Now showing 1 - 10 of 14
  • Publication
    Spatial-spectral analysis method using texture features combined with PCA for information extraction in hyperspectral images
    (Wiley, 2020-02) ;
    This work proposes a new method to treat spatial and spectral information interactively. The method extracts spatial features, ie, variogram, gray-level co-occurrence matrix (GLCM), histograms of oriented gradients (HOG), and local binary pattern (LBP) features, from each wavelength image of hypercube and principal component analysis (PCA) is applied on this spatial feature matrix to identify wavelength-dependent variation in spatial patterns. Resultant image is obtained by projecting the score values to the original data. Three datasets, including a synthetic hyperspectral image (Dataset 1), a set of real hyperspectral images of salmon fillets (Dataset 2), and remote-sensing images (Dataset 3), were utilized to evaluate the performance of the proposed method. Results from Dataset 1 showed that the spatial-spectral methods had the potential of reducing baseline offset noise. Dataset 2 revealed that spatial-spectral methods can alleviate noisy pixels with strong signal and reduce shadow effects. In addition, substantial improvements were obtained in case of classification between white stripe and red muscle pixels by using the HOG-based approach with correct classification rate (CCR) of 0.97 compared with the models directly built from raw and standard normal variate (SNV) preprocessed spectra (CCR = 0.94). Samson image of Dataset 3 suggested the flexibility and effectiveness of the proposed method by improving CCR of 0.96 using conventional PCA on SNV pretreated spectra to 0.98 using GLCM-based approach on SNV preprocessed spectra. Overall, experimental results demonstrated that the spatial-spectral methods can improve the results found by using the spectral information alone because of the spatial information provided.
      104Scopus© Citations 16
  • Publication
    FTIR Spectroscopy for Molecular Level Description of Water Vapor Sorption in Two Hydrophobic Polymers
    (IEEE, 2019-09-26) ;
    This work aims to investigate the sorption of water vapor in two hydrophobic polymers, i.e., polytetrafluoroethylene (PTFE) and polyurethane (PU) with water contact of 119 and 104, respectively, by using Fourier transform infrared (FTIR) spectroscopy combined with gravimetric analysis. Sample spectra were measured in a controlled humidity cell. Results demonstrated the absence of water molecules in PTFE after exposure to 100% RH for 2 hours. In contrast, the spectroscopic data provided strong evidence that PU film continued to sorb water as exposure time increased. The interaction between water molecules and PU polymer chain was characterized by the dynamics of hydrogen bonding. The hydrogen bonding between N-H and C=O groups was reduced by the sorbed water, as evidenced by the shift of NH to a higher frequency. Second derivative of difference spectra suggested the presence of multiple water species involved in different types of hydrogen bonding interaction at water/PU interface. Results also revealed that the sorbed water in PU film cannot be eliminated via evaporation at room temperature.
      289Scopus© Citations 2
  • Publication
    Photoinduced Enhanced Raman from Lithium Niobate on Insulator Template
    © 2018 American Chemical Society. Photoinduced enhanced Raman spectroscopy from a lithium niobate on insulator (LNOI)-silver nanoparticle template is demonstrated both by irradiating the template with 254 nm ultraviolet (UV) light before adding an analyte and before placing the substrate in the Raman system (substrate irradiation) and by irradiating the sample in the Raman system after adding the molecule (sample irradiation). The photoinduced enhancement enables up to an ∼sevenfold increase of the surface-enhanced Raman scattering signal strength of an analyte following substrate irradiation, whereas an ∼threefold enhancement above the surface-enhanced signal is obtained for sample irradiation. The photoinduced enhancement relaxes over the course of ∼10 h for a substrate irradiation duration of 150 min before returning to initial signal levels. The increase in Raman scattering intensity following UV irradiation is attributed to photoinduced charge transfer from the LNOI template to the analyte. New Raman bands are observed following UV irradiation, the appearance of which is suggestive of a photocatalytic reaction and highlight the potential of LNOI as a photoactive surface-enhanced Raman spectroscopy substrate.
      341Scopus© Citations 12
  • Publication
    Characterisation of titanium oxide layers using Raman spectroscopy and optical profilometry: Influence of oxide properties
    This study evaluates the use of a combination of Raman spectroscopy and optical profilometry as a surface characterisation technique for the examination of oxide layers grown on titanium metal substrates. The titanium oxide layers with thickness of up to 8 µm, were obtained using a low-pressure oxygen microwave plasma treatment of the titanium metal substrate. The effect of the microwave plasma processing conditions (input power, pressure and treatment time) on the Raman bandwidth, intensity and peak position was evaluated. Also, the effect of these processing conditions on the surface roughness parameters (Sa, Sdq, Ssk and Sku) of the oxide layers was investigated. Analysis of the peak positions of Eg and A1g modes indicated that the effects of input power and chamber pressure was to induce a shift towards the lower frequency with increasing input power and pressure (1–2 kPa). The intensity of the Raman bands was found to be dependent on the morphology and surface chemistry of the oxide layer. The intensity of Raman band (A1g), was found to be particularly influenced by the average surface roughness (Sa) and the crystallite size. Exponential and polynomial relations were found to correlate with these properties. A two-latent variable Partial Least Squares Regression model developed on Raman spectral data could predict surface roughness with a coefficient of determination (R2) of approx. 0.87 when applied to the testing of an independent set of titanium oxide test coatings.
      299Scopus© Citations 60
  • Publication
    Can attenuated total internal reflection-fourier transform infrared be used to understand the interaction between polymers and water? A hyperspectral imaging study
    This study investigates the potential use of attenuated total internal reflection-Fourier transform infrared (ATR-FT-IR) imaging, a hyperspectral imaging modality, to investigate molecular level trends in the interaction of water with polymeric surfaces of varying hydrophobicity. The hydrophobicity of two categories of polymeric biomaterials is characterised using contact angle (CA) measurements and their relationship with the band area of the OH stretching v S vibration of water over time is presented. This is supported with correlations between CA data and single wavenum-ber intensity values (univariate analysis). Multivariate analysis of the spectra captured at the OH stretch for all polymers is carried out using principal component analysis to study the spatial variation in the interaction between the polymeric surfaces and water. Finally, a comparison between the univariate and multivariate strategies is presented to understand the interaction between polymeric biomaterials and water.
      130Scopus© Citations 6
  • Publication
    Chemometric methods applied in the image plane to correct striping noise in hyperspectral chemical images of biomaterials
    Array 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.
      107Scopus© Citations 3
  • Publication
    Tutorial: Time series hyperspectral image analysis
    (NIR Publications, 2016-04-22) ; ;
    A hyperspectral image is a large dataset in which each pixel corresponds to a spectrum, thus providing high-quality detail of a sample surface. Hyperspectral images are thus characterised by dual information, spectral and spatial, which allows for the acquisition of both qualitative and quantitative information from a sample. A hyperspectral image, commonly known as a 'hypercube', comprises two spatial dimensions and one spectral dimension. The data of such a file contain both chemical and physical information. Such files need to be analysed with a computational 'chemometric' approach in order to reduce the dimensionality of the data, while retaining the most useful spectral information. Time series hyperspectral imaging data comprise multiple hypercubes, each presenting the sample at a different time point, requiring additional considerations in the data analysis. This paper provides a step-by-step tutorial for time series hyperspectral data analysis, with detailed command line scripts in the Matlab and R computing languages presented in the supplementary data. The example time series data, available for download, are a set of time series hyperspectral images following the setting of a cement-based biomaterial. Starting from spectral pre-processing (image acquisition, background removal, dead pixels and spikes, masking) and pre-treatments, the typical steps encountered in time series hyperspectral image processing are then presented, including unsupervised and supervised chemometric methods. At the end of the tutorial paper, some general guidelines on hyperspectral image processing are proposed.
      918Scopus© Citations 28
  • Publication
    Vibrational Spectroscopy for Analysis of Water for Human Use and in Aquatic Ecosystems
    Maintaining a clean water supply is one of the key challenges facing humanity today. Pollution, over-use and climate change are just some of the factors putting increased pressure on our limited water resources. Contamination of the water supply presents a high risk to public health, security and the environment; however, no adequate real-time methods exist to detect the wide range of potential contaminants. There is a need for rapid, low cost, multi target systems for water quality monitoring. Information rich techniques such as vibrational spectroscopy have been proposed for this purpose. This review presents developments in the applications of vibrational spectroscopy to water quality monitoring over the past 20 years, identifies emerging technologies and discusses future challenges.
      4348Scopus© Citations 22
  • Publication
    Predictive modelling of the water contact angle of surfaces using attenuated total reflection-Fourier transform infrared (ATR-FTIR) chemical imaging and partial least squares regression (PLSR)
    The static water contact angle (CA) quantifies the degree of wetting that occurs when a surface encounters a liquid, e.g. water. This property is a result of factors such as surface chemistry and local roughness and is an important analytical parameter linked to the suitability of a surface for a given bioanalytical process. Monitoring the spatial variation in wettability over surfaces is increasingly critical to analysts and manufacturers for improved quality control. However, CA acquisition is often time-consuming because it involves measurements over multiple spatial locations, independent sampling and the need for a single instrument operator. Furthermore, surfaces exposed to local environments specific to an intended application may affect the surface chemistry thereby modifying the surface properties. In this study, Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) chemical imaging data acquired from wet and dry polymer surfaces were used to develop multivariate predictive models for CA prediction. Partial Least Squares Regression (PLSR) models were built using IR spectra from surfaces presenting differences in the experimentally measured CA in the range 16°-141°. The best performing PLSR models were locally developed and combined to make a global model utilising wet IR spectra which performed well (R2p = 0.98, RMSECV ∼ 5°) when tested on an independent experimental set. This model was subsequently applied to IR spectra acquired from a surface exhibiting spatial differences in surface chemistry and the CA with a reasonable confidence and precision (prediction error within 10°), demonstrating the potential of this method for prediction of the spatially varying CA as a non-destructive in-line process monitoring technique.
      127Scopus© Citations 6
  • Publication
    Feasibility of attenuated total reflection-fourier transform infrared (ATR-FTIR) chemical imaging and partial least squares regression (PLSR) to predict protein adhesion on polymeric surfaces
    Predicting the degree to which proteins adhere to a polymeric surface is an ongoing challenge in the scientific community to prevent non-specific protein adhesion and drive favourable protein-surface interactions. This work explores the potential of multivariate PLSR modelling in conjunction with Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) chemical imaging to investigate whether experimentally characterised surface chemistry can be used to predict surface protein adhesion. ATR-FTIR spectra were collected on dry and wetted polymeric surfaces, followed by evaluation of adhered fibrinogen on surfaces using the micro bicinchoninic (BCA) protein assay as a reference method. Partial Least Squares Regression (PLSR) models were built using IR spectra as the predictor variable. Overall the models built with 'wetted polymer' IR spectra performed better as compared to the models built using 'dry polymer' IR spectra (average coefficient of determination, R 2P 0.998, 0.996 respectively), with the lowest error in prediction (4 ± 0.6 μg) for ultra-high molecular weight polyethylene (UHMPE) as a test surface. This indicates the potential of this method to predict the degree to which protein adhesion occurs on polymeric surfaces using experimentally determined surface chemistry.
      96Scopus© Citations 5