Critical Evaluation of Spectral Resolution Enhancement Methods for Raman Hyperspectra

Overlapping peaks in Raman spectra complicate the presentation, interpretation, and analyses of complex samples. This is particularly problematic for methods dependent on sparsity such as multivariate curve resolution and other spectral demixing as well as for two-dimensional correlation spectroscopy (2D-COS), multisource correlation analysis, and principal component analysis. Though software-based resolution enhancement methods can be used to counter such problems, their performances often differ, thereby rendering some more suitable than others for specific tasks. Furthermore, there is a need for automated methods to apply to large numbers of varied hyperspectral data sets containing multiple overlapping peaks, and thus methods ideally suitable for diverse tasks. To investigate these issues, we implemented three novel resolution enhancement methods based on pseudospectra, over-deconvolution, and peak fitting to evaluate them along with three extant methods: node narrowing, blind deconvolution, and the general-purpose peak fitting program Fityk. We first applied the methods to varied synthetic spectra, each consisting of nine overlapping Voigt profile peaks. Improved spectral resolution was evaluated based on several criteria including the separation of overlapping peaks and the preservation of true peak intensities in resolution-enhanced spectra. We then investigated the efficacy of these methods to improve the resolution of measured Raman spectra. High resolution spectra of glucose acquired with a narrow spectrometer slit were compared to ones using a wide slit that degraded the spectral resolution. We also determined the effects of the different resolution enhancement methods on 2D-COS and on chemical contrast image generation from mammalian cell spectra. We conclude with a discussion of the particular benefits, drawbacks, and potential of these methods. Our efforts provided insight into the need for effective resolution enhancement approaches, the feasibility of these methods for automation, the nature of the problems currently limiting their use, and in particular those aspects that need improvement.

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How Can We Unravel Complicated near Infrared Spectra?—Recent Progress in Spectral Analysis Methods for Resolution Enhancement and Band Assignments in the near Infrared Region

Journal of Near Infrared Spectroscopy ◽

10.1255/jnirs.295 ◽

2001 ◽

Vol 9 (2) ◽

pp. 63-95 ◽

Cited By ~ 23

Author(s):

Yukihiro Ozaki ◽

Slobodan Šašić ◽

Jian Hui Jiang

Keyword(s):

Spectral Analysis ◽

Recent Progress ◽

Near Infrared ◽

Resolution Enhancement ◽

Chemical Properties ◽

Principal Component ◽

Correlation Spectroscopy ◽

Two Dimensional ◽

Least Squares Regression ◽

Analysis Methods

This review paper reports recent progress in spectral analysis methods for resolution enhancement and band assignments in the near infrared (NIR) region. Spectra in the NIR region are inherently rich with information on the physical and chemical properties of molecules. However, it is not always straightforward to analyse the spectra because an NIR spectrum consists of a number of overlapped bands due to overtones and combination modes. An NIR spectrum may be analysed by conventional spectral analysis methods, chemometrics or two-dimensional correlation spectroscopy. The following conventional methods are currently utilised to analyse NIR spectra: (a) derivatives, (b) difference spectroscopy, (c) Fourier self-deconvolution and (d) curve fitting. The derivative method is powerful in separating superimposed bands and correcting for a baseline slope. Conventional experimental methods for spectral analysis, such as isotope exchange and measurement of polarisation spectra, are also valid in the NIR region. Chemometrics is very useful for extracting information from NIR spectra. Among a variety of chemometrics methods, multiple linear regression, principal component analysis, principal component regression and partial least squares regression are most often used for qualitative and quantitative analysis. Recently, chemometrics has been used for resolution enhancement of NIR spectra. Particularly, loadings plots or regression coefficients are useful for separating overlapped bands and for making band assignments. Notable recent advances in the analysis of NIR spectroscopy are the development or introduction of new spectral analysis methods such as two-dimensional (2D) correlation spectroscopy and self-modelling curve resolution methods (SMCR). 2D correlation analysis enables enhancement of apparent spectral resolution by spreading spectral peaks over a second dimension. SMCR allows one to resolve the experimental matrix into concentration profiles and pure spectra of the involved species without prior knowledge of any of these features.

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Beyond Beer’s Law: Quasi-Ideal Binary Liquid Mixtures

Applied Spectroscopy ◽

10.1177/00037028211056293 ◽

2021 ◽

pp. 000370282110562

Author(s):

Thomas G. Mayerhöfer ◽

Oleksii Ilchenko ◽

Andrii Kutsyk ◽

Jürgen Popp

Keyword(s):

Principal Components ◽

Principal Component ◽

Liquid Mixtures ◽

Attenuated Total Reflection ◽

Correlation Spectroscopy ◽

Multivariate Curve Resolution ◽

Curve Resolution ◽

Benzene Toluene ◽

The Individual ◽

Spectral Ranges

We have recorded attenuated total reflection infrared spectra of binary mixtures in the (quasi-)ideal systems benzene–toluene, benzene–carbon tetrachloride, and benzene–cyclohexane. We used two-dimensional correlation spectroscopy, principal component analysis, and multivariate curve resolution to analyze the data. The 2D correlation proves nonlinearities, also in spectral ranges with no obvious deviations from Beer’s approximation. The number of principal components is much higher than two and multivariate curve resolution carried out under the assumption of the presence of a third component, results in spectra which only show bands of the original components. The results negate the presence of third components, since any complex should have lower symmetry than the individual molecules and thus more and/or different infrared-active bands in the spectra. Based on Lorentz–Lorenz theory and literature values of the optical constants, we show that the nonlinearities and additional principal components are consequences of local field effects and the polarization of matter by light. Lorentz–Lorenz theory is, however, not able to explain, for example, the different blueshifts of the strong A2u band of benzene in the three mixtures. Obviously, infrared spectroscopy is sensitive to the short-range order around the molecules, which changes with content, their shapes, and their anisotropy.

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Time lapse synchrotron IR chemical imaging for observing the acclimation of a single algal cell to CO2 treatment

Scientific Reports ◽

10.1038/s41598-021-92657-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Ghazal Azarfar ◽

Ebrahim Aboualizadeh ◽

Simona Ratti ◽

Camilla Olivieri ◽

Alessandra Norici ◽

...

Keyword(s):

Algal Cell ◽

Principal Component ◽

External Perturbation ◽

Time Lapse ◽

Chemical Imaging ◽

Hyperspectral Data ◽

Aqueous Environment ◽

Ir Absorption ◽

Imaging Data ◽

Mid Infrared

AbstractAlgae are the main primary producers in aquatic environments and therefore of fundamental importance for the global ecosystem. Mid-infrared (IR) microspectroscopy is a non-invasive tool that allows in principle studying chemical composition on a single-cell level. For a long time, however, mid-infrared (IR) imaging of living algal cells in an aqueous environment has been a challenge due to the strong IR absorption of water. In this study, we employed multi-beam synchrotron radiation to measure time-resolved IR hyperspectral images of individual Thalassiosira weissflogii cells in water in the course of acclimation to an abrupt change of CO2 availability (from 390 to 5000 ppm and vice versa) over 75 min. We used a previously developed algorithm to correct sinusoidal interference fringes from IR hyperspectral imaging data. After preprocessing and fringe correction of the hyperspectral data, principal component analysis (PCA) was performed to assess the spatial distribution of organic pools within the algal cells. Through the analysis of 200,000 spectra, we were able to identify compositional modifications associated with CO2 treatment. PCA revealed changes in the carbohydrate pool (1200–950 cm$$^{-1}$$ - 1 ), lipids (1740, 2852, 2922 cm$$^{-1}$$ - 1 ), and nucleic acid (1160 and 1201 cm$$^{-1}$$ - 1 ) as the major response of exposure to elevated CO2 concentrations. Our results show a local metabolism response to this external perturbation.

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Unsupervised Identification of Targeted Spectra Applying Rank1-NMF and FCC Algorithms in Long-Wave Hyperspectral Infrared Imagery

Remote Sensing ◽

10.3390/rs13112125 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2125

Author(s):

Bardia Yousefi ◽

Clemente Ibarra-Castanedo ◽

Martin Chamberland ◽

Xavier P. V. Maldague ◽

Georges Beaudoin

Keyword(s):

Matched Filter ◽

Principal Component ◽

Hyperspectral Data ◽

Clustering Methods ◽

Spectral Angle Mapper ◽

Long Wave ◽

Clustering Approach ◽

Spectral Comparison ◽

Computational Simplicity ◽

Mineral Identification

Clustering methods unequivocally show considerable influence on many recent algorithms and play an important role in hyperspectral data analysis. Here, we challenge the clustering for mineral identification using two different strategies in hyperspectral long wave infrared (LWIR, 7.7–11.8 μm). For that, we compare two algorithms to perform the mineral identification in a unique dataset. The first algorithm uses spectral comparison techniques for all the pixel-spectra and creates RGB false color composites (FCC). Then, a color based clustering is used to group the regions (called FCC-clustering). The second algorithm clusters all the pixel-spectra to directly group the spectra. Then, the first rank of non-negative matrix factorization (NMF) extracts the representative of each cluster and compares results with the spectral library of JPL/NASA. These techniques give the comparison values as features which convert into RGB-FCC as the results (called clustering rank1-NMF). We applied K-means as clustering approach, which can be modified in any other similar clustering approach. The results of the clustering-rank1-NMF algorithm indicate significant computational efficiency (more than 20 times faster than the previous approach) and promising performance for mineral identification having up to 75.8% and 84.8% average accuracies for FCC-clustering and clustering-rank1 NMF algorithms (using spectral angle mapper (SAM)), respectively. Furthermore, several spectral comparison techniques are used also such as adaptive matched subspace detector (AMSD), orthogonal subspace projection (OSP) algorithm, principal component analysis (PCA), local matched filter (PLMF), SAM, and normalized cross correlation (NCC) for both algorithms and most of them show a similar range in accuracy. However, SAM and NCC are preferred due to their computational simplicity. Our algorithms strive to identify eleven different mineral grains (biotite, diopside, epidote, goethite, kyanite, scheelite, smithsonite, tourmaline, pyrope, olivine, and quartz).

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Separation of Chromatographic Co-Eluted Compounds by Clustering and by Functional Data Analysis

Metabolites ◽

10.3390/metabo11040214 ◽

2021 ◽

Vol 11 (4) ◽

pp. 214

Author(s):

Aneta Sawikowska ◽

Anna Piasecka ◽

Piotr Kachlicki ◽

Paweł Krajewski

Keyword(s):

Simulated Data ◽

Principal Component ◽

Real Data ◽

Functional Principal Component Analysis ◽

Additional Advantage ◽

Time Alignment ◽

Peak Separation ◽

Biological Mixtures ◽

Overlapping Peaks ◽

Retention Time Alignment

Peak overlapping is a common problem in chromatography, mainly in the case of complex biological mixtures, i.e., metabolites. Due to the existence of the phenomenon of co-elution of different compounds with similar chromatographic properties, peak separation becomes challenging. In this paper, two computational methods of separating peaks, applied, for the first time, to large chromatographic datasets, are described, compared, and experimentally validated. The methods lead from raw observations to data that can form inputs for statistical analysis. First, in both methods, data are normalized by the mass of sample, the baseline is removed, retention time alignment is conducted, and detection of peaks is performed. Then, in the first method, clustering is used to separate overlapping peaks, whereas in the second method, functional principal component analysis (FPCA) is applied for the same purpose. Simulated data and experimental results are used as examples to present both methods and to compare them. Real data were obtained in a study of metabolomic changes in barley (Hordeum vulgare) leaves under drought stress. The results suggest that both methods are suitable for separation of overlapping peaks, but the additional advantage of the FPCA is the possibility to assess the variability of individual compounds present within the same peaks of different chromatograms.

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Possibility of Human Gender Recognition Using Raman Spectra of Teeth

Molecules ◽

10.3390/molecules26133983 ◽

2021 ◽

Vol 26 (13) ◽

pp. 3983

Author(s):

Ozren Gamulin ◽

Marko Škrabić ◽

Kristina Serec ◽

Matej Par ◽

Marija Baković ◽

...

Keyword(s):

Raman Spectra ◽

Principal Component ◽

Support Vector ◽

Gender Recognition ◽

Proof Of Concept ◽

Male And Female ◽

Tooth Type ◽

Tooth Apex ◽

The Difference

Gender determination of the human remains can be very challenging, especially in the case of incomplete ones. Herein, we report a proof-of-concept experiment where the possibility of gender recognition using Raman spectroscopy of teeth is investigated. Raman spectra were recorded from male and female molars and premolars on two distinct sites, tooth apex and anatomical neck. Recorded spectra were sorted into suitable datasets and initially analyzed with principal component analysis, which showed a distinction between spectra of male and female teeth. Then, reduced datasets with scores of the first 20 principal components were formed and two classification algorithms, support vector machine and artificial neural networks, were applied to form classification models for gender recognition. The obtained results showed that gender recognition with Raman spectra of teeth is possible but strongly depends both on the tooth type and spectrum recording site. The difference in classification accuracy between different tooth types and recording sites are discussed in terms of the molecular structure difference caused by the influence of masticatory loading or gender-dependent life events.

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Hyperspectral Image Classification with Localized Graph Convolutional Filtering

Remote Sensing ◽

10.3390/rs13030526 ◽

2021 ◽

Vol 13 (3) ◽

pp. 526

Author(s):

Shengliang Pu ◽

Yuanfeng Wu ◽

Xu Sun ◽

Xiaotong Sun

Keyword(s):

Hyperspectral Image ◽

Principal Component ◽

Representation Learning ◽

Classification Performance ◽

Hyperspectral Data ◽

Spectral Graph Theory ◽

Feature Reduction ◽

Graph Representation ◽

Novel Method ◽

Local Graph

The nascent graph representation learning has shown superiority for resolving graph data. Compared to conventional convolutional neural networks, graph-based deep learning has the advantages of illustrating class boundaries and modeling feature relationships. Faced with hyperspectral image (HSI) classification, the priority problem might be how to convert hyperspectral data into irregular domains from regular grids. In this regard, we present a novel method that performs the localized graph convolutional filtering on HSIs based on spectral graph theory. First, we conducted principal component analysis (PCA) preprocessing to create localized hyperspectral data cubes with unsupervised feature reduction. These feature cubes combined with localized adjacent matrices were fed into the popular graph convolution network in a standard supervised learning paradigm. Finally, we succeeded in analyzing diversified land covers by considering local graph structure with graph convolutional filtering. Experiments on real hyperspectral datasets demonstrated that the presented method offers promising classification performance compared with other popular competitors.

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Rotational hyperspectral scanner and related image reconstruction algorithm

Scientific Reports ◽

10.1038/s41598-021-82819-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Longqiang Luo ◽

Shuo Li ◽

Xinli Yao ◽

Sailing He

Keyword(s):

Image Reconstruction ◽

Spectral Resolution ◽

Reconstruction Algorithm ◽

Hyperspectral Data ◽

Data Cube ◽

Spatial Accuracy ◽

Scanning Method ◽

Image Reconstruction Algorithm

AbstractWe design and implement a compact and lightweight hyperspectral scanner. Based on this, a novel rotational hyperspectral scanner was demonstrated. Different from translational scanning, rotational scanning is a moveless and stable scanning method. We also designed a relevant image algorithm to reconstruct the image from an angular recorded hyperspectral data cube. The algorithm works well even with uncertain radial and tangential offset, which is caused by mechanical misalignment. The system shown a spectral resolution of 5 nm after calibration. Finally, spatial accuracy and spectral precision were discussed, based on some additional experiments.

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Hyperspectral Data Simulation (Sentinel-2 to AVIRIS-NG) for Improved Wildfire Fuel Mapping, Boreal Alaska

Remote Sensing ◽

10.3390/rs13091693 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1693

Author(s):

Anushree Badola ◽

Santosh K. Panda ◽

Dar A. Roberts ◽

Christine F. Waigl ◽

Uma S. Bhatt ◽

...

Keyword(s):

Land Cover ◽

Spectral Resolution ◽

Hyperspectral Image ◽

Spectral Response ◽

Spectral Characteristics ◽

Hyperspectral Data ◽

Spectral Unmixing ◽

High Spectral Resolution ◽

Fuel Mapping ◽

Sentinel 2

Alaska has witnessed a significant increase in wildfire events in recent decades that have been linked to drier and warmer summers. Forest fuel maps play a vital role in wildfire management and risk assessment. Freely available multispectral datasets are widely used for land use and land cover mapping, but they have limited utility for fuel mapping due to their coarse spectral resolution. Hyperspectral datasets have a high spectral resolution, ideal for detailed fuel mapping, but they are limited and expensive to acquire. This study simulates hyperspectral data from Sentinel-2 multispectral data using the spectral response function of the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor, and normalized ground spectra of gravel, birch, and spruce. We used the Uniform Pattern Decomposition Method (UPDM) for spectral unmixing, which is a sensor-independent method, where each pixel is expressed as the linear sum of standard reference spectra. The simulated hyperspectral data have spectral characteristics of AVIRIS-NG and the reflectance properties of Sentinel-2 data. We validated the simulated spectra by visually and statistically comparing it with real AVIRIS-NG data. We observed a high correlation between the spectra of tree classes collected from AVIRIS-NG and simulated hyperspectral data. Upon performing species level classification, we achieved a classification accuracy of 89% for the simulated hyperspectral data, which is better than the accuracy of Sentinel-2 data (77.8%). We generated a fuel map from the simulated hyperspectral image using the Random Forest classifier. Our study demonstrated that low-cost and high-quality hyperspectral data can be generated from Sentinel-2 data using UPDM for improved land cover and vegetation mapping in the boreal forest.

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Characterization of Pharmaceutical Tablets Using UV Hyperspectral Imaging as a Rapid Line to Line Analysis Tool

Sensors ◽

10.3390/s21134436 ◽

2021 ◽

Vol 21 (13) ◽

pp. 4436

Author(s):

Mohammad Al Ktash ◽

Mona Stefanakis ◽

Barbara Boldrini ◽

Edwin Ostertag ◽

Marc Brecht

Keyword(s):

Hyperspectral Imaging ◽

Large Scale ◽

Solid Phase ◽

Ultra Violet ◽

Principal Component ◽

Ccd Camera ◽

Conveyor Belt ◽

Hyperspectral Data ◽

Analysis Tool ◽

Sample Set

A laboratory prototype for hyperspectral imaging in ultra-violet (UV) region from 225 to 400 nm was developed and used to rapidly characterize active pharmaceutical ingredients (API) in tablets. The APIs are ibuprofen (IBU), acetylsalicylic acid (ASA) and paracetamol (PAR). Two sample sets were used for a comparison purpose. Sample set one comprises tablets of 100% API and sample set two consists of commercially available painkiller tablets. Reference measurements were performed on the pure APIs in liquid solutions (transmission) and in solid phase (reflection) using a commercial UV spectrometer. The spectroscopic part of the prototype is based on a pushbroom imager that contains a spectrograph and charge-coupled device (CCD) camera. The tablets were scanned on a conveyor belt that is positioned inside a tunnel made of polytetrafluoroethylene (PTFE) in order to increase the homogeneity of illumination at the sample position. Principal component analysis (PCA) was used to differentiate the hyperspectral data of the drug samples. The first two PCs are sufficient to completely separate all samples. The rugged design of the prototype opens new possibilities for further development of this technique towards real large-scale application.

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