Application Of Multivariate Curve Resolution Research Articles

Multivariate curve resolution-based data fusion approaches applied in 1H NMR metabolomic analysis of healthy cohorts

BackgroundMetabolomics plays a critical role in deciphering metabolic alterations within individuals, demanding the use of sophisticated analytical methodologies to navigate its intricate complexity. While many studies focus on single biofluid types, simultaneous analysis of multiple matrices enhances understanding of complex biological mechanisms. Consequently, the development of data fusion methods enabling multiblock analysis becomes essential for comprehensive insights into metabolic dynamics. ResultsThis study introduces a novel guideline for jointly analyzing diverse metabolomic datasets (serum, urine, metadata) with a focus on metabolic differences between groups within a healthy cohort. The guideline presents two fusion strategies, ‘Low-Level data fusion’ (LLDF) and ‘Mid-Level data fusion’ (MLDF), employing a sequential application of Multivariate Curve Resolution with Alternating Least Squares (MCR-ALS), linking the outcomes of successive analyses. MCR-ALS is a versatile method for analyzing mixed data, adaptable at various stages of data processing—encompassing resonance integration, data compression, and exploratory analysis. The LLDF and MLDF strategies were applied to 1H NMR spectral data extracted from urine and serum samples, coupled with biochemical metadata sourced from 145 healthy volunteers. SignificanceBoth methodologies effectively integrated and analysed multiblock datasets, unveiling the inherent data structure and variables associated with discernible factors among healthy cohorts. While both approaches successfully detected sex-related differences, the MLDF strategy uniquely revealed components linked to age. By applying this analysis, we aim to enhance the interpretation of intricate biological mechanisms and uncover variations that may not be easily discernible through individual data analysis.

Multiplane Image Restoration Using Multivariate Curve Resolution: An Alternative Approach to Deconvolution in Conventional Brightfield Microscopy

Three-dimensional reconstruction in brightfield microscopy is challenging since a 2D image includes from in-focus and out-of-focus light which removes the details of the specimen’s structures. To overcome this problem, many techniques exist, but these generally require an appropriate model of Point Spread Function (PSF). Here, we propose a new images restoration method based on the application of Multivariate Curve Resolution (MCR) algorithms to a stack of brightfield microscopy images to achieve 3D reconstruction without the need for PSF. The method is based on a statistical reconstruction approach using a self-modelling mixture analysis. The MCR-ALS (ALS for Alternating Least Square) algorithm under non-negativity constraints, Wiener, Richardson–Lucy, and blind deconvolution algorithms were applied to silica microbeads and red blood cells images. The MCR analysis produces restored images that show informative structures which are not noticeable in the initial images, and this demonstrates its capability for the multiplane reconstruction of the amplitude of 3D objects. In comparison with 3D deconvolution methods based on a set of No Reference Images Quality Metrics (NR-IQMs) that are Standard Deviation, ENTROPY Average Gradient, and Auto Correlation, our method presents better values of these metrics, showing that it can be used as an alternative to 3D deconvolution methods.

Multimodal multisample spectroscopic imaging analysis of tumor tissues using multivariate curve resolution

Chemical imaging aims to characterize the molecular patterns present in a tissue or surface. High-resolution images with high content of molecular information represent a clear advantage in many research areas, such as in biomedicine. Specific molecular patterns, when related to pathological traits, could help understanding the molecular events related to the condition studied. Chemical composition of the different regions within the tissue may reveal interesting patterns that cannot be detected using conventional staining techniques. In this work, a new chemometrics approach is proposed for the simultaneous multimodal chemical imaging analysis of six tissue slices from two types of tumor tissues (EG and 1070) exhibiting different sensitivity to the docetaxel drug. Therefore, a deeper chemical imaging study of these tissues could reveal new chemical fingerprints related to the different susceptibility and response to this drug treatment. The spectral imaging techniques used in this study were MALDI-TOF mass spectrometry imaging (MSI) and FT-IR hyperspectral imaging. In addition, a RGB color picture of the hematoxylin/eosin staining was also included in the joint image analysis. The huge spectral data information generated by these multiple imaging techniques in the simultaneous analysis of the different tissue samples was organized in combined multiset and multimodal spectral image data structures which were then analyzed by multivariate curve resolution. Using this approach, the nature of the chemical constituents and their high-resolution spatial distribution were detangled showing what were the main chemical patterns present in specific areas of the tissues which distinguished the two types of tumors. In summary, in the present work, the application of multivariate curve resolution is confirmed to be a powerful and flexible data analysis tool able to extract chemical information from the simultaneous analysis of huge, fused image data sets from obtained by different type of spectroscopic imaging techniques having different spatial resolution powers.

On the area of feasible solutions for rank‐deficient problems: I. Introduction of a generalized concept

AbstractRank deficiency of a spectral data matrix means that its rank is smaller than the number of the anticipated chemical components. A rank deficiency can hide the true chemical structure of the underlying pure components and complicates the application of multivariate curve resolution and self‐modeling curve resolution techniques. A new approach for the analysis of the factor ambiguities is introduced, and the area of feasible solutions (AFS) is generalized to rank‐deficient spectral data. The extended tools are tested for the Michaelis–Menten kinetics and abstract model data.

Influence of Zn(II) Ion on the Autoxidation of Pyrogallol and Gallic Acid in Weakly Acidic Aqueous Solutions / Uticaj Zn(II) jona na autooksidaciju pirogalola i galne kiseline u slabo kiselim vodenim rastvorima

SUMMARY Pyrogallol-type phenolic compounds are widespread in nature and may have significant impact on human health. As Zn(II) ion was proved to be capable of enhancing some biological activities of pyrogallol-type natural phenolic compounds, we decided to study its influence on the autoxidation of pyrogallol and gallic acid in weakly acidic aqueous solutions. UV-Vis spectrophotometric measurements showed that autoxidation of pyrogallol was initiated by the influence of Zn(II) ions at pH 5.5 and pH 6.5. The differences in UV-Vis spectra of the first autoxidation products resolved by the application of multivariate curve resolution - alternating least squares (MCR-ALS) method indicated that pH change also changed the mechanism of autoxidation process. Formation of stable Zn(II) ion spin stabilized free radical obtained during the autoxidation of pyrogallol at pH 6.5 was confirmed by using electron spin resonance (ESR) spectroscopy and its structure was determined. Both UV-Vis spectrophotometric and ESR spectroscopic measurements did not give any evidence that gallic acid autoxidation was initiated by the influence of Zn(II) ions in weakly acidic aqueous solutions. The results of this study may be used for explaining possible differences in the Zn(II) ion influence on various biological activities of pyrogallol-type phenolic compounds containing simple pyrogallol moiety and the ones containing gallate moiety as a part of the molecule

Sliding window multi-curve resolution: Application to gas chromatography–ion mobility spectrometry

Blind source separation (BSS) techniques aim to extract a set of source signals from a measured mixture in an unsupervised manner. In the chemical instrumentation domain source signals typically refer to time-varying analyte concentrations, while the measured mixture is the set of observed spectra. Several techniques exist to perform BSS on ion mobility spectrometry, being simple-to-use interactive self-modelling mixture analysis (SIMPLISMA) and multivariate curve resolution (MCR) the most commonly used. The addition of a multi-capillary gas chromatography column using the ion mobility spectrometer as detector has been proposed in the past to increase chemical resolution. Short chromatography times lead to high levels of co-elution, and ion mobility spectra are key to resolve them. For the first time, BSS techniques are used to deconvolve samples of the gas chromatography–ion mobility spectrometry tandem. We propose a method to extract spectra and concentration profiles based on the application of MCR in a sliding window. Our results provide clear concentration profiles and pure spectra, resolving peaks that were not detected by the conventional use of MCR. The proposed technique could also be applied to other hyphenated instruments with similar strong co-elutions.

Application of multivariate curve resolution alternating least squares method for determination of caffeic acid in the presence of catechin interference

In the current article, preparation and application of a graphene oxide nanosheets-based sensor for electrochemical determination of caffeic acid (CA) in the presence of catechin is described. This measurement was performed using the differential pulse voltammetry (DPV) technique and chemometric methods such as multivariate curve resolution–alternating least squares (MCR–ALS). The modified sensor was characterized by various techniques such as Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy, ultraviolet–visible, X-ray diffraction (XRD), and scanning electron microscopy (SEM). Operating conditions and influencing variables (involving several chemical and instrumental variables) were optimized with central composite rotatable design and response surface methodology. The second-order electrochemical data were generated by changing the pulse height in DPV, and after potential shift correction MCR–ALS was applied. Under the optimized conditions, the dynamic range for CA was from 0.5 to 100.0μM and the detection limit was found to be 1.1×10–9M. The results revealed that the modified electrode shows an improvement in anodic oxidation activity of CA due to a marked enhancement in the current response compared with the bare carbon paste electrode. The modified electrode demonstrated good sensitivity, selectivity, and stability. The proposed method was successfully applied in determination of caffeic acid in the presence of unexpected electroactive interferences with a very high degree of overlapping such as catechin in real samples.

A non-target chemometric strategy applied to UPLC-MS sphingolipid analysis of a cell line exposed to chlorpyrifos pesticide: A feasibility study

A non-target chemometrics study based on the application of Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) method to a data set obtained by ultra-performance liquid chromatography coupled to mass spectrometry (UPLC-MS) has been applied to the study of human prostate cancer (DU145) cell line samples treated with the organophosphate pesticide chlorpyrifos (CPF). Full scan UPLC-MS data sets were segmented in 17 different chromatographic windows and submitted to a non-target detailed study. Every one of these chromatographic windows of the different analyzed samples (treated and non-treated with CPF) was column-wise augmented in a new data matrix with their m/z values in the common column mode to preserve the fulfillment of the assumed spectral bilinear model. MCR-ALS was used to recover the elution and mass spectral profiles of the pure components present in each of the analyzed chromatographic windows. ANOVA (p≤0.05) was then applied to compare the areas under the concentration profiles of the MCR-ALS resolved components in the CPS treated and control samples. This analysis allowed the detection of those sphingolipids having their concentration in cells modified by the presence of CPS compared to control samples where this contaminant was absent. Positively identified sphingolipids included sphingomyelins, dihydrosphingomyelin and C16 ceramide. The strategy described in this work is proposed for a general non-target UPLC-MS MCR-ALS analysis of the effect of environmental contaminants in cells in lipidomic and metabonomic studies.

Application of multivariate curve resolution – alternating least square (MCR-ALS) method for determination of concentration profiles of flavonoid–DNA equilibrium monitored by UV-Vis data

Application of multivariate curve resolution – alternating least square (MCR-ALS) method for determination of concentration profiles of flavonoid–DNA equilibrium monitored by UV-Vis data

Multivariate curve resolution applied to in situ X-ray absorption spectroscopy data: An efficient tool for data processing and analysis

Large datasets containing many spectra commonly associated with in situ or operando experiments call for new data treatment strategies as conventional scan by scan data analysis methods have become a time-consuming bottleneck. Several convenient automated data processing procedures like least square fitting of reference spectra exist but are based on assumptions. Here we present the application of multivariate curve resolution (MCR) as a blind-source separation method to efficiently process a large data set of an in situ X-ray absorption spectroscopy experiment where the sample undergoes a periodic concentration perturbation. MCR was applied to data from a reversible reduction–oxidation reaction of a rhenium promoted cobalt Fischer–Tropsch synthesis catalyst. The MCR algorithm was capable of extracting in a highly automated manner the component spectra with a different kinetic evolution together with their respective concentration profiles without the use of reference spectra. The modulative nature of our experiments allows for averaging of a number of identical periods and hence an increase in the signal to noise ratio (S/N) which is efficiently exploited by MCR. The practical and added value of the approach in extracting information from large and complex datasets, typical for in situ and operando studies, is highlighted.

Spectrophotometric determination of concentration profiles and thermodynamic parameters of aggregation phenomena of rhodamine B by application of multivariate curve resolution

Spectrophotometric determination of concentration profiles and thermodynamic parameters of aggregation phenomena of rhodamine B by application of multivariate curve resolution

Application of multivariate curve resolution alternating least squares to biomedical analysis using electrochemical techniques at a nanostructure-based modified sensor

Application of multivariate curve resolution alternating least squares (MCR-ALS), for the resolution and quantification of two sulfonamide drugs containing sulfamethizole (SMT) and sulfapyridine (SPY) with a very high degree of overlapping voltammograms, is described. A simple and sensitive carbon nanocomposite electrode has been developed for the electrochemical trace determination of SMT and SPY in Britton-Robinson (B-R) buffer solution. This sensor was designed by incorporation of multi-walled carbon nanotubes (MWCNTs) into the carbon paste electrode (CPE). Response surface methodology (RSM) using central composite rotatable design (CCRD) model was used to optimize effective parameters on differential pulse voltammetry (DPV) response of the two analytes. A 4-factor-5-level CCRD of RSM was applied to model linear, interaction and quadratic effects of chemical and instrumental variables. After potential shift correction, MCR-ALS has been applied to data collected from DPV analysis of the oxidation process of SMT and SPY in different pulse heights. Quantitative voltammetric analyses of SMT and SPY were performed in the concentration range of 0.1-180.0 and 0.5-165.0μM, and it was found that the detection limits were 50.0 and 60.0nM, respectively. The method was applied for simultaneous determination of SMT and SPY in spiked human serum and urine samples.

Simultaneous Anodic Stripping Voltammetric Determination of Pb and Cd, Using a Vibrating Gold Microwire Electrode, Assisted by Chemometric Techniques

AbstractSimultaneous anodic stripping voltammetric determination of Pb and Cd is restricted on gold electrodes as a result of the overlapping of these two peaks. This work describes the quantitative determination of a binary mixture system of Pb and Cd, at low concentration levels (up to 15.0 and 10.0 µg L−1 for Pb and Cd, respectively) by differential pulse anodic stripping voltammetry (DPASV; deposition time of 30 s), using a green electrode (vibrating gold microwire electrode) without purging in a chloride medium (0.5 M NaCl) under moderate acidic conditions (HCl 1.0 mM), assisted by chemometric tools. The application of multivariate curve resolution alternating least squares (MCR‐ALS) for the resolution and quantification of both metals is shown. The optimized MCR‐ALS models showed good prediction ability with concentration prediction errors of 12.4 and 11.4 % for Pb and Cd, respectively. The quantitative results obtained by MCR‐ALS were compared to those obtained with partial least squares (PLS) and classical least squares (CLS) regression methods. For both metals, PLS and MCR‐ALS results are comparable and superior to CLS. For Cd, as a result of the peak shift problem, the application of CLS was unsuitable. MCR‐ALS provides additional advantage compared to PLS since it estimates the pure response of the analytes signal. Finally, the built up multivariate calibration models, based either in MCR‐ALS or PLS regression, allowed to quantify concentrations of Pb and Cd in surface river water samples, with satisfactory results.

Chemometric Strategies To Unmix Information and Increase the Spatial Description of Hyperspectral Images: A Single-Cell Case Study

Hyperspectral images are analytical measurements that provide spatial and structural information. The spatial description of the samples is the specific asset of these measurements and the reason why they have become so important in (bio)chemical fields, where the microdistribution of sample constituents or the morphology or spatial pattern of sample elements constitute very relevant information. Often, because of the small size of the samples, the spatial detail provided by the image acquisition systems is insufficient. This work proposes a data processing strategy to overcome this instrumental limitation and increase the natural spatial detail present in the acquired raw images. The approach works by combining the information of a set of images, slightly shifted from each other with a motion step among them lower than the pixel size of the raw images. The data treatment includes the application of multivariate curve resolution (unmixing) multiset analysis to the set of collected images to obtain the distribution maps and spectral signatures of the sample constituents. These sets of maps are noise-filtered and compound-specific representations of all the relevant information in the pixel space and decrease the dimensionality of the original image from hundreds of spectral channels to few sets of maps, one per sample constituent or element. The information in each compound-specific set of maps is combined via a super-resolution post-processing algorithm, which takes into account the shifting, decimation, and point spread function of the instrument to reconstruct a single map per sample constituent with much higher spatial detail than that of the original image measurement.

Application of Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) to remote sensing hyperspectral imaging

The application of the MCR-ALS method is demonstrated on two simulated remote sensing spectroscopic images and on one experimental reference remote sensing spectroscopic image obtained by the Airborn Visible/Infrared Imaging Spectrometer (AVIRIS). By application of MCR-ALS, the spectra signatures of the pure constituents present in the image and their concentration distribution at a pixel level are estimated. Results obtained by MCR-ALS are compared to those obtained by other methods frequently used in the remote sensing spectroscopic imaging field like VCA and MVSA. In the case of the analysis of the experimental data set, the resolved pure spectra signatures were compared to reference spectra from USGS library for their identification. In all cases, results were also evaluated for the presence of rotational ambiguities using the MCR-BANDS method. The obtained results confirmed that the MCR-ALS method can be successfully used for remote sensing hyperspectral image resolution purposes. However, the amount of rotation ambiguity still present in the solutions obtained by this and other resolution methods (like VCA or MVSA) can still be large and it should be evaluated with care, trying to reduce its effects by selecting the more appropriate constraints. Only in this way it is possible to increase the reliability of the solutions provided by these methods and decrease the uncertainties associated to their use.

Exploring in vivo violacein biosynthesis by application of multivariate curve resolution on fused UV–VIS absorption, fluorescence, and liquid chromatography–mass spectrometry data

In this work, the application of multivariate curve resolution-alternating least squares (MCR-ALS) is proposed for extracting information from multitechnique fused multivariate data (UV-VIS absorption, fluorescence, and liquid chromatography-mass spectrometry) gathered during the biosynthesis of violacein pigment. Experimental data sets were pretreated and arranged in a row-wise augmented data matrix before their chemometric investigation. Five different chemical components were resolved. Kinetic and spectral information about these components were obtained and their relationship with violacein biosynthesis was established. Three new chemical compounds with molar masses of 453, 465, and 479 u, until now not reported in the literature, were identified and proposed as intermediates in the biosynthesis of other indolocarbazoles. The precursor (tryptophan), one intermediate (deoxyviolacein), and the final product (violacein) of violacein biosynthesis were identified and characterized using the proposed approach. The chemometric procedure based on the MCR-ALS method has proved to be a powerful tool to investigate violacein biosynthesis and its application can be easily extended to the study of other bioprocesses.

A case study of real-time monitoring of solid-state phase transformations in acoustically levitated particles using near infrared and Raman spectroscopy

The objective of this study was to monitor the amorphous-to-crystalline solid-state phase transformation kinetics of the model drug ibuprofen with spectroscopic methods during acoustic levitation. Chemical and physical information was obtained by real-time near infrared (NIRS) and Raman spectroscopy measurements. The recrystallisation kinetic parameters (overall recrystallisation rate constant β and the time needed to reach 50% of the equilibrated level t50), were determined using a multivariate curve resolution approach.The acoustic levitation device coupled with non-invasive spectroscopy enabled monitoring of the recrystallisation process of the difficult-to-handle (adhesive) amorphous sample. The application of multivariate curve resolution enabled isolation of the underlying pure spectra, which corresponded well with the reference spectra of amorphous and crystalline ibuprofen. The recrystallisation kinetic parameters were estimated from the recrystallisation profiles. While the empirical recrystallisation rate constant determined by NIR and Raman spectroscopy were comparable, the lag time for recrystallisation was significantly lower with Raman spectroscopy as compared to NIRS. This observation was explained by the high energy density of the Raman laser beam, which might have led to local heating effects of the sample and thus reduced the recrystallisation onset time.It was concluded that acoustic levitation with NIR and Raman spectroscopy combined with multivariate curve resolution allowed direct determination of the recrystallisation kinetics of amorphous drugs and thus is a promising technique for monitoring solid-state phase transformations of adhesive small-sized samples during the early phase of drug development.

Multivariate Curve Resolution Alternating Least-Squares As a Tool for Analyzing Crude Oil Extracted Asphaltene Samples

Asphaltene deposition in the early stage of the oil reservoir life and later during any stimulation process emerges critical problems to the petroleum industry. Deposition of asphaltene aggregates raises strict problems in industries and demands markedly a practical and scientific knowledge of the mechanisms of aggregation and precipitation. Fluorescence emission spectroscopy has been widely used to illuminate the fundamental properties of crude oils and asphaltenes. It proposes analysis of some details of equilibrium, dynamic behavior, and aggregation composition of crude oil under specific condition. In this work, the fluorescence spectra of crude-oil extracted asphaltene samples were studied and analyzed by the application of multivariate curve resolution alternating least-squares (MCR-ALS). The asphaltene samples were extracted from crude oil of three different regions of Iran (Kuh-e-Mond, Bangestan, and Gachsaran). The excitation–emission fluorescence spectra of asphaltene solutions of variable concentrations (1.0–60.0 μg/mL) in toluene were recorded. The application of MCR-ALS analysis on the row-wise augmented matrices allows the identification of major components at different asphaltene solution concentration in each sample. The emission spectra, excitation spectra, and concentration profile of these components were deconvoluted to demonstrating structural features characteristic of each sample.

Pros and cons of benzodiazepines screening in human saliva by ion mobility spectrometry

The usefulness of ion mobility spectrometry as a screening methodology for the on-site benzodiazepine analysis in saliva samples has been critically evaluated. The procedure involved the injection of clear supernatant extracts after centrifugation and provided limit of detection values ranging from 2.0 to 18 μg L(-1), and a precision, expressed as relative standard deviation, from 2.9% to 16%, depending on the different benzodiazepines studied. Those values are appropriate for their positive identification in saliva samples in which benzodiazepine concentration, after a chronic or acute dose, is in the range of 2-30 μg L(-1). Problems related with overlapped benzodiazepine signals have been successfully overcome by application of multivariate curve resolution, which is a helpful tool to improve the resolution of the technique, without sacrificing the method simplicity and frequency of analysis. The possibility of false positives caused by the presence of interferents with the same drift time as the benzodiazepines and the possibility of false negatives due to the presence of interferents by competitive ionization have been critically evaluated. The satisfactory results obtained for the analysis of real saliva samples after an acute dose of diazepam through sublingual and oral intakes confirm the capability of the technique to be used as a screening methodology in the analysis of benzodiazepines in oral fluids.

Multivariate curve resolution modeling of liquid chromatography–mass spectrometry data in a comparative study of the different endogenous metabolites behavior in two tomato cultivars treated with carbofuran pesticide

A metabonomic study based on the application of multivariate curve resolution and alternating least squares (MCR-ALS) to three-way data sets obtained by liquid chromatography coupled to mass spectrometry detection (LC–MS) was carried out for Rambo and Raf tomato cultivars treated with carbofuran pesticide. Samples were picked up during a 21 days period after treatment and analyzed by LC–MS in scan mode, along with the corresponding blank samples. Then, MCR-ALS was applied to the three-way data sets using column wise augmented matrices, and the evolutionary profiles as a function of the time after treatment were estimated for the metabolites present in both cultivars, as well as their corresponding pure spectra estimations. A comparative study using those estimations showed that some of these metabolites followed different behavior for the different cultivars after treatment. Since all treated and untreated Rambo and Raf samples were picked up according to the same sampling protocol and in a similar state of maturation, any difference in the behavior between profiles can be interpreted as an effect due to the presence of pesticide and to the kind of cultivar. Based on this hypothesis, several PLS-DA approaches were tested to check if it would be possible to classify samples by using the metabolites MCR estimations. Results showed that PLS-DA models for classification of treated or non-treated (blank) samples were the best ones obtained (98.44% of correct classifications for the validation set), which supports the stress effects related to carbofuran treatment. In addition, excellent discrimination among the four groups could be attained (89.06% of correct classifications for the validation set).