Oxidative weathering of pyrite in acidic environments: Data-driven experimental evaluation coupled with Raman hyperspectral imaging.

This study deals with the identification of the factors that affect pyrite oxidation in acid mine drainage conditions. For this scope, weathering experiments have been carried at laboratory scale based on the design of experiments methodology to evaluate the effect of factors such as major ion concentrations, crystal size, and humic acids presence over the amount of elemental sulfur produced due to the involved weathering reactions. In particular, metal and anionic concentrations in solution were quantified by inductively coupled plasma-atomic emission spectroscopy and ion-chromatography techniques, respectively, whereas the amount of elemental sulfur was quantified with a high-performance liquid chromatography with diode-array detection technique after proper extraction procedure. A partial least squares regression was calculated to establish a quantitative relationship between the considered factors and the amount of elemental sulfur. After evaluation of the model, ferric iron, crystal size and the presence of humic acids were identified as the relevant factors for pyrite oxidation under acidic conditions. In addition, the surface of the samples was characterized by Raman imaging spectroscopy and subsequently analyzed by explorative hyperspectral analysis methods to assess the spatial distribution of the elemental sulfur as the main weathering product, resulting in a homogenous distribution.

Stochastic image spectroscopy: a discriminative generative approach to hyperspectral image modelling and classification.

This paper introduces a new latent variable probabilistic framework for representing spectral data of high spatial and spectral dimensionality, such as hyperspectral images. We use a generative Bayesian model to represent the image formation process and provide interpretable and efficient inference and learning methods. Surprisingly, our approach can be implemented with simple tools and does not require extensive training data, detailed pixel-by-pixel labeling, or significant computational resources. Numerous experiments with simulated data and real benchmark scenarios show encouraging image classification performance. These results validate the unique ability of our framework to discriminate complex hyperspectral images, irrespective of the presence of highly discriminative spectral signatures.

iVision HHID: Handwritten hyperspectral images dataset for benchmarking hyperspectral imaging-based document forensic analysis.

This article presents a dataset of hyperspectral images of handwriting samples collected from 54 individuals. The purpose of the presented dataset is to further explore the use of hyperspectral imaging in document image analysis and to benchmark the performance of forensic analysis methods for hyperspectral document images. Each hyperspectral cube in the dataset has a spatial resolution of 512 × 650 pixels and contains 149 spectral channels in the spectral range of 478-901 nm. All the individuals have different personalities and have their writing patterns. The information of age and gender of each individual is collected. Each subject has written twenty-eight sentences using 12 different varieties of pens from different brands in blue color, each approximately 9 words or 33 characters long, all English alphabets in capital and small cases, digits from 0 to 9. The previous methods use synthetic mixed samples created by joining different parts of the images from the UWA WIHSI dataset.Each document consists of real mixed samples written withdifferent pens and by different writers with a variety of mixing ratios of inks and writers for forensic analysis.The standard A4 pages, each weighing 70 gs and manufactured by "AA" company, are used for data collection. The handwritten notes written by each subject with different pens are annotated in rectangular boxes. This dataset can be used for several tasks related to hyperspectral document image analysis and document forensic analysis including, handwritten optical character recognition, ink mismatch detection, writer identification at sentence, word, and character-level, handwriting-based gender classification, handwriting-based age prediction, handwritten word segmentation, and word generation. This dataset was designed and collected by the research team at the Artificial intelligence and Computer Vision Lab (iVision), Institute of Space Technology, Pakistan, and the hyperspectral images were acquired through imaging spectroscopy in the visible wavelength range at Wageningen University & Research, the Netherlands.

Recent Advances in Multi- and Hyperspectral Image Analysis.

Current advancements in sensor technology bring new possibilities in multi- and hyperspectral imaging. Real-life use cases which can benefit from such imagery span across various domains, including precision agriculture, chemistry, biology, medicine, land cover applications, management of natural resources, detecting natural disasters, and more. To extract value from such highly dimensional data capturing up to hundreds of spectral bands in the electromagnetic spectrum, researchers have been developing a range of image processing and machine learning analysis pipelines to process these kind of data as efficiently as possible. To this end, multi- or hyperspectral analysis has bloomed and has become an exciting research area which can enable the faster adoption of this technology in practice, also when such algorithms are deployed in hardware-constrained and extreme execution environments; e.g., on-board imaging satellites.

Multivariate Curve Resolution: 50 years addressing the mixture analysis problem - A review.

Multivariate Curve Resolution (MCR) covers a wide span of algorithms designed to tackle the mixture analysis problem by expressing the original data through a bilinear model of pure component meaningful contributions. Since the seminal work by Lawton and Sylvestre in 1971, MCR methods are dynamically evolving to adapt to a wealth of diverse and demanding scientific scenarios. To do so, essential concepts, such as basic constraints, have been revisited and new modeling tasks, mathematical properties and domain-specific information have been incorporated; the initial underlying bilinear model has evolved into a flexible framework where hybrid bilinear/multilinear models can coexist, the regular data structures have undergone a turn of the screw and incomplete multisets and matrix and tensor combinations can be now analyzed. Back to the fundamentals, the theoretical core of the MCR methodology is deeply understood due to the thorough studies about the ambiguity phenomenon. The adaptation of the method to new analytical measurements and scientific domains is continuous. At this point of the story, MCR can be considered a mature yet lively methodology, where many steps forward can still be taken.

Raman Imaging of Plant Cell Walls.

Raman imaging is a microspectroscopic approach revealing the chemistry and structure of plant cell walls in situ on the micro- and nanoscale. The method is based on the Raman effect (inelastic scattering) that takes place when monochromatic laser light interacts with matter. The scattered light conveys a change in energy that is inherent of the involved molecule vibrations. The Raman spectra are thus characteristic for the chemical structure of the molecules and can be recorded spatially ordered with a lateral resolution of about 300 nm. Based on thousands of acquired Raman spectra, images can be assessed using univariate as well as multivariate data analysis approaches. One advantage compared to staining or labeling techniques is that not only one image is obtained as a result but different components and characteristics can be displayed in several images. Furthermore, as every pixel corresponds to a Raman spectrum, which is a kind of "molecular fingerprint," the imaging results should always be evaluated and further details revealed by analysis (e.g., band assignment) of extracted spectra. In this chapter, the basic theoretical background of the technique and instrumentation are described together with sample preparation requirements and tips for high-quality plant tissue sections and successful Raman measurements. Typical Raman spectra of the different plant cell wall components are shown as well as an exemplified analysis of Raman data acquired on the model plant Arabidopsis. Important preprocessing methods of the spectra are included as well as single component image generation (univariate) and spectral unmixing by means of multivariate approaches (e.g., vertex component analysis).

Biomedical perfluorohexane-loaded nanocapsules prepared by low-energy emulsification and selective solvent diffusion.

Perfluorohexane-loaded nanocapsules are interesting materials for many biomedical applications such as oxygen delivery systems or contrast agents. However, their formulation into stable colloidal systems is challenging because of their hydro- and lipophobicity, high density and high vapour pressure. In this study, perfluorohexane-loaded polymeric nanocapsules are prepared for the first time by low-energy emulsification and selective solvent diffusion. The colloidal stability of the perfluorohexane nano-emulsion templates has been improved by the incorporation of an apolar low-density oil (isopropyl myristate) in the dispersed phase, thus addressing droplet coarsening and migration phenomena. The perfluorohexane-loaded nanocapsules prepared from the nano-emulsions show sizes smaller than the corresponding emulsion templates (below 150 nm by dynamic light scattering) and exhibit good stability under storage conditions. Hyperspectral enhanced dark field microscopy revealed a layered core/shell structure and allowed also to confirm the encapsulation of perfluorohexane which was quantified by elemental microanalysis. Although isopropyl myristate has an unfavourable biocompatibility profile, cell viability is enhanced when perfluorohexane is present in the nanocapsules, which is attributed to its high oxygen transport capacity.

Application of machine learning techniques to electron microscopic/spectroscopic image data analysis.

The combination of scanning transmission electron microscopy (STEM) with analytical instruments has become one of the most indispensable analytical tools in materials science. A set of microscopic image/spectral intensities collected from many sampling points in a region of interest, in which multiple physical/chemical components may be spatially and spectrally entangled, could be expected to be a rich source of information about a material. To unfold such an entangled image comprising information and spectral features into its individual pure components would necessitate the use of statistical treatment based on informatics and statistics. These computer-aided schemes or techniques are referred to as multivariate curve resolution, blind source separation or hyperspectral image analysis, depending on their application fields, and are classified as a subset of machine learning. In this review, we introduce non-negative matrix factorization, one of these unfolding techniques, to solve a wide variety of problems associated with the analysis of materials, particularly those related to STEM, electron energy-loss spectroscopy and energy-dispersive X-ray spectroscopy. This review, which commences with the description of the basic concept, the advantages and drawbacks of the technique, presents several additional strategies to overcome existing problems and their extensions to more general tensor decomposition schemes for further flexible applications are described.

Efficient quantitative hyperspectral image unmixing method for large-scale Raman micro-spectroscopy data analysis.

Vibrational micro-spectroscopy is a powerful optical tool, providing a non-invasive label-free chemically specific imaging for many chemical and biomedical applications. However, hyperspectral image produced by Raman micro-spectroscopy typically consists of thousands discrete pixel points, each having individual Raman spectrum at thousand wavenumbers, and therefore requires appropriate image unmixing computational methods to retrieve non-negative spatial concentration and corresponding non-negative spectra of the image biochemical constituents. Here, we present a new efficient Quantitative Hyperspectral Image Unmixing (Q-HIU) method for large-scale Raman micro-spectroscopy data analysis. This method enables to simultaneously analyse multi-set Raman hyperspectral images in three steps: (i) Singular Value Decomposition with innovative Automatic Divisive Correlation which autonomously filters spatially and spectrally uncorrelated noise from data; (ii) a robust subtraction of fluorescent background from the data using a newly developed algorithm called Bottom Gaussian Fitting; (iii) an efficient Quantitative Unsupervised/Partially Supervised Non-negative Matrix Factorization method, which rigorously retrieves non-negative spatial concentration maps and spectral profiles of the samples' biochemical constituents with no a priori information or when one or several samples' constituents are known. As compared with state-of-the-art methods, our approach allows to achieve significantly more accurate results and efficient quantification with several orders of magnitude shorter computational time as verified on both artificial and real experimental data. We apply Q-HIU to the analysis of large-scale Raman hyperspectral images of human atherosclerotic aortic tissues and our results show a proof-of-principle for the proposed method to retrieve and quantify the biochemical composition of the tissues, consisting of both high and low concentrated compounds. Along with the established hallmarks of atherosclerosis including cholesterol/cholesterol ester, triglyceride and calcium hydroxyapatite crystals, our Q-HIU allowed to identify the significant accumulations of oxidatively modified lipids co-localizing with the atherosclerotic plaque lesions in the aortic tissues, possibly reflecting the persistent presence of inflammation and oxidative damage in these regions, which are in turn able to promote the disease pathology. For minor chemical components in the diseased tissues, our Q-HIU was able to detect the signatures of calcium hydroxyapatite and ß-carotene with relative mean Raman concentrations as low as 0.09% and 0.04% from the original Raman intensity matrix with noise and fluorescent background contributions of 3% and 94%, respectively.

Application of a sparseness constraint in multivariate curve resolution - Alternating least squares.

The use of sparseness in chemometrics is a concept that has increased in popularity. The advantage is, above all, a better interpretability of the results obtained. In this work, sparseness is implemented as a constraint in multivariate curve resolution - alternating least squares (MCR-ALS), which aims at reproducing raw (mixed) data by a bilinear model of chemically meaningful profiles. In many cases, the mixed raw data analyzed are not sparse by nature, but their decomposition profiles can be, as it is the case in some instrumental responses, such as mass spectra, or in concentration profiles linked to scattered distribution maps of powdered samples in hyperspectral images. To induce sparseness in the constrained profiles, one-dimensional and/or two-dimensional numerical arrays can be fitted using a basis of Gaussian functions with a penalty on the coefficients. In this work, a least squares regression framework with L0-norm penalty is applied. This L0-norm penalty constrains the number of non-null coefficients in the fit of the array constrained without having an a priori on the number and their positions. It has been shown that the sparseness constraint induces the suppression of values linked to uninformative channels and noise in MS spectra and improves the location of scattered compounds in distribution maps, resulting in a better interpretability of the constrained profiles. An additional benefit of the sparseness constraint is a lower ambiguity in the bilinear model, since the major presence of null coefficients in the constrained profiles also helps to limit the solutions for the profiles in the counterpart matrix of the MCR bilinear model.

Edge-Preserving Image Smoothing Constraint in Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) of Hyperspectral Data.

This article explores smoothing with edge-preserving properties as a spatial constraint for the resolution of hyperspectral images with multivariate curve resolution-alternating least squares (MCR-ALS). For each constrained component image (distribution map), irrelevant spatial details and noise are smoothed applying an L1- or L0-norm penalized least squares regression, highlighting in this way big changes in intensity of adjacent pixels. The feasibility of the constraint is demonstrated on three different case studies, in which the objects under investigation are spatially clearly defined, but have significant spectral overlap. This spectral overlap is detrimental for obtaining a good resolution and additional spatial information should be provided. The final results show that the spatial constraint enables better image (map) abstraction, artifact removal, and better interpretation of the results obtained, compared to a classical MCR-ALS analysis of hyperspectral images.

Fourier Transform Infrared (FT-IR) and Laser Ablation Inductively Coupled Plasma-Mass Spectrometry (LA-ICP-MS) Imaging of Cerebral Ischemia: Combined Analysis of Rat Brain Thin Cuts Toward Improved Tissue Classification.

Microspectroscopic techniques are widely used to complement histological studies. Due to recent developments in the field of chemical imaging, combined chemical analysis has become attractive. This technique facilitates a deepened analysis compared to single techniques or side-by-side analysis. In this study, rat brains harvested one week after induction of photothrombotic stroke were investigated. Adjacent thin cuts from rats' brains were imaged using Fourier transform infrared (FT-IR) microspectroscopy and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). The LA-ICP-MS data were normalized using an internal standard (a thin gold layer). The acquired hyperspectral data cubes were fused and subjected to multivariate analysis. Brain regions affected by stroke as well as unaffected gray and white matter were identified and classified using a model based on either partial least squares discriminant analysis (PLS-DA) or random decision forest (RDF) algorithms. The RDF algorithm demonstrated the best results for classification. Improved classification was observed in the case of fused data in comparison to individual data sets (either FT-IR or LA-ICP-MS). Variable importance analysis demonstrated that both molecular and elemental content contribute to the improved RDF classification. Univariate spectral analysis identified biochemical properties of the assigned tissue types. Classification of multisensor hyperspectral data sets using an RDF algorithm allows access to a novel and in-depth understanding of biochemical processes and solid chemical allocation of different brain regions.

Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress.

This review explores how imaging techniques are being developed with a focus on deployment for crop monitoring methods. Imaging applications are discussed in relation to both field and glasshouse-based plants, and techniques are sectioned into 'healthy and diseased plant classification' with an emphasis on classification accuracy, early detection of stress, and disease severity. A central focus of the review is the use of hyperspectral imaging and how this is being utilised to find additional information about plant health, and the ability to predict onset of disease. A summary of techniques used to detect biotic and abiotic stress in plants is presented, including the level of accuracy associated with each method.

Measurement of ripening of raspberries (Rubus idaeus L) by near infrared and colorimetric imaging techniques.

This work includes the evaluation of 168 samples of raspberries 'Glen Lyon', representing whole maturation period, by colorimetric and near infrared imaging techniques, as well as the quantification of total phenols, total anthocyanins and antioxidant activity by chemical methods. Samples showed significant differences depending on the maturation stage using CIELAB colour parameters and total anthocyanins content. The application of partial least squares regression allowed predicting the chemical features from image analysis data, with coefficients of determination (R2) up to 0.75. The best prediction for total anthocyanins including colorimetric data was observed. The proposed methodology can be used as a reference method for assessing important quality attributes of raspberries. Moreover, it is useful, rapid and accurate automatic inspection method.

Application of Hyperspectral imaging to predict the pH, intramuscular fatty acid content and composition of lamb M. longissimus lumborum at 24h post mortem.

Cost-effective, rapid and objective measurement of lamb quality on a routine basis is an important step for lamb value chains wishing to manage lamb product quality. Hyperspectral imaging (HSI) technology has shown promise as a solution for objective non-invasive prediction of meat quality. The performance of HSI applied 24h post mortem to lamb M. longissimus lumborum (LL) within a processing plant environment was assessed over two sampling years to evaluate its suitability for an objective lamb meat quality assurance tool. Calibration and validation steps were undertaken to evaluate HSI prediction performance for predicting fatty acid content and composition (n=1020 lambs) and pH (n=2406 lambs). Practical considerations of reference meat quality data quality and validation strategies are discussed. HSI can be used to predict meat quality parameters of lamb LL with varying accuracy levels, but ongoing calibration and validation across seasons is required to improve robustness of HSI for objective non-invasive assessment of lamb meat quality.

Short wave infrared chemical imaging as future tool for analysing gunshot residues patterns in targets.

This work used chemical imaging in the short-wave infrared region for analysing gunshot residues (GSR) patterns in cotton fabric targets shot with conventional and non-toxic ammunition. It presents a non-destructive, non-toxic, highly visual and hiperspectral-based approach. The method was based on classical least squares regression, and was tested with the ammunition propellants and their standard components' spectra. The propellants' spectra were satisfactorily used (R2 >0.966, and CorrCoef >0.982) for identifying the GSR irrespective of the type of ammunition used for the shooting. In a more versatile approach, nitrocellulose, the main component in the ammunition propellants, resulted an excellent standard for identifying GSR patterns (R2>0.842, and CorrCoef >0.908). In this case, the propellants' stabilizers (diphenilamine and centralite), and its nitrated derivatives as well as dinitrotoluene, showed also high spectral activity. Therefore, they could be recommended as complementary standards for confirming the GSR identification. These findings establish the proof of concept for a science-based evidence useful to support expert reports and final court rulings. This approach for obtaining GSR patterns can be an excellent alternative to the current and traditional chemical methods, which are based in presumptive and invasive colour tests.

Hyperspectral volumetric coherent anti-Stokes Raman scattering microscopy: quantitative volume determination and NaCl as non-resonant standard.

In this work, we demonstrate quantitative volume determination of chemical components in three dimensions using hyperspectral coherent anti-Stokes Raman scattering microscopy, phase-corrected Kramers-Kronig retrieval of the coherent anti-Stokes Raman scattering susceptibility and factorization into concentration of chemical components. We investigate the influence of the refractive index contrast between water and polymer beads (polystyrene and polymethylmethacrylate), showing that it leads mainly to concentration errors, while the spectral error is less affected. The volume of polystyrene beads of sizes from 200 nm to 3 µm is determined with 10% relative error and 1% absolute error in the region of interest. We furthermore establish the use of sodium chloride as non-resonant reference material free of Raman-active vibrational resonances.

Hyperspectral image analysis for CARS, SRS, and Raman data.

In this work, we have significantly enhanced the capabilities of the hyperspectral image analysis (HIA) first developed by Masia et al. 1 The HIA introduced a method to factorize the hyperspectral data into the product of component concentrations and spectra for quantitative analysis of the chemical composition of the sample. The enhancements shown here comprise (1) a spatial weighting to reduce the spatial variation of the spectral error, which improves the retrieval of the chemical components with significant local but small global concentrations; (2) a new selection criterion for the spectra used when applying sparse sampling2 to speed up sequential hyperspectral imaging; and (3) a filter for outliers in the data using singular value decomposition, suited e.g. to suppress motion artifacts. We demonstrate the enhancements on coherent anti-Stokes Raman scattering, stimulated Raman scattering, and spontaneous Raman data. We provide the HIA software as executable for public use. © 2015 The Authors. Journal of Raman Spectroscopy published by John Wiley & Sons, Ltd.