2–10 μm in the mid-infrared region. The synchrotron X-ray fluorescence (SXRF) microprobe is a complementary technique used to probe trace element content in the same systems with a similar spatial resolution. However to be most beneficial, it is important to combine the results from both imaging techniques on a single sample, which requires precise overlap of the IR and X-ray images. In this work, we have developed a sample substrate containing a gold grid pattern on its surface, which can be imaged with both the IR and X-ray microscopes. The substrate consists of a low trace element glass slide that has a gold grid patterned on its surface, where the major and minor parts of the grid contain 25 and 12 nm gold, respectively. This grid pattern can be imaged with the IR microscope because the reflectivity of gold differs as a function of thickness. The pattern can also be imaged with the SXRF microprobe because the Au fluorescence intensity changes with gold thickness. The tissue sample is placed on top of the patterned substrate. The grid pattern’s IR reflectivity image and the gold SXRF image are used as fiducial markers for spatially overlapping the IR and SXRF images from the tissue. Results show that IR and X-ray images can be correlated precisely, with a spatial resolution of less than one pixel (i.e., 2–3 microns). The development of this new tool will be presented along with applications to paraffin-embedded metalloprotein crystals, Alzheimer’s disease, and hair composition.     

On the implementation of spatial constraints in multivariate curve resolution alternating least squares for hyperspectral image analysis

Hyperspectral imaging (HSI) is a method for exploring spatial and spectral information associated with the distribution of the different compounds in a chemical or biological sample. Amongst the multivariate image analysis tools utilized to decompose the raw data into a bilinear model, multivariate curve resolution alternating least squares (MCR‐ALS) can be applied to obtain the distribution maps and pure spectra of the components of the sample image. However, a requirement is to have the data in a two‐way matrix. Thus, a preliminary step consists of unfolding the raw HSI data into a single‐pixel direction. Consequently, through this data manipulation, the information regarding pixel neighboring is lost, and spatial information cannot directly be constrained on the component profiles in the current MCR‐ALS algorithm. In this short communication, we propose an adaptation of the MCR‐ALS framework, enabling the potential implementation of any variation of spatial constraint. This can be achieved by adding, at each least‐squares step, refolding/unfolding of the distribution maps for the components. The implementation of segmentation, shape smoothness, and image modeling as spatial constraints is proposed as a proof of concept. Copyright © 2015 John Wiley & Sons, Ltd.     

Application of Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) to remote sensing hyperspectral imaging

The application of the MCR-ALS method is demonstrated on two simulated remote sensing spectroscopic images and on one experimental reference remote sensing spectroscopic image obtained by the Airborn Visible/Infrared Imaging Spectrometer (AVIRIS). By application of MCR-ALS, the spectra signatures of the pure constituents present in the image and their concentration distribution at a pixel level are estimated. Results obtained by MCR-ALS are compared to those obtained by other methods frequently used in the remote sensing spectroscopic imaging field like VCA and MVSA. In the case of the analysis of the experimental data set, the resolved pure spectra signatures were compared to reference spectra from USGS library for their identification. In all cases, results were also evaluated for the presence of rotational ambiguities using the MCR-BANDS method. The obtained results confirmed that the MCR-ALS method can be successfully used for remote sensing hyperspectral image resolution purposes. However, the amount of rotation ambiguity still present in the solutions obtained by this and other resolution methods (like VCA or MVSA) can still be large and it should be evaluated with care, trying to reduce its effects by selecting the more appropriate constraints. Only in this way it is possible to increase the reliability of the solutions provided by these methods and decrease the uncertainties associated to their use.     

A Markov random field based approach to the identification of meat and bone meal in feed by near-infrared spectroscopic imaging

Contaminated meat and bone meal (MBM) in animal feedstuff has been the source of bovine spongiform encephalopathy (BSE) disease in cattle, leading to a ban in its use, so methods for its detection are essential. In this study, five pure feed and five pure MBM samples were used to prepare two sets of sample arrangements: set A for investigating the discrimination of individual feed/MBM particles and set B for larger numbers of overlapping particles. The two sets were used to test a Markov random field (MRF)-based approach. A Fourier transform infrared (FT-IR) imaging system was used for data acquisition. The spatial resolution of the near-infrared (NIR) spectroscopic image was 25 μm?×?25 μm. Each spectrum was the average of 16 scans across the wavenumber range 7,000-4,000 cm^?1, at intervals of 8 cm^?1. This study introduces an innovative approach to analyzing NIR spectroscopic images: an MRF-based approach has been developed using the iterated conditional mode (ICM) algorithm, integrating initial labeling-derived results from support vector machine discriminant analysis (SVMDA) and observation data derived from the results of principal component analysis (PCA). The results showed that MBM covered by feed could be successfully recognized with an overall accuracy of 86.59 % and a Kappa coefficient of 0.68. Compared with conventional methods, the MRF-based approach is capable of extracting spectral information combined with spatial information from NIR spectroscopic images. This new approach enhances the identification of MBM using NIR spectroscopic imaging.

An image analysis suite for spot detection and spot matching in two-dimensional electrophoresis gels

We propose a suite of novel algorithms for image analysis of protein expression images obtained from 2-D electrophoresis. These algorithms are a segmentation algorithm for protein spot identification, and an algorithm for matching protein spots from two corresponding images for differential expression study. The proposed segmentation algorithm employs the watershed transformation, k-means analysis, and distance transform to locate the centroids and to extract the regions of the proteins spots. The proposed spot matching algorithm is an integration of the hierarchical-based and optimization-based methods. The hierarchical method is first used to find corresponding pairs of protein spots satisfying the local cross-correlation and overlapping constraints. The matching energy function based on local structure similarity, image similarity, and spatial constraints is then formulated and optimized. Our new algorithm suite has been extensively tested on synthetic and actual 2-D gel images from various biological experiments, and in quantitative comparisons with ImageMaster2D Platinum the proposed algorithms exhibit better spot detection and spot matching.     

Combined chemical and topographic imaging at atmospheric pressure via microprobe laser desorption/ionization mass spectrometry–atomic force microscopy

The operational characteristics and imaging performance are described for a new instrument comprising an atomic force microscope coupled with a pulsed laser and a linear ion trap mass spectrometer. The operating mode of the atomic force microscope is used to produce topographic surface images having sub‐micrometer spatial and height resolution. Spatially resolved mass spectra of ions, produced from the same surface via microprobe‐mode laser desorption/ionization at atmospheric pressure, are also used to create a 100 × 100 µm chemical image. The effective spatial resolution of the image (～2 µm) was constrained by the limit of detection (estimated to be 10⁹–10¹⁰ molecules) rather than by the diameter of the focused laser spot or the step size of the sample stage. The instrument has the potential to be particularly useful for surface analysis scenarios in which chemical analysis of targeted topographic features is desired; consequently, it should have extensive application in a number of scientific areas. Because the number density of desorbed neutral species in laser desorption/ionization is known to be orders‐of‐magnitude greater than that of ions, it is expected that improvements in imaging performance can be realized by implementation of post‐ionization methods. Published in 2009 by John Wiley & Sons, Ltd.     

Infrared and Raman imaging of biological and biomimetic samples

Established methods for imaging of biological or biomimetic samples, such as fluorescence and optical microscopy, magnetic resonance imaging (MRI), X-ray tomography or positron emission tomography (PET) are currently complemented by infrared (both near-IR and mid-IR) as well as Raman spectroscopic imaging, whether it be on a microscopic or macroscopic scale. These vibrational spectroscopic techniques provide a wealth of information without a priori knowledge of either the spectral data or the composition of the sample. Infrared radiation does not harm the organism, no electric potential needs to be applied, and the measurements are not influenced by electromagnetic fields. In addition, no extrinsic labeling or staining, which may perturb the system under investigation, has to be added. The immense volume of information contained in spectroscopic images requires multivariate analysis methodologies in order to effectively mine the chemical and spatial information contained within the data as well as to analyze a time-series of images in order to reveal the origin of a chemical or biochemical process. The promise and limitations of this new analytical tool are surveyed in this review.     

Nanoscale Chemical Imaging Using Tip‐Enhanced Raman Spectroscopy: A Critical Review

Methods for chemical analysis at the nanometer scale are crucial for understanding and characterizing nanostructures of modern materials and biological systems. Tip‐enhanced Raman spectroscopy (TERS) combines the chemical information provided by Raman spectroscopy with the signal enhancement known from surface‐enhanced Raman scattering (SERS) and the high spatial resolution of atomic force microscopy (AFM) or scanning tunneling microscopy (STM). A metallic or metallized tip is illuminated by a focused laser beam and the resulting strongly enhanced electromagnetic field at the tip apex acts as a highly confined light source for Raman spectroscopic measurements. This Review focuses on the prerequisites for the efficient coupling of light to the tip as well as the shortcomings and pitfalls that have to be considered for TERS imaging, a fascinating but still challenging way to look at the nanoworld. Finally, examples from recent publications have been selected to demonstrate the potential of this technique for chemical imaging with a spatial resolution of approximately 10 nm and sensitivity down to the single‐molecule level for applications ranging from materials sciences to life sciences.     

Chemically resolved solid state NMR images of polymers

Polymers are generally spatially heterogeneous, either in terms of morphology or as blends of various components. Solid state NMR imaging provides a means of characterizing both the chemical/morphological composition and its spatial variation. Here we discuss multiple-pulse line-narrowing approaches to acquiring high resolution NMR images and how these sequences can be modified so as to be sensitive to the chemical composition of a sample.     

Nanometer-scale optical imaging of epitaxially grown GaN and InN islands using apertureless near-field microscopy

Nanometer-scale chemical imaging of epitaxially grown gallium nitride (GaN) and indium nitride (InN) islands is performed using scattering-type apertureless near-field scanning optical microscopy (ANSOM). The scattering of 633 nm laser radiation is modulated by an oscillating metallic probe, and the scattered radiation is detected by homodyne amplification, followed by high-harmonic demodulation, yielding optical near-field scattering maps with a spatial resolution better than 30 nm. The image contrast between InN and GaN, and the tip-sample distance dependence, can be qualitatively explained by a simple dipole-coupling model. The ANSOM images of InN and GaN also show structures that are absent in the topographic counterpart, and these substructures are explained by the variations of the local dielectric environment of InN and GaN.     

Addressing metal centres in supramolecular assemblies

Supramolecular metal ion assemblies are deposited from their solutions onto highly orientated pyrolytic graphite (HOPG) substrates to be imaged by scanning tunnelling microscopy (STM). Since the structural and electronic information of STM measurements are strongly entangled, the spectroscopic interpretation and analysis of the images of such molecular assemblies has proven to be challenging. This tutorial review focuses on a general room temperature scanning tunnelling spectroscopy (STS) protocol, current induced tunnelling spectroscopy (CITS), applied to free-standing 1D and 2D arrangements of supramolecular metal ion assemblies rendering local tunnelling probabilities with submolecular resolution. The size of the investigated molecular assemblies was confirmed by comparison with X-ray crystallographic data, while the consistency of the spectroscopic investigations and of the determined positions of the metal ions within the assemblies was checked by DFT calculations. Due to the genuine level structure of coordinated metal centers, it was possible to map exclusively the position of the coordination bonds in supramolecular transition metal assemblies with submolecular spatial resolution using the CITS technique. CITS might thus constitute an important tool to achieve directed bottom-up construction and controlled manipulation of fully electronically functional, two-dimensional molecular designs.