Multivariate Curve Resolution-alternating Least Squares Analysis Research Articles

Investigation of bread staling by handheld NIR spectroscopy in tandem with 2D-COS and MCR-ALS analysis

The present investigations were carried out using the technique of handheld near-infrared (NIR) spectroscopy for time-dependent diffuse-reflection measurements of the cut face of a fresh baguette. The critical structural factors in the staling process of bread are the crystallization of amylopectin in starch and the decrease of water content by evaporation and diffusion from core to crust. To monitor the synchronicity and sequence of these changes, Two-Dimensional Correlation Spectroscopy (2D-COS) was applied. Due to the significant variation of spectral changes at different stages of the aging process, the spectra were split into two sets of 0 – 6/6 – 48 hr. 2D-COS analysis allowed to differentiate the detailed sequence of the following structural events for these time intervals: crystallization of amylopectin/evaporation of weakly and strongly hydrogen-bonded water/reorganization of starch OH-functionalities. Furthermore, Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) was used to investigate the changes in the spectra profile as a function of aging time.

On-line Raman spectroscopy combined with multivariate curve resolution-alternating least squares (MCR-ALS) to investigate the synthesis mechanism of 3,5-diamino-1,2,4-triazole (DAT)

The precise and accurate synthesis mechanism of typical energetic materials (EMs) intermediate is extremely important for the optimization of synthesis technology of EMs. In this research, on-line Raman spectroscopy technique combined with multivariate curve resolution-alternating least squares(MCR-ALS) method was proposed and used to investigate the synthesis mechanism of EMs intermediate (3,5-diamino-1,2,4-triazole, DAT). Initially, on-line Raman spectroscopy was applied to collect the Raman spectral data of DAT synthesis process. Secondly, principal component analysis (PCA), coupled with singular value decomposition (SVD) were used to determine the number of component of the reaction system and the components was 5. Thirdly, MCR-ALS was used to extract the pure Raman spectra and concentration curves of each substance of DAT synthesis process. During the MCR-ALS operation, evolving factor analysis (EFA) was choose to acquire the initial concentration estimation for MCR-ALS. Several constraints were selected to apply to ALS optimization including non-negative, closure, equality and correlation constraint. And the correlation coefficient between the Raman spectra and the actual Raman spectra of the hydrazine hydrochloride, dicyandiamide and DAT was calculated, their correlation coefficient R2 were 0.9522, 0.9446, 0.9908 respectively which showed a good data fit of MCR-ALS method. Finally, according to the results of MCR-ALS analysis, the structure of the synthetic intermediates was successfully deduced and the mechanism of DAT synthesis was proposed. Hence, a precise and comprehensive method for analyzing the DAT synthesis reaction mechanism is proposed, which is helpful to provide a new idea for the analysis of the synthesis reaction mechanism of energetic materials.

Multivariate Curve Resolution Alternating Least Squares Analysis of In Vivo Skin Raman Spectra

In recent years, Raman spectroscopy has been used to study biological tissues. However, the analysis of experimental Raman spectra is still challenging, since the Raman spectra of most biological tissue components overlap significantly and it is difficult to separate individual components. New methods of analysis are needed that would allow for the decomposition of Raman spectra into components and the evaluation of their contribution. The aim of our work is to study the possibilities of the multivariate curve resolution alternating least squares (MCR-ALS) method for the analysis of skin tissues in vivo. We investigated the Raman spectra of human skin recorded using a portable conventional Raman spectroscopy setup. The MCR-ALS analysis was performed for the Raman spectra of normal skin, keratosis, basal cell carcinoma, malignant melanoma, and pigmented nevus. We obtained spectral profiles corresponding to the contribution of the optical system and skin components: melanin, proteins, lipids, water, etc. The obtained results show that the multivariate curve resolution alternating least squares analysis can provide new information on the biochemical profiles of skin tissues. Such information may be used in medical diagnostics to analyze Raman spectra with a low signal-to-noise ratio, as well as in various fields of science and industry for preprocessing Raman spectra to remove parasitic components.

Detection of bacterial spoilage during wine alcoholic fermentation using ATR-MIR and MCR-ALS

A new methodology is proposed to describe the evolution of the main chemical compounds of grape must during wine alcoholic fermentation using Attenuated Total Reflectance Mid Infrared (ATR-MIR) spectra in combination with Multivariate Curve Resolution Alternating Least-Squares (MCR-ALS). In addition, we have developed a process control strategy to detect differences between fermentations running under Normal Operation Conditions (NOC) and fermentations intentionally spoiled with lactic acid bacteria at the beginning of alcoholic fermentation (MLF) to promote deviations from NOC. MCR-ALS models on these data showed a good data fit (R 2 = 99.95% and lack of fit = 2.31%). It was possible to resolve the spectral profiles of relevant molecules involved in the alcoholic fermentation process, including the one related to bacterial spoilage (lactic acid). MSPC charts were built based on the concentration profiles obtained from the MCR-ALS models and using T 2 and Q statistics. Spoiled wines showed off-limit values for T 2 after 96 hours, making it possible to detect lactic acid bacteria spoilage in early stages of alcoholic fermentation. Thus, the use of ATR-MIR spectra and MCR-ALS analysis shows a great potential for a rapid control of the state of the alcoholic fermentation process, making it possible to early detect the appearance of undesired molecules during the process, which allows the winemaker to apply corrective measures and obtain a good final product. • ATR-MIR and MCR-ALS allow resolving main compounds involved in wine fermentation. • T 2 and Q MSPC charts are built using MCR-ALS concentration profiles. • The concept of ‘Inverse’ MSPC charts' is introduced. • Early detection of undesired molecules related to bacterial spoilage is achieved.

Multivariate chemometric methods and Vis-NIR spectrophotometry for monitoring plutonium-238 anion exchange column effluent in a radiochemical hot cell

The Plutonium-238 Supply Program at Oak Ridge National Laboratory has developed the capability to inform process decisions during full-scale 238Pu anion exchange column runs using multivariate chemometrics and visible-near infrared (Vis-NIR) absorption spectroscopy. Multivariate analytical methods provided a suitable option for real-time analysis of complex neptunium (Np) and plutonium (Pu) absorption spectra. Thousands of spectra from multiple production campaigns were analyzed by principal component analysis and Kennard-Stone sample selection to select a training set composed of 60 spectra. Multivariate curve resolution–alternating least squares analysis (MCR-ALS) identified spectral components and component concentrations. A partial least squares regression (PLSR) model was built using the spectra and concentration values determined by these virtually unsupervised methods. Next, a supervised PLSR model was built by scaling the MCR-ALS–selected concentration matrix using molar epsilon values. Both PLSR models predicted the concentrations of Np(IV), Np(V), Pu(III), Pu(IV), and Pu(VI) in the column effluent and identified nearly identical product cut decisions. The time-dependent Np and Pu concentration profiles provided valuable insight into column dynamics and the predictions agreed with traditional methods including alpha spectrometry and inductively coupled plasma mass spectrometry. These results establish a resourceful avenue for modeling multicomponent systems with convoluted absorption bands without significant user input. It is particularly advantageous for production-oriented radiochemical applications in restrictive hot cell environments.

Multivariate curve resolution for analysis of Raman hyperspectral imaging data sets for enzyme immobilization

• Raman hyperspectral imaging of an enzyme immobilized onto two resins was accomplished. • Multivariate data analysis spectrally resolved chemical species involved in immobilization. • Chemical images spatially resolved chemical species involved in immobilization. • The Raman hyperspectral imaging data sets can be used for alternative applications. Biocatalytic processes are an essential component in achieving efficient and productive chemical syntheses in the scientific and industrial communities. Immobilization of the biocatalyst offers distinct advantages for the process, including increasing the stability and reusability of the biocatalyst as well as increasing control over the process. Herein, directly engineered pantothenate kinase (PanK) was immobilized to two commercially available microporous resins. Ralbovsky et al. (2021) utilized Raman hyperspectral imaging in combination with multivariate curve resolution alternating least squares (MCR-ALS) to spatially and spectrally resolve the chemical species involved in the enzyme immobilization process. Here, we provide eight Raman hyperspectral imaging data sets collected from the enzyme immobilized to the resin beads under different collection parameters, encompassing over 26,000 raw spectra. A brief discussion of the results, in the form of chemical maps and chemical images, indicates both resins, PanK, the glass substrate, and a Bis-Tris wash solution were resolved using MCR-ALS analysis of a subset of hyperspectral images, thus providing crucial chemical and spatial information about the system. The eight total Raman hyperspectral imaging data sets are shared to provide the scientific community with a thoroughly characterized set of spectral data which can be used for a variety of applications, including for further understanding and optimization of enzyme immobilization systems as well as for individual learning and development applications in the fields of chemical imaging and chemometrics.

Mechanisms of Cr(VI) adsorption on schwertmannite under environmental disturbance: Changes in surface complex structures

Hexavalent chromium (Cr(VI)) mobility, reactivity and bioavailability in the acid mine drainage (AMD) are restricted by adsorption reactions on schwertmannite. However, the Cr(VI) adsorption mechanisms remain unclear. In this study, batch adsorption/desorption experiments, X-ray photoelectron spectroscopy (XPS), and in‒situ attenuated total reflectance–Fourier transform infrared spectroscopy (ATR-FTIR) in combination with a multivariate curve resolution– alternating least squares (MCR-ALS) analysis were employed to characterize Cr(VI) adsorption on schwertmannite. The results of batch experiments suggested that two kinds of anion exchange reactions occurred on Sch surface: the outer-sphere complexes and the inner-sphere complexes of sulfate were successively substituted by aqueous Cr(VI) to form inner-sphere complexes. XPS analysis showed that the adsorbed Cr (VI) tended to exchange with sulfate rather than with surface hydroxyl groups on schwertmannite. In-situ ATR-FTIR spectroscopic results confirmed that the Cr(VI) coordination species contained bidentate inner-sphere (C2ν) and monodentate inner-sphere complexes (C3ν). MCR-ALS analysis revealed that monodentate complexes were dominant at pH 5.0–8.0. The proportion of bidentate complexes decreased from 47% to 25% when pH increased from 5.0 to 8.0. Thus, we concluded that a transition occurred between bidentate to monodentate complexes. In addition, the Cr(VI) concentration exerted little influence on the change of surface complexes.

Near infrared spectroscopy coupled to MCR-ALS for the identification and quantification of saffron adulterants: Application to complex mixtures

The adulteration of saffron, due to its high market value, is an old and common practice. Hence, there is a high demand for rapid, easy-to-go, environmentally friendly and low-cost techniques able to perform the simultaneous identification and quantification of saffron adulterants. In this sense, the goal of this work was to use near infrared (NIR) spectroscopy coupled to multivariate curve resolution – alternating least-squares (MCR-ALS) for the identification and quantification of four common adulterants (onion, calendula, pomegranate and turmeric). A total of 38 samples containing all these adulterants were prepared. Several spectral regions and pre-processing techniques were tested in the calibration models to find the best combination. The application of MCR-ALS allowed the identification of the adulterants present in the samples through the comparison between the spectral profiles recovered by MCR-ALS and the respective spectra of pure adulterants. Additionally, the quantification of the adulterants was also successfully achieved in these complex samples with relative percentage errors in concentration predictions (RE) of around 10%, except for onion. Overall, the results obtained in this work demonstrate the suitability of NIR spectroscopy coupled to MCR-ALS analysis to identify and quantify adulterants in a very complex mixture and at low levels. To the best of our knowledge, it was the first time that NIR spectroscopy and MCR-ALS were coupled to simultaneously identify and quantify four adulterants in saffron samples.

Light-Activated Carbon Monoxide Prodrugs Based on Bipyridyl Dicarbonyl Ruthenium(II) Complexes.

Two photoactivatable dicarbonyl ruthenium(II) complexes based on an amide‐functionalised bipyridine scaffold (4‐position) equipped with an alkyne functionality or a green‐fluorescent BODIPY (boron‐dipyrromethene) dye have been prepared and used to investigate their light‐induced decarbonylation. UV/Vis, FTIR and 13C NMR spectroscopies as well as gas chromatography and multivariate curve resolution alternating least‐squares analysis (MCR‐ALS) were used to elucidate the mechanism of the decarbonylation process. Release of the first CO molecule occurs very quickly, while release of the second CO molecule proceeds more slowly. In vitro studies using two cell lines A431 (human squamous carcinoma) and HEK293 (human embryonic kidney cells) have been carried out in order to characterise the anti‐proliferative and anti‐apoptotic activities. The BODIPY‐labelled compound allows for monitoring the cellular uptake, showing fast internalisation kinetics and accumulation at the endoplasmic reticulum and mitochondria.

MCR-ALS analysis of 1H NMR spectra by segments to study the zebrafish exposure to acrylamide.

Metabolomics is currently an important field within bioanalytical science and NMR has become a key technique for drawing the full metabolic picture. However, the analysis of 1H NMR spectra of metabolomics samples is often very challenging, as resonances usually overlap in crowded regions, hindering the steps of metabolite profiling and resonance integration. In this context, a pre-processing method for the analysis of 1D 1H NMR data from metabolomics samples is proposed, consisting of the blind resolution and integration of all resonances of the spectral dataset by multivariate curve resolution-alternating least squares (MCR-ALS). The resulting concentration estimates can then be examined with traditional chemometric methods such as principal component analysis (PCA), ANOVA-simultaneous component analysis (ASCA), and partial least squares-discriminant analysis (PLS-DA). Since MCR-ALS does not require the use of spectral templates, the concentration estimates for all resonances are obtained even before being assigned. Consequently, the metabolomics study can be performed without neglecting any relevant resonance. In this work, the proposed pipeline performance was validated with 1D 1H NMR spectra from a metabolomics study of zebrafish upon acrylamide (ACR) exposure. Remarkably, this method represents a framework for the high-throughput analysis of NMR metabolomics data that opens the way for truly untargeted NMR metabolomics analyses. Graphical abstract.

Real-time monitoring of multi-layered film coating processes using Raman spectroscopy

Raman spectroscopy was used as an in-line PAT tool to predict the applied coating mass of three different coating layers on caffeine cores. The different coating suspensions contained titanium dioxide in the anatase and rutile modification and iron oxide as Raman markers. Partial least squares-regression (PLSR) and multivariate curve resolution-alternating least squares (MCR-ALS) were used for multivariate analysis. The acquired Raman spectra were correlated to the applied coating mass. MCR-ALS models were built and applied offline, while PLS-regression was implemented in the coating process to enable a real-time monitoring. Inline-measurements were optimized by a higher frequency of the spectral measurements and the implementation of a moving average. By PLS-regression analysis, all three layers could be predicted with root mean square errors (RMSEP) of less than 2.3%. Inline implementation and optimization resulted in RMSEPs less than 1.9%. MCR-ALS analysis was able to predict the application of the first and the second layer with RMSEPs less than 2.9%, but failed in predicting the application of the third layer. In conclusion, a real-time monitoring of a multi-layered coating process was achieved, PLS-regression was found to be superior to MCR-ALS and smoothing by the implementation of a moving average enhanced the predictability.

Surface chemistry study of Cu-Pb sulfide ore using ToF-SIMS and multivariate analysis

Studying the surface chemical composition of minerals in the Cu-Pb sulfide ore flotation system is helpful to understand the reasons for the difficulty of selective flotation of Cu-Pb sulfide ore. In this work, the surface chemical compositions of Cu and Pb concentrates were investigated by time-of-flight secondary ion mass spectrometry (ToF-SIMS). Considering the complexity of the surface chemical composition of minerals, multivariate curve resolution-alternating least squares (MCR-ALS) and principal component analysis (PCA) were used to interpret and compare ToF-SIMS data. MCR-ALS analysis of the ToF-SIMS imaging dataset identified the dominant distribution areas of mineral phases and flotation reagents. PCA of the spectral datasets of the regions of interest revealed the differences of surface chemical compositions between the minerals in Cu concentrate and those in Pb concentrate. The main finding of this study was that Cu and Pb on the mineral surfaces were responsible for the failure of selective flotation of Cu-Pb sulfide ore.

Inhomogeneous Molecular Distributions and Cytochrome Types and Redox States in Fungal Cells Revealed by Raman Hyperspectral Imaging Using Multivariate Curve Resolution-Alternating Least Squares.

Hyphae of filamentous fungi consist of compartments that are distinct both spatially and functionally, thereby forming a unique multicellular system. Much work has been done mainly using fluorescence imaging to reveal what biomolecules are present in those different hyphal sections and what physiological roles they play. Nevertheless, a holistic understanding of hyphal functions including the polarized growth of hyphae is still lacking because of the difficulty in simultaneous acquisition of spatial and chemical information on various molecular components in living hyphae. Here, we used a multivariate curve resolution-alternating least-squares (MCR-ALS) analysis of Raman hyperspectral imaging data to study in vivo the spatial distributions and chemical properties of major cellular components in the tip, basal, and branching regions of the model fungus Aspergillus nidulans. The MCR-ALS Raman imaging method visualized, without any labeling, the characteristic distributions of cytochromes as well as other components including polysaccharides, noncytochrome proteins, nucleic acids, lipids, and ergosterol in the hyphal regions studied. Furthermore, the intrinsic Raman spectra derived for the first time from the MCR-ALS analysis enabled us to gain otherwise unobtainable chemical insights into those visualized components. We show variations in the relative abundance of cytochromes b and c and in their redox states (reduced vs oxidized form) among the three different representative compartments of A. nidulans hyphae, which could potentially be associated with specific physiological activities and functions of hyphae. The present results demonstrate that our MCR-ALS Raman imaging can serve as a useful tool complementary to the conventional approaches, for elucidating the diverse roles of filamentous fungi at the molecular level.

Contribution to second-order calibration based on multivariate curve resolution with and without previous chromatographic synchronization

Rotational ambiguity in the bilinear solutions provided by multivariate curve resolution - alternating least-squares (MCR-ALS) leads to an additional source of uncertainty in the estimation of analyte concentrations by second-order multivariate calibration. The phenomenon is particularly important when measuring matrix instrumental data derived from liquid chromatography with spectral detection, where elution time profiles usually vary from sample to sample both in position and shape. This makes the data non-trilinear, precluding the use of unique trilinear decomposition models. The present report compares some analytical results achieved by: (1) the usual MCR-ALS analysis of augmented matrices built from raw matrix data and (2) a previously reported procedure based on synchronizing the MCR-ALS elution time profiles using correlation optimized warping (COW), reconstructing the augmented matrix with the spectra and the aligned chromatograms, and then applying MCR-ALS again with the trilinearity constraint, leading to unique solutions, which is possible because the warping process restores the trilinearity of the data. We show that alternative (2) does not solve the rotational ambiguity issues and artificially modifies the original data, without significant improvements in analytical performance. In the simulated systems, the best average errors for alternative (1) were about 2%, whereas for alternative (2) they were in the range 4–11%. For the experimental system, the corresponding errors were 2–3% and 3–4% respectively, i.e. with no significant improvement in going to alternative (2). All efforts should be directed to reduce the degree of rotational ambiguity by applying a full battery of chemically reasonable constraints.

MCR-ALS analysis of IR spectroscopy and XRD for the investigation of ibuprofen - nicotinamide cocrystal formation.

To improve aqueous solubility, a poorly water-soluble active ingredient is classically combined with a conformer to form cocrystals. Hot melt extrusion is one preparation method for the formation of cocrystal solids. The aim of our study was to determine the optimal temperature conditions for the formation of ibuprofen and nicotinamide cocrystals using real-time infrared (IR) and X-ray diffraction (XRD) measurements. IR spectra and XRD patterns were subjected to multivariate curve resolution alternating least squares (MCR-ALS) analysis and decomposed into several components. Each component was descriptive of a specific step in the formation of the cocrystal. Cocrystal formation was followed by a separation phase between amorphous ibuprofen and crystalline nicotinamide. Our results suggest that, when using the hot melt exclusion method, careful consideration should be made towards optimizing processing temperatures in order to prevent amorphization and promote control over the process of cocrystal formation.

Exploring the binding interaction of Maillard reaction by-product 5-hydroxymethyl-2-furaldehyde with calf thymus DNA.

5-Hydroxymethyl-2-furaldehyde (5-HMF), a by-product of the Maillard reaction, usually present in fried and baked food, may cause potential harm to the human body. Here, the interaction between 5-HMF and calf thymus DNA (ctDNA) under physiological buffer (pH 7.4) was studied using multi-spectroscopic methods combined with multivariate curve resolution-alternating least squares (MCR-ALS) chemometrics and molecular simulation techniques. The concentration profiles and pure spectra of the three components (5-HMF, ctDNA and 5-HMF-ctDNA complex) were extracted from highly overlapping spectra using MCR-ALS analysis, which verified the formation of 5-HMF-ctDNA complex. The binding constant being of the order of 103 L mol-1 at four temperatures (292, 298, 304 and 310 K) indicated a weak affinity in the binding of 5-HMF to ctDNA. The binding interaction was mainly driven by hydrogen bonds and van der Waals forces. Viscosity analysis, melting assay, ionic strength effect and competitive fluorescence studies ascertained that 5-HMF bound to ctDNA through groove binding, and it tended to bind to guanine-cytosine rich region of ctDNA which was characterized using Fourier transform infrared spectra and molecular docking. Circular dichroism spectral analysis and DNA cleavage assays indicated that the ctDNA conformation was altered from B to A form and 5-HMF caused DNA damage at higher concentration. The results suggested that 5-HMF bound to ctDNA through groove binding and caused DNA damage. This research may contribute to understand the binding mechanism of 5-HMF to ctDNA and to the assessment of the toxicological effect of 5-HMF in biological processes. © 2018 Society of Chemical Industry.

Effect of image processing constraints on the extent of rotational ambiguity in MCR-ALS of hyperspectral images

Hyperspectral imaging is a way to explore the spatial and spectral information of the different compounds in chemical or biological samples. In addition, multivariate curve resolution - alternating least squares (MCR-ALS) can be used to extract this information based on the bilinearity assumption. However, it is well-known that using proper constraints can reduce the amount of uncertainty in the results of MCR, which is called rotational ambiguity. In MCR-ALS analysis of hyperspectral images, different image processing techniques, such as model fitting, image segmentation or sparse image recovery can be applied as spatial constraints. In this contribution, we aim to investigate how the use of these spatial constraints limits the extent of rotational ambiguity of MCR-ALS solutions. For this purpose, we evaluate the extent of rotational ambiguity and use Borgen plots to visualize it. We show on simulations and real hyperspectral imaging data that accuracy of the results is improved when spatial constraints are applied.

Tracking hidden organic carbon in rocks using chemometrics and hyperspectral imaging

Finding traces of life or organic components of prebiotic interest in the rock record is an appealing goal for numerous fields in Earth and space sciences. However, this is often hampered by the scarceness and highly heterogeneous distribution of organic compounds within rocks. We assess here an innovative analytical strategy combining Synchrotron radiation-based Fourier-Transform Infrared microspectroscopy (S-FTIR) and multivariate analysis techniques to track and characterize organic compounds at the pore level in complex oceanic rocks. S-FTIR hyperspectral images are analysed individually or as multiple image combinations (multiset analysis) using Principal Component Analyses (PCA) and Multivariate Curve Resolution – Alternating Least Squares (MCR-ALS). This approach allows extracting simultaneously pure organic and mineral spectral signatures and determining their spatial distributions and relationships. MCR-ALS analysis provides resolved S-FTIR signatures of 8 pure mineral and organic components showing the close association at a micrometric scale of organic compounds and secondary clays formed during rock alteration and known to catalyse organic synthesis. These results highlights the potential of the serpentinizing oceanic lithosphere to generate and preserve organic compounds of abiotic origin, in favour of the hydrothermal theory for the origin of life.

Monitoring of Adulteration and Purity in Coconut Oil Using Raman Spectroscopy and Multivariate Curve Resolution

This study evaluates the use of Raman spectroscopy with a multivariate curve resolution–alternating least squares (MCR-ALS) analysis to monitor the adulteration and purity of coconut oil. Sunflower, soybean, canola, sesame, corn, castor bean, peanut, palm kernel, babassu, mineral, and Vaseline oils have been used as adulterants in this work. Control charts were developed to evaluate the purity of an oil sample using the scores from the MCR-ALS analysis of a data set containing pure and adulterated oils. These control charts were able to detect the adulteration of coconut oil in a range of 2–30% with all the oils tested. Additionally, quantification models were developed using MCR-ALS with correlation constraints for coconut oil adulterated with sunflower, canola, Vaseline, babassu, and palm kernel oils. The models presented satisfactory results, which had absolute errors below 5%, for samples adulterated with sunflower, canola, and Vaseline oils. The babassu and palm kernel adulterants could also be quantified with a superior margin of error. The results indicated that using Raman spectroscopy with MCR is a clean and non-destructive method for assessing coconut oil purity that can be used without removing a sample from its bottle.

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.