Monitoring of water sorption and swelling of potato starch-glycerol extruded blend by magnetic resonance imaging and multivariate curve resolution.

Magnetic resonance microimaging (MRµI) is an outstanding technique for studying water transfers in millimetric bio-based materials in a non-destructive and non-invasive manner. However, depending on the composition of the material, monitoring and quantification of these transfers can be very complex, and hence reliable image processing and analysis tools are necessary. In this study, a combination of MRµI and multivariate curve resolution-alternating least squares (MCR-ALS) is proposed to monitor the water ingress into a potato starch extruded blend containing 20% glycerol that was shown to have interesting properties for biomedical, textile, and food applications. In this work, the main purpose of MCR is to provide spectral signatures and distribution maps of the components involved in the water uptake process that occurs over time with various kinetics. This approach allowed the description of the system evolution at a global (image) and a local (pixel) level, hence, permitted the resolution of two waterfronts, at two different times into the blend that could not be resolved by any other mathematical processing method usually used in magnetic resonance imaging (MRI). The results were supplemented by scanning electron microscopy (SEM) observations in order to interpret these two waterfronts in a biological and physico-chemical point of view.

A multivariate curve resolution-alternating least squares (MCR-ALS) technology assisted 1 H-NMR methodology for multi-component quantitation of Trichosanthis Pericarpium injection.

INTRODUCTION: Trichosanthis Pericarpium injection (TPI) is a traditional Chinese medicine preparation obtained from Trichosanthis Pericarpium by extraction, purification and sterilisation. It contains amino acids, alkaloids, nucleotides and other components. Existing quantitative methods only analyse a few components in injections, so this study intends to develop a method for comprehensive analysis of TPI components. OBJECTIVE: To develop a method for quantification of components in TPI by multivariate curve resolution-alternating least squares (MCR-ALS) assisted proton nuclear magnetic resonance (1 H-NMR). METHODS: A 1 H-NMR method was developed for the quantification of components in TPI. For components with independent signals, 3-(trimethylsilyl) propionic-2,2,3,3-d4 acid sodium salt (TSP) was used as an internal standard to calculate the component contents. For components with overlapping signals, the method of MCR-ALS was used. RESULTS: A total of 36 components were identified in TPI, of which 33 were quantified. Methodological validation results showed that the developed 1 H-NMR method has good linearity, accuracy, precision, robustness and specificity. CONCLUSION: The use of 1 H-NMR provides a reliable and universal method for the TPI components identification and quantification. Also, it can be used as a powerful tool for analysing the contents in a complex mixture as a quality control measure.

Coupling of spectrometric, chromatographic, and chemometric analysis in the investigation of the photodegradation of sulfamethoxazole.

A workflow is proposed for the study of the photodegradation process of the sulfamethoxazole (SMX) based on the combination of different experimental techniques, including liquid chromatography, mass spectrometry, UV-Visible spectrophotometry, and the treatment of all the analytical data with advanced chemometric methods. SMX, which is one of the most widely used antibiotics worldwide and has been found at remarkable concentrations in various rivers and effluents over all Europe, was degraded in the laboratory under a controlled source of UV radiation, which simulates the environmental solar radiation (Suntest). Kinetic monitoring of the photodegradation process was performed using UV-Visible spectrophotometric measurements and by further Liquid Chromatography with Diode Array Detector and Mass Spectrometry analysis (LC-DAD-MS). Additionally, the acid-base properties were also investigated to see how the pH can affect the speciation of this substance during the photodegradation process. Based on the Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) application, the proposed chemometric method coped with the large amounts of data generated by the different analytical techniques used to monitor the evolution of the photodegradation process. Their simultaneous analysis involved applying a data fusion strategy and an advanced MCR-ALS constrained analysis, which allowed and improved the description of the complete degradation process, detecting the different species of the reaction, and identifying the possible transformation products formed. A total number of six species were resolved in the degradation process of SMX. In addition to the initial SMX, a second species corresponded to a conformational isomer, and the other four species represented different photoproducts, which have also been identified. Furthermore, three different acid-base species of SMX were obtained, and their pKa values were estimated.

Untargeted analysis of TD-NMR signals using a multivariate curve resolution approach: Application to the water-imbibition kinetics of Arabidopsis seeds.

The aim of this study is to investigate the ability of Time-Domain Nuclear Magnetic Resonance (TD-NMR) combined with Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) analysis to detect changes in hydration properties of nineteen genotypes of Arabidopsis (Arabidopsis thaliana) seeds during the imbibition process. The Hybrid hard and Soft modelling version of MCR-ALS (HS-MCR) applied to raw TD-NMR data allowed the introduction of kinetic models to elucidate underlying biological mechanisms. The imbibition process of all investigated hydrated Arabidopsis seeds could be described with a kinetic model based on two consecutive first-order reactions related to an initial absorption of water from the bulk around the seed and a posteriori hydration of the internal seed tissues, respectively. Good data fit was achieved (LOF % = 0.98 and r2% = 99.9), indicating that the hypothesis of the selected kinetic model was correct. An interpretation of the mucilage characteristics of the studied Arabidopsis seeds was also provided. The presented methodology offers a novel and general strategy to describe in a comprehensive way the kinetic process of plant tissue hydration in a screening objective. This work also proves the potential of the MCR methods to analyse raw TD-NMR signals as alternative to the controversial and time-consuming pre-processing techniques of this kind of data, known to be an ill-conditioned and ill-posed problem.

A spatial constraint to model and extract texture components in Multivariate Curve Resolution of near-infrared hyperspectral images.

This article highlights the importance of properly taking into account spatial structures and features to better resolve near-infrared (NIR) hyperspectral images by Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS), especially when highly mixed components (in terms of spatial and spectral overlap) underlying the systems under study are dealt with. As in the NIR domain these components can explain both chemical properties and physical phenomena, their improved unravelling can therefore represent an alternative or a complement to more standard approaches for, e.g., spectral data preprocessing. These points will be illustrated through the comprehensive analysis of a complex real-world forensic case-study where texture characterization is crucial for the sake of a more appropriate resolution.

Assessment of tissue-specific multifactor effects in environmental -omics studies of heterogeneous biological samples: Combining hyperspectral image information and chemometrics.

The use of hyperspectral imaging techniques in biological studies has increased in the recent years. Hyperspectral images (HSI) provide chemical information and preserve the morphology and original structure of heterogeneous biological samples, which can be potentially useful in environmental -omics studies when effects due to several factors, e.g., contaminant exposure, phenotype,…, at a specific tissue level need to be investigated. Yet, no available strategies exist to exploit adequately this kind of information. This work offers a novel chemometric strategy to pass from the raw image information to useful knowledge in terms of statistical assessment of the multifactor effects of interest in -omic studies. To do so, unmixing of the hyperspectral image measurement is carried out to provide tissue-specific information. Afterwards, several specific ANOVA-Simultaneous Component Analysis (ASCA) models are generated to properly assess and interpret the diverse effect of the factors of interest on the spectral fingerprints of the different tissues characterized. The unmixing step is performed by Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) on multisets of biological images related to each studied condition and provides reliable HSI spectral signatures and related image maps for each specific tissue in the regions imaged. The variability associated with these signatures within a population is obtained through an MCR-based resampling step on representative pixel subsets of the images analyzed. All spectral fingerprints obtained for a particular tissue in the different conditions studied are used to obtain the related ASCA model that will help to assess the significance of the factors studied on the tissue and, if relevant, to describe the associated fingerprint modifications. The potential of the approach is assessed in a real case of study linked to the investigation of the effect of exposure time to chlorpyrifos-oxon (CPO) on ocular tissues of different phenotypes of zebrafish larvae from Raman HSI of eye cryosections. The study allowed the characterization of melanin, crystalline and internal eye tissue and the phenotype, exposure time and the interaction of the two factors were found to be significant in the changes found in all kind of tissues. Factor-related changes in the spectral fingerprint were described and interpreted per each kind of tissue characterized.

Resolution of intermediate surface species by combining modulated infrared spectroscopy and chemometrics.

In this work, aiming at exploiting the essential particularities of modulation excitation spectroscopy (MES) coupled to phase sensitive detection (PSD) and chemometrics, a data analysis procedure was implemented to analyze phase-resolved infrared (IR) data. The fundamental principle of the proceedings is the application of successive multivariate curve resolution-alternating least squares (MCR-ALS) resolutions to MES-PSD IR data. The applicability of the strategy was evaluated in several cases of simulated data considering the effect of spectral band overlapping and presence of noise. Outcomes related to data-processing are depicted in detail. As a proof of concept, the data resolution approach was validated by resolving an experimental real system related to the adsorption-desorption dynamic of oxalic acid on titanium dioxide by in situ IR spectroscopy in attenuated total reflection (ATR) mode. After data resolution, different oxalate species were assigned to each spectral band and information about the kinetics in terms of phase lag was obtained. In the light of the obtained results, this approach is rather appealing in other research fields, very helpful for the non-chemometric community and foresees manifold applications, specially, in catalytic system investigations.

Chemometric analysis of comprehensive two dimensional gas chromatography-mass spectrometry metabolomics data.

Comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS) is a powerful tool for separation and identification of analytes in complex natural samples. In this paper, different chemometric methods were compared for fast non-targeted GC×GC-TOFMS metabolomic profiling of the crustaceous species Daphnia magna and a general chemometric strategy and workflow is proposed. The strategy proposed in this work combined the compression of GC×GC-TOFMS data matrices in the retention time direction using wavelets and the appropriate column-wise data matrix augmentation arrangement, and its modeling by Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS). Using the proposed strategy, eighty different D. magna metabolites were resolved and identified. After calculation of the peak capacities of different columns and peak area changes of these metabolites, the best instrumental configuration and column combination for the GC×GC-TOFMS metabolomic study of D. magna are proposed and discussed. The procedure described in this work can be applied as a general method for untargeted GC×GC-TOFMS data processing and metabolomic profiling.

Spectroscopic and dynamic properties of arachidonoyl serotonin- ß-lactoglobulin complex: A molecular modeling and chemometric study.

UV-Vis absorption data of ß-lactoglobulin (BLG) and arachidonoyl serotonin (AA-5HT) in BLG complex were examined and analyzed using chemometrics method. Analysis of the spectral data matrices by using the multivariate curve resolution-alternating least squares (MCR-ALS) algorithm resulted to the pure concentration calculation and spectral profiles resolution of the chemical constituents and the values of (6.433±0.019)×10(4)M(-1), (4.532±0.007)×10(4)M(-1), (3.364±0.010)×10(4)M(-1) and (2.977±0.013)×10(4)M(-1) as estimated equilibrium constants at 288, 293, 298 and 303K, respectively. The number of chemical constituents involved in the interaction which was extracted by PCA method were free and bound BLG. The spontaneity of the binding process and critical role of hydrogen bonding and van der Waals interactions in stabilizing protein-ligand complex have been designated by negative values of Gibbs free energy, entropy and enthalpy changes. Molecular docking study showed that AA-5HT binds to Val(41), Leu(39), Leu(54), Ile(71), Phe(82), Asn(90), Val(92), Phe(105), Met(107), Glu(108) with the free binding energy of -37.478kJ/mol. Computational studies predicted that in spite of serotonin (5HT) which anchors to the outer surface of BLG by hydrogen bonds, AA-5HT is situated in the calyx pose and stayed there during the entire time of simulation. This binding is accompanying with no apparent influence on secondary structure and partially destabilization of tertiary structure of BLG which pointed the suitability of this protein as drug carrier for AA-5HT.

Quantitative measurements of binary amino acids mixtures in yellow foxtail millet by terahertz time domain spectroscopy.

Terahertz time domain spectroscopy (THz-TDS) combined with chemometrics has been utilized for the qualitative and quantitative analysis of binary mixtures of l-glutamic acid and l-glutamine which have similar chemical structures and properties. The binary mixtures of amino acids were prepared with yellow foxtail millet matrix, substituted for polyethylene (PE) as previously reported. After proper pretreatment of absorption spectra, quantitative analysis was achieved by partial least squares (PLS) and interval partial least squares (iPLS) regressions. The performance of models was evaluated based on the root mean square error of prediction (RMSEP) and correlation coefficient (R(2)) of cross-validations with bootstrapped Latin partitions as criterion. The iPLS yielded better results with low RMSEP (0.39±0.02%, 0.39±0.02%), and higher R(2) values (0.9904, 0.9906) for glutamine and glutamic acid comparing to the conventional PLS models. Multivariate curve resolution alternating least squares (MCR-ALS) was successfully applied for resolution of pure THz spectra and concentration profiles of two amino acids components from mixtures.

Investigating the discrimination potential of linear and nonlinear spectral multivariate calibrations for analysis of phenolic compounds in their binary and ternary mixtures and calculation pKa values.

Vanillin (VA), vanillic acid (VAI) and syringaldehyde (SIA) are important food additives as flavor enhancers. The current study for the first time is devote to the application of partial least square (PLS-1), partial robust M-regression (PRM) and feed forward neural networks (FFNNs) as linear and nonlinear chemometric methods for the simultaneous detection of binary and ternary mixtures of VA, VAI and SIA using data extracted directly from UV-spectra with overlapped peaks of individual analytes. Under the optimum experimental conditions, for each compound a linear calibration was obtained in the concentration range of 0.61-20.99 [LOD=0.12], 0.67-23.19 [LOD=0.13] and 0.73-25.12 [LOD=0.15] µgmL(-1) for VA, VAI and SIA, respectively. Four calibration sets of standard samples were designed by combination of a full and fractional factorial designs with the use of the seven and three levels for each factor for binary and ternary mixtures, respectively. The results of this study reveal that both the methods of PLS-1 and PRM are similar in terms of predict ability each binary mixtures. The resolution of ternary mixture has been accomplished by FFNNs. Multivariate curve resolution-alternating least squares (MCR-ALS) was applied for the description of spectra from the acid-base titration systems each individual compound, i.e. the resolution of the complex overlapping spectra as well as to interpret the extracted spectral and concentration profiles of any pure chemical species identified. Evolving factor analysis (EFA) and singular value decomposition (SVD) were used to distinguish the number of chemical species. Subsequently, their corresponding dissociation constants were derived. Finally, FFNNs has been used to detection active compounds in real and spiked water samples.

Spectrophotometric determination of Rare Earth Elements in aqueous nitric acid solutions for process control.

Noninvasive analytical control is of special interest for the complicated and hazardous production processes. On-line monitoring provides a unique opportunity to determine critical concentrations rapidly and without serious risks to operating personnel and the environment. Models for quantitative determination of concentrations of Rare Earth Elements in complex mixtures in nitric acid serve for these purposes. Here, the feasibility of simultaneous determination of cerium, praseodymium, and neodymium using the whole UV-vis spectroscopic range, together with chemometric data processing, is studied. The predictability of two chemometric techniques, partial least squares regression and correlation constrained multivariate curve resolution-alternating least squares are compared. Models' performances are analyzed in out-of-control cases.

Probing the binding mode of psoralen to calf thymus DNA.

The binding properties between psoralen (PSO) and calf thymus DNA (ctDNA) were predicted by molecular docking, and then determined with the use of UV-vis absorption, fluorescence, circular dichroism (CD) and Fourier transform infrared (FT-IR) spectroscopy, coupled with DNA melting and viscosity measurements. The data matrix obtained from UV-vis spectra was resolved by multivariate curve resolution-alternating least squares (MCR-ALS) approach. The pure spectra and the equilibrium concentration profiles for PSO, ctDNA and PSO-ctDNA complex extracted from the highly overlapping composite response were obtained simultaneously to evaluate the PSO-ctDNA interaction. The intercalation mode of PSO binding to ctDNA was supported by the results from the melting studies, viscosity measurements, iodide quenching and fluorescence polarization experiments, competitive binding investigations and CD analysis. The molecular docking prediction showed that the specific binding most likely occurred between PSO and adenine bases of ctDNA. FT-IR spectra studies further confirmed that PSO preferentially bound to adenine bases, and this binding decreased right-handed helicity of ctDNA and enhanced the degree of base stacking with the preservation of native B-conformation. The calculated thermodynamic parameters indicated that hydrogen bonds and van der Waals forces played a major role in the binding process.