Hyperspectral Chemical Imaging Research Articles

High-speed scanless entire bandwidth mid-infrared chemical imaging

Mid-infrared spectroscopy probes molecular vibrations to identify chemical species and functional groups. Therefore, mid-infrared hyperspectral imaging is one of the most powerful and promising candidates for chemical imaging using optical methods. Yet high-speed and entire bandwidth mid-infrared hyperspectral imaging has not been realized. Here we report a mid-infrared hyperspectral chemical imaging technique that uses chirped pulse upconversion of sub-cycle pulses at the image plane. This technique offers a lateral resolution of 15 µm, and the field of view is adjustable between 800 µm × 600 µm to 12 mm × 9 mm. The hyperspectral imaging produces a 640 × 480 pixel image in 8 s, which covers a spectral range of 640–3015 cm–1, comprising 1069 wavelength points and offering a wavenumber resolution of 2.6–3.7 cm–1. For discrete frequency mid-infrared imaging, the measurement speed reaches a frame rate of 5 kHz, the repetition rate of the laser. As a demonstration, we effectively identified and mapped different components in a microfluidic device, plant cell, and mouse embryo section. The great capacity and latent force of this technique in chemical imaging promise to be applied to many fields such as chemical analysis, biology, and medicine.

Use of hyperspectral chemical imaging to determine the age of milled rice post harvest

The main goal of this study was to predict the age-after-harvest of milled rice and classify it for stale or fresh rice during storage by determining the thiobarbituric acid (TBA) value non-destructively via a hyperspectral imaging (HSI). Thai jasmine rice (KDML 105 variety) was stored at 25°C, 35°C, and 50°C and randomly sampled every month for 12 months for TBA testing (for 4 months at 50°C). During storage, the chemical analysis value of TBA increased over the storage time at all storage temperatures. Hyperspectral imaging in the range 864–1695 nm was used, and partial least squares regression was used to develop multivariate calibration models. The resulting prediction model could approximate quantitative values for TBA with a ratio of performance to the deviation at 2.0 and the root mean square error of prediction of 3.20 μmol MDA/kg. Partial least squares discriminant analysis was conducted for quality analysis based on the TBA value. The age-after-harvest prediction model and the model for classifying stale or fresh rice effectively performed on milled rice, providing a total cross-validation accuracy of 98% and 100%, respectively.

Hyperspectral Chemical Imaging of Single Bacterial Cell Structure by Raman Spectroscopy and Machine Learning

In this work, biomolecules, such as membrane proteins, lipids, and DNA, were identified and their spatial distribution was mapped within a single Escherichia coli cell by Raman hyperspectral imaging. Raman spectroscopy allows direct, nondestructive, rapid, and cost-effective analysis of biological samples, minimizing the sample preparation and without the need of chemical label or immunological staining. Firstly, a comparison between an air-dried and a freeze-dried cell was made, and the principal vibrational modes associated to the membrane and nucleic acids were identified by the bacterium’s Raman chemical fingerprint. Then, analyzing the Raman hyperspectral images by multivariate statistical analysis, the bacterium biological status was investigated at a subcellular level. Principal components analysis (PCA) was applied for dimensionality reduction of the spectral data, then spectral unmixing was performed by multivariate curve resolution–alternating least squares (MCR-ALS). Thanks to multivariate data analysis, the DNA segregation and the Z-ring formation of a replicating bacterial cell were detected at a sub-micrometer level, opening the way to real-time molecular analysis that could be easily applied on in vivo or ex vivo biological samples, avoiding long preparation and analysis process.

Total lipid prediction in single intact cocoa beans by hyperspectral chemical imaging

This work aimed to explore the possibility of predicting total fat content in whole dried cocoa beans at a single bean level using hyperspectral imaging (HSI).170 beans randomly selected from 17 batches were individually analysed by HSI and by reference methodology for fat quantification. Both whole (i.e. in-shell) beans and shelled seeds (cotyledons) were analysed. Partial Least Square (PLS) regression models showed good performance for single shelled beans (R2 = 0.84, external prediction error of 2.4%). For both in-shell beans a slightly lower prediction error of 4.0% and R2 = 0.52 was achieved, but fat content estimation is still of interest given its wide range. Beans were manually segregated, demonstrating an increase by up to 6% in the fat content of sub-fractions.HSI was shown to be a valuable technique for rapid, non-contact prediction of fat content in cocoa beans even from scans of unshelled beans, enabling significant practical benefits to the food industry for quality control purposes and for obtaining a more consistent raw material.

Correlative infrared optical coherence tomography and hyperspectral chemical imaging.

Optical coherence tomography (OCT) is a high-resolution three-dimensional imaging technique that enables nondestructive measurements of surface and subsurface microstructures. Recent developments of OCT operating in the mid-infrared (MIR) range (around 4 µm) lifted fundamental scattering limitations and initiated applied material research in formerly inaccessible fields. The MIR spectral region, however, is also of great interest for spectroscopy and hyperspectral imaging, which allow highly selective and sensitive chemical studies of materials. In this contribution, we introduce an OCT system (dual-band, central wavelengths of 2 µm and 4 µm) combined with MIR spectroscopy that is implemented as a raster scanning chemical imaging modality. The fully integrated and cost-effective optical instrument is based on a single supercontinuum laser source (emission spectrum spanning from 1.1 µm to 4.4 µm). Capabilities of the in situ correlative measurements are experimentally demonstrated by obtaining complex multidimensional material data, comprising morphological and chemical information, from a multilayered composite ceramic-polymer specimen.

Broadband near-infrared hyperspectral single pixel imaging for chemical characterization.

We introduce a compressive sensing based approach for single pixel hyperspectral chemical imaging in a broad spectral range in the near-infrared. Fully integrated MEMS based Fabry-Pérot tunable filter spectrometers and a digital micro-mirror device were employed to achieve spectral and spatial resolution, respectively. The available spectral range from 1500 to 2200 nm covers molecular overtone vibrations enabling chemical identification. Hyperspectral images of different adhesives deposited on a textile were recorded revealing their chemical composition. Furthermore, spectrally resolved near-infrared images with compression rates up to 90% are presented. The approach of single pixel imaging illustrates a promising technology for the infrared spectral range superior to conventionally used costly focal plane arrays.

Identification of Magnesium Oxychloride Cement Biomaterial Heterogeneity using Raman Chemical Mapping and NIR Hyperspectral Chemical Imaging

The present study investigated spatial heterogeneity in magnesium oxychloride cements within a model of a mould using hyperspectral chemical imaging (HCI). The ability to inspect cements within a mould allows for the assessment of material formation in real time in addition to factors affecting ultimate material formation. Both macro scale NIR HCI and micro scale pixel-wise Raman chemical mapping were employed to characterise the same specimens. NIR imaging is rapid, however spectra are often convoluted through the overlapping of overtone peaks, which can make interpretation difficult. Raman spectra are more easily interpretable, however Raman imaging can suffer from slower acquisition times, particularly when the signal to noise ratio is relatively poor and the spatial resolution is high. To overcome the limitations of both, Raman/NIR data fusion techniques were explored and implemented. Spectra collected using both modalities were co-registered and intra and inter-modality peak correlations were investigated while k-means cluster patterns were compared. In addition, partial least squares regression models, built using NIR spectra, predicted chemical-identifying Raman peaks with an R2 of up to >0.98. As macro scale imaging presented greater data collection speeds, chemical prediction maps were built using NIR HCIs.

Hyperspectral chemical imaging reveals spatially varied degradation of polycarbonate urethane (PCU) biomaterials

Hyperspectral chemical imaging (HCI) is an emerging technique which combines spectroscopy with imaging. Unlike traditional point spectroscopy, which is used in the majority of polymer biomaterial degradation studies, HCI enables the acquisition of spatially localised spectra across the surface of a material in an objective manner. Here, we demonstrate that attenuated total reflectance Fourier transform infra-red (ATR-FTIR) HCI reveals spatial variation in the degradation of implantable polycarbonate urethane (PCU) biomaterials. It is also shown that HCI can detect possible defects in biomaterial formulation or specimen production; these spatially resolved images reveal regional or scattered spatial heterogeneity. Further, we demonstrate a map sampling method, which can be used in time-sensitive scenarios, allowing for the investigation of degradation across a larger component or component area. Unlike imaging, mapping does not produce a contiguous image, yet grants an insight into the spatial heterogeneity of the biomaterial across a larger area. These novel applications of HCI demonstrate its ability to assist in the detection of defective manufacturing components and lead to a deeper understanding of how a biomaterial’s chemical structure changes due to implantation. Statement of significanceThe human body is an aggressive environment for implantable devices and their biomaterial components. Polycarbonate urethane (PCU) biomaterials in particular were investigated in this study. Traditionally one or a few points on the PCU surface are analysed using ATR-FTIR spectroscopy. However the selection of acquisition points is susceptible to operator bias and critical information can be lost. This study utilises hyperspectral chemical imaging (HCI) to demonstrate that the degradation of a biomaterial varies spatially. Further, HCI revealed spatial variations of biomaterials that were not subjected to oxidative degradation leading to the possibility of HCI being used in the assessment of biomaterial formulation and/or component production.

Chemometric methods applied in the image plane to correct striping noise in hyperspectral chemical images of biomaterials

AbstractArray detectors improve data collection speed in hyperspectral chemical imaging, yet are prone to striping noise patterns in the image plane, which is difficult to remove. This type of noise affects spectral features and disturbs visual impression of the scene. We found that this type of noise depends on the material composition and setting parameters, ie, pixel size, and it also varies in accordance with the signal intensity of the observed wavelength. To address this, we proposed a new correction method on the basis of the application of chemometric techniques in the image plane of each wavelength. To verify the effectiveness of this method, infrared transmission images of the 2″ × 2″ positive, 1951 USAF Hi‐Resolution Target and biomaterial samples were obtained with a 16‐element (8 × 2) pixel array detector. Point detector images of some samples were also acquired and used as reference images. The proposed correction method produced substantial improvements in the visual impression of intensity images. Principal components analysis was performed to inspect spectral changes after preprocessing, and the results suggested that the major spectral features were not altered while the stripes on intensity images were removed. Spectral profiles and principal components analysis loadings inspection confirmed the smoothing ability of this correction method. As traditional preprocessing techniques, standard normal variate and derivative transformation were not able to remove line artifacts, especially on the biomaterial images. Overall, the proposed method was effective for removing striping noise patterns from infrared images with a minimal alteration of the valuable hyperspectral image information.

Time series hyperspectral chemical imaging (HCI) for investigation of spectral variations associated with water and plasticizers in casein based biopolymers

This present work aims to provide new insights into the spectral variations associated with water and plasticizers in the protein matrix of casein based biopolymers by conducting time series hyperspectral chemical imaging (HCI). Near infrared (NIR) hyperspectral images (880–1720 nm) of sodium caseinate film were acquired at different periods during film forming process. Second derivative spectra revealed a red shift (from lower to higher wavelength) in the region of 1410–1492 nm during drying, indicating that hydrogen bond distribution converted from free toward associated hydroxyl groups. The introduction of plasticizers resulted in a strong hydrogen bonding matrix in the caseinate polymeric film as evidenced by analysis of second derivative spectra and curve fitting results. Moisture content (MC) of film samples were predicted with coefficient of determination in prediction (RPred2) of 0.96 and 0.97, and root mean square error of prediction (RMSEP) of 0.067 and 0.056 by conducting partial least squares (PLS) and support vector machine (SVM), respectively. SVM model was further used to visualize water distribution within the film samples over drying time and the time-evolution of water migration into a dried film. A novel time series spectral angle mapper (SAM) was also proposed to elucidate water penetration pattern in film material. Time series principal components analysis (PCA) and K-means analysis exhibited temporal variation of film samples during drying steps of film formation. The results clearly demonstrated the occurrence of strong hydrogen-bonded aggregates in the film samples, which was more likely due to the congregation of redundant plasticizers. The overall results confirmed that time series NIR HCI is a powerful and versatile tool to investigate water and plasticizer interactions in the protein-based polymeric film.

Nonnegative Matrix Factorization Using Nonnegative Polynomial Approximations

Nonnegative matrix factorization is a key tool in many data analysis applications such as feature extraction, compression, and noise filtering. Many existing algorithms impose additional constraints to take into account prior knowledge and to improve the physical interpretation. This letter proposes a novel algorithm for nonnegative matrix factorization, in which the factors are modeled by nonnegative polynomials. Using a parametric representation of finite-interval nonnegative polynomials, we obtain an optimization problem without external nonnegativity constraints, which can be solved using conventional quasi-Newton or nonlinear least-squares methods. The polynomial model guarantees smooth solutions and may realize a noise reduction. A dedicated orthogonal compression enables a significant reduction of the matrix dimensions, without sacrificing accuracy. The overall approach scales well to large matrices. The approach is illustrated with applications in hyperspectral imaging and chemical shift brain imaging.

Evaluation of Hyperspectral Imaging Visible/Near Infrared Spectroscopy as a forensic tool for automotive paint distinction

Spectroscopies have been widely used in forensic science to analyze fingerprints, bloodstains, paints, etc. Hyperspectral chemical imaging is a fast and non-destructive method that provides spatial and spectral chemical information of a particular specimen. Recently, researchers have evaluated the characterization of automotive paints using Principal Components Analysis (PCA) performed on infrared and Raman spectroscopies data. However, to date, there is no report concerning the discrimination power of such trace evidence using Hyperspectral Imaging Visible/Near Infrared Spectroscopy (HSI-UV/VIS/NIR). Therefore, this work evaluated the potential of this technique combined with PCA as a forensic approach to discriminate automotive paints. In total, 38 samples from twelve brands and a variety of colors were analyzed. HSI-UV/VIS/NIR data was acquired directly from the paint chip’s surfaces, which includes the basecoat and clear coat layers. PCA was individually performed in distinct color datasets and could discriminate 100% of the white, silver, red and grey samples collected from different brands or from the same manufacturer with different color/shades. Furthermore, the PCA reliability for the identification of similar paint chips was successfully tested using fragments of silver and grey samples collected from different vehicle sources with common brand, color and shade. In agreement with the literature, the automotive black paints presented a much lower discriminating power (62.5%) because the spectra did not provide enough reflectance suitable for differentiation. These new findings support the idea of using HSI-UV/VIS/NIR as a fast, easy and efficient alternative technique to other spectroscopies for accurate forensic assessment of automotive paints.

A novel substrate for multisensor hyperspectral imaging.

The quality of chemical imaging, especially multisensor hyperspectral imaging, strongly depends on sample preparation techniques and instrumental infrastructure but also on the choice of an appropriate imaging substrate. To optimize the combined imaging of Raman microspectroscopy, scanning-electron microscopy and energy-dispersive X-ray spectroscopy, a novel substrate was developed based on sputtering of highly purified aluminium onto classical microscope slides. The novel aluminium substrate overcomes several disadvantages of classical substrates like impurities of the substrate material and contamination of the surface as well as surface roughness and homogeneity. Therefore, it provides excellent conditions for various hyperspectral imaging techniques and enables high-quality multisensor hyperspectral chemical imaging at submicron lateral resolutions.

Assessing Hyperspectral Image Content of Pharmaceutical Products Using the Herfindahl-Hirschman Index and Ripley's K-Functions.

Hyperspectral chemical imaging technologies are frequently applied in the pharmaceutical industry to assess the distribution of ingredients in product intermediates and finished products. This article discusses two recognized measures of spatial uniformity in the context of pharmaceuticals. Synthetic images are used to introduce the utility of Ripley's K-function and the Herfindahl-Hirschman index (HHI) for describing image content. These metrics were applied to a commercial-like product to demonstrate the practical interpretation for product development. The simple approaches presented here offer the possibility of reporting objective measures of intra-tablet compositional uniformity with minimal supervision.

A study on the application of near infrared hyperspectral chemical imaging for monitoring moisture content and water activity in low moisture systems.

Moisture content and water activity are key parameters in predicting the stability of low moisture content products. However, conventional methods for moisture content and water activity determination (e.g., loss on drying method, ‎Karl Fischer titration, dew point method) are time consuming, demand specialized equipment and are not amenable to online processing. For this reason they are typically applied at-line on a limited number of samples. Near infrared hyperspectral chemical imaging is an emerging technique for spatially characterising the spectral properties of samples. Due to the fast acquisition of chemical images, many samples can be evaluated simultaneously, thus providing the potential for online evaluation of samples during processing. In this study, the potential of NIR chemical imaging for predicting the moisture content and water activity of a selection of low moisture content food systems is evaluated.

Skullcap and germander: preventing potential toxicity through the application of hyperspectral imaging and multivariate image analysis as a novel quality control method.

Scutellaria lateriflora (skullcap) is a medicinal herb that has a long history of use in the treatment of ailments such as insomnia and anxiety. Commercial herbal formulations claiming to contain S. laterifolia herba have flooded the consumer markets. However, due to intentional or unintentional adulteration, cases of hepatotoxicity have been reported. Possible adulteration with the potentially hepatotoxic Teucrium spp., T. canadense and T. chamaedrys has been reported. In this study, hyperspectral imaging in combination with multivariate image analysis methods was used to differentiate S. laterifolia, T. canadense, and T. chamaedrys raw materials in a non-destructive manner. Furthermore, the ability to detect adulteration of raw materials using the developed multivariate models was also investigated. Chemical images were captured using a shortwave infrared pushbroom imaging system in the wavelength range 920-2514 nm. Principal component analysis was applied to the images to investigate chemical differences between the species. Partial least squares discriminant analysis was used to model pre-assigned class images, and the classification model predicted the levels of adulteration in spiked raw materials. UHPLC-MS as an independent analytical technique was used to confirm chemical differences between the three species. The ability of hyperspectral chemical imaging as a non-destructive technique in the differentiation of the three species was achieved with three distinct clusters in the score scatter plot. A 92.3 % variation in modelled data using PC1 and PC2 was correlated to chemical differences between the three species. Near infrared signals in the regions 1924 nm and 2092 nm (positive P1), 1993 nm and 2186 nm (negative P1), 1918 nm, 2092 nm, and 2266 nm (positive P2), as well as 1993 nm and 2303 nm (negative P2) were identified as containing discriminating information using the loadings line plots. Chemical imaging of spiked samples showed spatial orientation of contaminants within the powdered samples, and percentage adulteration was accurately predicted at levels ≥ 40 % adulteration based on pixel abundance.

Analysis of collagen preservation in bones recovered in archaeological contexts using NIR Hyperspectral Imaging

The scope of this article is to propose an innovative method based on Near Infrared Hyperspectral Chemical Imaging (NIR-HCI) to rapidly and non-destructively evaluate the relative degree of collagen preservation in bones recovered from archaeological contexts. This preliminary study has allowed the evaluation of the potential of the method using bone samples from the Early Upper Palaeolithic, Mesolithic and Neolithic periods at the site of Trou Al׳Wesse in Belgium. NIR-HCI, combined with chemometric tools, has identified specific spectral bands characteristic of collagen. A chemometric model has been built using Partial Least Squares Discriminant Analysis (PLS-DA) to identify bones with and without collagen. This enables the evaluation of the degree of collagen preservation and homogeneity in bones within and between different strata, which has direct implications for archaeological applications (e.g., taphonomic analyses, assemblage integrity) and sample selection for subsequent analyses requiring collagen. Two archaeological applications are presented: comparison between sub-layers in an Early Upper Palaeolithic unit, and evaluation of the range of variability in collagen preservation within a single Holocene stratum.

Time series hyperspectral chemical imaging data: Challenges, solutions and applications

Hyperspectral chemical imaging (HCI) integrates imaging and spectroscopy resulting in three-dimensional data structures, hypercubes, with two spatial and one wavelength dimension. Each spatial image pixel in a hypercube contains a spectrum with >100 datapoints. While HCI facilitates enhanced monitoring of multi-component systems; time series HCI offers the possibility of a more comprehensive understanding of the dynamics of such systems and processes. This implies a need for modeling strategies that can cope with the large multivariate data structures generated in time series HCI experiments. The challenges posed by such data include dimensionality reduction, temporal morphological variation of samples and instrumental drift. This article presents potential solutions to these challenges, including multiway analysis, object tracking, multivariate curve resolution and non-linear regression. Several real world examples of time series HCI data are presented to illustrate the proposed solutions.

Radial tensile strength prediction of relaxing and relaxed compacts by near-infrared chemical imaging

The prediction of radial tensile strength (RTS) of relaxing solid dosage forms by near-infrared hyperspectral chemical imaging was studied. Compacts consisting of starch, lactose, and a mixture of four components were created at different compression forces to develop density models. Predicted density distribution parameters were subsequently used to estimate RTS. Chemical images were collected shortly after compression, repeated every 30 min for 2 h, and a final image was collected after 2 weeks. A two step process, involving first the prediction of compact density at each pixel (using a partial least squares model) and second the relationship between compact density distributions and RTS was implemented. Among the parameters with a significant relationship with RTS, the median of the distribution of density predictions in an image was identified as a robust parameter. Coefficients of determinations for this prediction ranged from 0.96 to 0.99 were obtained with a maximum error in validation of 0.10 MPa for the four-component formulation compacts. The prediction of RTS of fully relaxed compacts from spectral data collected on relaxing compacts was demonstrated. These results demonstrate the potential to use near-infrared chemical imaging in real-time to predict RTS values of fully relaxed compacts.

Implementation of enhanced correlation maps in near infrared chemical images: Application in pharmaceutical research

Recent developments in Hyperspectral Imaging equipment have made possible the use of this analytical technique for fast scanning of sample surfaces. This technique has turned out to be especially useful in Pharmacy, where information about the distribution of the components in the surface of a tablet can be obtained. One particular application of Hyperspectral Chemical Imaging is the search for singularities inside pharmaceutical tablets, e.g. coating defects. Nevertheless, one problem has to be faced: how to analyze a sample without any previous knowledge about it, or having only the minimum information about the tablet.In this work a new methodology, based on correlation coefficients, is introduced to obtain valuable information about one Hyperspectral Image (detection of defects, punctual contaminants, etc.) without any previous knowledge. The methodology combines Principal Component Analysis (PCA), correlation coefficient between one specific pixel included in the image and the rest of the image; and a new enhanced contrast function to obtain more selective chemical and spatial information about the image. To illustrate the applicability of the proposed methodology, real tablets of ibuprofen have been studied.The proposed methodology is presented as a control technique to detect batch variability, defects in final tablets and punctual contaminants, being a potential supplementary tool for quality controls. In addition, the usefulness of the proposed methodology is not exclusive to NIR-CI devices, but to any hyperspectral and multivariate image system.