Multivariate Curve Resolution Procedure Research Articles

Fully automatic resolution of untargeted GC-MS data with deep learning assistance

DeepResolution (Deep learning-assisted multivariate curve Resolution) has been proposed to solve the co-eluting problem for GC-MS data. However, DeepResolution models must be retrained when encountering unknown components, which is undoubtedly time-consuming and burdensome. In this study, a new pipeline named DeepResoution2 was proposed to overcome these limitations. DeepResolution2 utilizes deep neural networks to divide the profile into segments, estimate the number of components in each segment, and predict the elution region of each component. Subsequently, the information obtained by these deep learning models is used to assist the multivariate curve resolution procedure. Only seven models (1 + 1 + 5) are required to automate the whole analysis procedure of untargeted GC-MS data, which is an important improvement over DeepResolution. These seven models are stable and universal. Once established, they can be used to resolve most GC-MS data. Compared with MS-DIAL, ADAP-GC, and AMDIS, DeepResolution2 can obtain more reasonable mass spectra, chromatograms and peak areas to identify and quantify compounds. DeepResoution2 (0.955) outperformed AMDIS (0.939), MS-DIAL (0.948) and ADAP-GC (0.860) in terms of the linear correlation between concentrations and peak areas on overlapped peaks in fatty acid dataset. In real biological samples of human male infertility plasma, the peak areas and mass spectra of 136 untargeted GC-MS files were automatically extracted by DeepResolution2 without any prior information and manual intervention. DeepResolution2 includes all the functions for analyzing untargeted GC-MS datasets from the feature extraction of raw data files to the establishment of discriminant models.

Exploratory Analysis of the Distribution of Lignin and Cellulose in Woods by Raman Imaging and Chemometrics

The determinations of cellulose and lignin are important in wood analysis from both perspectives: chemistry composition, commercial and industrial application of woods. In this context, Raman image spectroscopy was applied in the determination of cellulose and lignin distribution in wood surfaces of Swietenia macrophylla King (Mahogany) and Eucalyptus hybrid (E. urophylla × E. camaldulensis). The relative concentration maps were obtained by applying a multivariate curve resolution procedure. The estimated Raman spectra for cellulose and lignin agrees with the ones available in the literature. The cellulose concentration maps showed similar variations for both studied species with an average value of nearly 85% as obtained by the reference method. The lignin concentration maps were somewhat different. The Eucalyptus hybrid presents a broader distribution (20 to 45%) while for Mahogany it is nearly constant, around 25%, with localized regions presenting contents of 45 to 65% of lignin. The reference method showed that the mean lignin content of both species is 27%. The cellulose and lignin concentration maps agree with the biological functions of the anatomic structures observed in the images.

Model‐free multivariate curve resolution combined with model‐based kinetics: algorithm and applications

Multivariate curve resolution techniques are powerful tools to extract from sequences of spectra of a chemical reaction system the number of independent chemical components, their associated spectra, and the concentration profiles in time. Usually, these solutions are not unique because of the so‐called rotational ambiguity.In the present work, we reduce the non‐uniqueness by enforcing the consistency of the computed concentration profiles with a given kinetic model. Traditionally, the kinetic modeling is realized in a separate step, which follows the multivariate curve resolution procedure. In contrast to this, we consider a hybrid approach that combines the model‐free curve resolution technique with the model‐based kinetic modeling in an overall optimization. For a two‐component model problem, the range of possible solutions is analyzed, and its reduction to a single, unique solution by means of the hybrid kinetic modeling is shown. The algorithm reduces the rotational ambiguity and improves the quality of the kinetic fitting. Numerical results are also presented for a multi‐component catalytic reaction system that obeys the Michaelis–Menten kinetics. Copyright © 2012 John Wiley & Sons, Ltd.

VOC-based metabolic profiling for food spoilage detection with the application to detecting Salmonella typhimurium-contaminated pork

In this study, we investigated the feasibility of using a novel volatile organic compound (VOC)-based metabolic profiling approach with a newly devised chemometrics methodology which combined rapid multivariate analysis on total ion currents with in-depth peak deconvolution on selected regions to characterise the spoilage progress of pork. We also tested if such approach possessed enough discriminatory information to differentiate natural spoiled pork from pork contaminated with Salmonella typhimurium, a food poisoning pathogen commonly recovered from pork products. Spoilage was monitored in this study over a 72-h period at 0-, 24-, 48- and 72-h time points after the artificial contamination with the salmonellae. At each time point, the VOCs from six individual pork chops were collected for spoiled vs. contaminated meat. Analysis of the VOCs was performed by gas chromatography/mass spectrometry (GC/MS). The data generated by GC/MS analysis were initially subjected to multivariate analysis using principal component analysis (PCA) and multi-block PCA. The loading plots were then used to identify regions in the chromatograms which appeared important to the separation shown in the PCA/multi-block PCA scores plot. Peak deconvolution was then performed only on those regions using a modified hierarchical multivariate curve resolution procedure for curve resolution to generate a concentration profiles matrix C and the corresponding pure spectra matrix S. Following this, the pure mass spectra (S) of the peaks in those region were exported to NIST 02 mass library for chemical identification. A clear separation between the two types of samples was observed from the PCA models, and after deconvolution and univariate analysis using N-way ANOVA, a total of 16 significant metabolites were identified which showed difference between natural spoiled pork and those contaminated with S. typhimurium.

A comparison of multivariate statistical analysis protocols for ToF‐SIMS spectral images

AbstractTime‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) instruments produce raw data sets with a tremendous quantity of data. Multivariate statistical analysis (MVA) tools are being used to reduce the massive amount of chemical information into a smaller set of components that are easier to interpret and understand as a result of species association. Standard principal component analysis (PCA) is the most heavily used MVA algorithm in the ToF‐SIMS community, and is frequently computed using the singular value decomposition (SVD). Other algorithms such as multivariate curve resolution (MCR) have also gained popularity over the past few years. In this work, we compare the as‐measured ToF‐SIMS spectrum and ion images with four MVA data analysis protocols: standard PCA, image‐rotated SVD, spectra‐rotated SVD, and a PCA‐based MCR procedure. Image‐rotated SVD and spectra‐rotated SVD are closely connected to PCA and involve abstract rotations of the singular vectors that naturally arise during computation of the principal components via SVD. These rotations are designed to enhance either spatial contrast or spectral contrast in the components, respectively. We will show that the four MVA protocols provide essentially the same information, but accentuate different aspects of the sample's composition and lateral distribution, and that taken together these methods provide a more complete understanding of the sample. We will demonstrate that the component spectra estimated by MVA protocols assist the analyst in discovering minor constituents and understanding species correlation that would have been difficult, if not impossible, using univariate analysis protocols. For the data set described here, MVA tools identified unexpected species, which were not obvious in the as‐measured data. Copyright © 2008 John Wiley & Sons, Ltd.

Exploratory data analysis of DNA microarrays by multivariate curve resolution

In this work, the application of a multivariate curve resolution procedure based on alternating least squares optimization (MCR-ALS) for the analysis of data from DNA microarrays is proposed. For this purpose, simulated and publicly available experimental data sets have been analyzed. Application of MCR-ALS, a method that operates without the use of any training set, has enabled the resolution of the relevant information about different cancer lines classification using a set of few components; each of these defined by a sample and a pure gene expression profile. From resolved sample profiles, a classification of samples according to their origin is proposed. From the resolved pure gene expression profiles, a set of over- or underexpressed genes that could be related to the development of cancer diseases has been selected. Advantages of the MCR-ALS procedure in relation to other previously proposed procedures such as principal component analysis are discussed.

Study of solvent effects on the acid–base behaviour of adenine, adenosine 3′,5′-cyclic monophosphate and poly(adenylic) acid in acetonitrile–water mixtures using hard-modelling and soft-modelling approaches

The acid–base behaviour of adenine, adenosine 3′,5′-cyclic monophosphate and poly(adenylic) acid was studied at 37 °C by spectrometric titration in acetonitrile–water mixtures. Hard-modelling and soft-modelling approaches were used for this purpose. The first one, which requires the assumption of a previous chemical model, was used for the study of monomeric compounds and the second one, an alternating least-squares multivariate curve resolution procedure, was used for the study of poly(adenylic) acid, because it does not require any previous chemical model. The p K a values were correlated with either macroscopic or microscopic parameters, such as solvatochromic parameters. The influence of solvent composition on the intramolecular and solute/solvent interactions such as base stacking and hydrogen-bonding, affects the structure, solubility and the number of species detected.

Continuous-Flow and Flow Injection pH Gradients for Spectrophotometric Determinations of Mixtures of Nucleic Acid Components

A procedure for the rapid determination of mixtures of nucleic acid components from the analysis of spectrophotometric multivariate data obtained with continuous-flow and flow injection pH-gradient systems is proposed. Three flow systems have been developed and assayed in which an on-line pH gradient is generated from the mixing and controlled dispersion of acidic and basic titrant solutions. Quantitative determinations of any particular analyte in the unknown samples in the presence of interferences is performed with a single pure standard for this analyte. They are carried out using an alternating least squares multivariate curve resolution procedure. The methods proposed have been validated using synthetic and real sample mixtures. The results obtained are concordant with the labeled values, and the relative prediction errors are around 5%.

Multivariate Curve Resolution: A Possible Tool in the Detection of Intermediate Structures in Protein Folding

Different multivariate data analysis techniques based on factor analysis and multivariate curve resolution are shown for the study of biochemical evolutionary processes like conformational changes and protein folding. Several simulated CD spectral data sets describing different hypothetical protein folding pathways are analyzed and discussed in relation to the feasibility of factor analysis techniques to detect and resolve the number of components needed to explain the evolution of the CD spectra corresponding to the process (i.e., to detect the presence of intermediate forms). When more than two components (the native and unordered forms) are needed to explain the evolution of the spectra, an iterative multivariate curve resolution procedure based on an alternating least squares algorithm is proposed to estimate the CD spectrum corresponding to the intermediate form.

Acid-base and copper (II) complexation equilibria of poly(inosinic)-poly(cytidylic)

The acid-base and copper (II) complexing properties of the heteropolynucleotide poly(inosinic)-poly(cytidylic) acid were studied by means of potentiometric, spectrophotometric, CD, and molecular fluorescence titrations in a working aqueous medium of 0.15 M ionic strength at 37°C. The study of the acid-base equilibria has proved the existence of four different species in the pH interval 2–11. Two new species are detected in the presence of copper(II) ion, at acidic and neutral pH values. Spectrophotometric, CD, melting, and fluorescence data were treated with a multivariate curve resolution procedure that allowed the determination of the number of species simultaneously present in each system [acid-base equilibria, copper(II) complexation equilibria], and the estimation of their concentration profiles and of the pure spectra for each species. From the concentration profiles an unambiguous assignment of which species are present at any pH conditions can be made. Furthermore, the knowledge of the pure uv-visible, fluorescence, and CD spectra recovered by the proposed resolution method for each species brings important structural information. When this mathematical approach was applied to the thermal denaturation data, the melting profile and the uv pure spectra for all the different species or conformations formed in the melting process, were calculated. © 1997 John Wiley & Sons, Inc. Biopoly 42: 271–283, 1997

Application of a Multivariate Curve Resolution Procedure to the Analysis of Second-Order Melting Data of Synthetic and Natural Polynucleotides

The melting behavior of several synthetic polynucleotides and a mixture of natural tRNAs was studied by monitoring the changes in the whole UV absorbance spectrum at several pH values. The second-order absorbance data were analyzed with a soft-modeling multivariate curve resolution procedure that allows the determination of the number of different species or conformations present along the melting experiment and the calculation of the melting profile and the pure spectrum for each chemical species or conformation. The melting temperature, T(m), for each thermal transition can be calculated from the melting profiles, and structural information on the different species or conformations can be obtained from their pure spectra. The multistranded species formed at certain pH conditions show several sharp thermal transitions related to the loss of the initial highly ordered structure. For these transitions, the mixture of species obtained in the denaturing process can be resolved when several data matrices, each giving additional information, are analyzed simultaneously with the mathematical procedure proposed.

Application of a multivariate curve resolution procedure for the study of the acid-base and copper(II) complexation equilibria of polycytidylic acid

The acid-base properties and the copper(II)-complexing behaviour of the polynucleotide polycytidylic acid (poly(C)) were studied by means of potentiometric, spectrophotometric and circular dichroism titrations in a working aqueous medium of 0.15 M ionic strength and at 37°C. Spectrophotometric and circular dichroism data were treated with a multivariate curve resolution procedure that is successfully applied to the study of macromolecular systems, where polyfunctional, conformational and/or polyelectrolytic effects may be present. This procedure allows different sets of spectrometric titrations of the multi-equilibria systems to be analyzed simultaneously. Three spectroscopically different species are detected for poly(C) in the pH range 3.1–7.0. Two of them are forms with different protonation extent and different conformation, and the other is the unprotonated form of poly(C). One new species is detected in the presence of Cu(II) ion.

Application of a new multivariate curve resolution procedure to the simultaneous analysis of several spectroscopic titrations of the copper(II)—polyinosinic acid system

The acid-base properties and the copper(II)-complexing behaviour of the polynucleotide polyinosinic acid have been studied by means of potentiometric and spectrophotometric titrations. Spectrophotometric data have been treated with a new multivariate curve resolution procedure that allows to analyze simultaneously different sets of spectroscopic titrations of the same multi-equilibria system. The results obtained give a real proof of the advantages of the proposed procedure over the single titration analysis: intensity ambiguities are eliminated, as well as rotational ambiguities if the information yielded by at least one of the titrations is selective.

Application of a new multivariate curve resolution procedure to the simultaneous analysis of several spectroscopic titrations of the copper(II)-polyinosinic acid system

Application of a new multivariate curve resolution procedure to the simultaneous analysis of several spectroscopic titrations of the copper(II)-polyinosinic acid system