Multivariate Curve Resolution Techniques Research Articles

An automated Peak Group Analysis for vibrational spectra analysis

Peak Group Analysis (PGA) is a multivariate curve resolution technique that attempts to extract single pure component spectra from time series of spectral mixture data. It requires that the mixture spectra consist of relatively sharp peaks, as is typical in IR and Raman spectroscopy. PGA aims to construct from individual peaks the associated pure component spectra in the form of nonnegative linear combinations of the right singular vectors of the spectral data matrix.This work presents an automated PGA (autoPGA) that starts with upstream peak detection applied to time series of spectra, combining different window-based peak detection techniques with balanced peak acceptance criteria and peak grouping to deal with repeated detections. The next step is a single-spectrum-oriented PGA analysis. This is followed by a downstream correlation analysis to identify pure component spectra that occur multiple times. AutoPGA provides a complete pure component factorization of the matrix of measured data. The algorithm is applied to FT-IR data sets on various rhodium carbonyl complexes and from an equilibrium of iridium complexes.

A comparison of multivariate curve resolution with endmember extraction methods in hyperspectral Raman imaging

Hyperspectral Raman imaging not only offers spectroscopic fingerprints but also reveals morphological information such as spatial distributions in an analytical sample. However, the spectrum-per-pixel nature of hyperspectral imaging (HSI) results in a vast amount of data. Furthermore, HSI often requires pre- and post-processing steps to extract valuable chemical information. To derive pure spectral signatures and concentration abundance maps of the active spectroscopic compounds, both endmember extraction (EX) and Multivariate Curve Resolution (MCR) techniques are widely employed. The objective of this study is to carry out a systematic investigation based on Raman mapping datasets to highlight the similarities and differences between these two approaches in retrieving pure variables, and ultimately provide guidelines for pure variable extraction. Numerical simulations and Raman mapping experiments on a mixture of pharmaceutical powders and on a layered plastic foil sample were conducted to underscore the distinctions between MCR and EX algorithms (in particular Vertex Component Analysis, VCA) and their outputs. Both methods were found to perform well if the dataset contains pure pixels for each of the individual components. However, in cases where such pure pixels do not exist, only MCR was found to be capable of extracting the pure component spectra.

Study of the perturbed hydrogen-bonded water structure of the hydration shell of N-Methyl-Acetamide by Raman multivariate curve resolution spectroscopy

In this work, Raman scattering measurement combined with multivariate curve resolution (Raman-MCR) technique has been used to obtain the solute correlated spectra (SC) of N-methyl-acetamide (NMA) in an aqueous medium. The SC spectra is composed of NMA intramolecular vibrations and NMA induced-perturbations of water OH stretch vibrations. The SC spectra obtained for different mole-fractions of NMA reveal that as the mole-fraction of NMA increases the perturbed water interacting with the amide group is retained whereas it gets depleted from the hydrophobic hydration shell of the methyl group of NMA. The high wavenumber region of the OH spectrum shows that the fraction of the weakly hydrogen-bonded population is higher in the bulk water compared to perturbed water. The Raman spectra of perturbed OH spectral features due to hydration of the hydrophobic methyl group of NMA have been extracted from SC spectra obtained at low mole-fraction of NMA by the MCR technique. Its spectral feature resembles the SC-OH spectra of hydrophobic hydration of the methyl group of methanol.

Multispectral Raman Differentiation of Malignant Skin Neoplasms In Vitro: Search for Specific Biomarkers and Optimal Wavelengths.

Confocal scanning Raman and photoluminescence (PL) microspectroscopy is a structure-sensitive optical method that allows the non-invasive analysis of biomarkers in the skin tissue. We used it to perform in vitro diagnostics of different malignant skin neoplasms at several excitation wavelengths (532, 785 and 1064 nm). Distinct spectral differences were noticed in the Raman spectra of basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), compared with healthy skin. Our analysis of Raman/PL spectra at the different excitation wavelengths enabled us to propose two novel wavelength-independent spectral criteria (intensity ratios for 1302 cm-1 and 1445 cm-1 bands, 1745 cm-1 and 1445 cm-1 bands), related to the different vibrational "fingerprints" of cell membrane lipids as biomarkers, which was confirmed by the multivariate curve resolution (MCR) technique. These criteria allowed us to differentiate healthy skin from BCC and SCC with sensitivity and specificity higher than 95%, demonstrating high clinical importance in the differential diagnostics of skin tumors.

Stain-free mapping of polymer-blend morphologies via application of high-voltage STEM-EELS hyperspectral imaging to low-loss spectra

Polymer blends composed of multiple types of polymers are used for various industrial applications; therefore, their morphologies must be understood to predict and improve their physical properties. Herein, we propose a spectral imaging method based on scanning transmission electron microscopy (STEM) and electron energy-loss spectroscopy to map polymer morphologies with nanometric resolution as an alternative to the conventional electron staining technique. In particular, the low-loss spectra of the 5–30 eV energy-loss region were measured to minimize electron irradiation damage rather than the core-loss spectra, such as carbon K-shell absorption spectra, which require significantly longer recording times. Medium-voltage (200 kV) and high-voltage (1000 kV) STEM was used at various temperatures to compare the degrees of electron-beam damage resulting from various electron energies and sample temperatures. A multivariate curve resolution technique was used to isolate the constituent spectra and visualize their distributions by distinguishing the characteristic peaks derived from various chemical species. High-voltage STEM was more useful than medium-voltage STEM for analyzing thicker samples while suppressing ionization damage.

SERS-TLC Device for Simultaneous Determination of Sulfamethoxazole and Trimethoprim in Milk

The aim of this work is to develop a device based on thin-layer chromatography coupled with surface-enhanced Raman spectroscopy (TLC-SERS) to analyze sulfamethoxazole (SMX) and trimethoprim (TMP) in commercial milk samples using chemometric tools. Samples were eluted in TLC plates, and a central composite design (CCD) of two factors was performed to optimize the gold nanoparticle dispersion on TLC plates for SERS, aiming at the detection of both drugs at concentrations close to their maximum residual limits (MRLs). Following the optimization, hyperspectral images from the SERS were captured of the TLC plates. Multivariate curve resolution (MCR-ALS) and independent component analysis (ICA) chemometric techniques were used to extract the signals of the analytes. All the samples presented recovery values of 81–128% for TMP. The quantification of SMX was not possible due to SERS suppression by an interferent. However, it was possible to detect SMX at a concentration of two times the MRL (8.0 × 10−7 mol·L−1). The results demonstrate that the TLC-SERS device is a potential tool for the quantification of TMP and the detection of SMX in milk.

Multiple adulterants detection in turmeric powder using Vis-SWNIR hyperspectral imaging followed by multivariate curve resolution and classification techniques

In the present contribution, visible and short wavelengths of near infrared hyperspectral imaging (Vis-SWNIR-HSI) combined with different chemometric techniques is proposed as a novel technique for turmeric authentication and multiple adulterants (corn flour, rice flour, starch, wheat flour, and zedoary) detection. In this regard, twenty-three samples of turmeric were collected as whole rhizomes or powdered from seven countries and then their VIS-SWNIR hyperspectral images were obtained in 400–1000 nm using SPECIM IQ HSI device. Two multivariate resolution techniques of multivariate curve resolution-alternating least squares (MCR-ALS) and mean-field independent component analysis (MF-ICA) were used to extract pure spatial and spectral profiles of the components, and their results were compared by projecting their solutions in the area of feasible solutions (AFSs). Then, the distribution maps of turmeric component obtained using MCR-ALS and MF-ICA were used for authentication. Principal component analysis (PCA) was used to find the pattern of authentic samples using their distribution maps. Additionally, data-driven soft independent modeling of class analogy (DD-SIMCA) was employed to find boundary between authentic and adulterated turmeric samples. On this matter, good class modelling results with sensitivity of 95 % and specificity of 100 % were obtained. Finally, partial least squares-discriminant analysis (PLS-DA) was utilized for discrimination of the of adulternats and their in binary and multi-class classification modes. On this matter, PLS-DA accuracies were acceptable for binary and mixed samples which confirmed the validity of the proposed method.

Synthesis of Nanoceria with Varied Ratios of Ce3+/Ce4+ Utilizing Soluble Borate Glass.

Mixed-valence cerium oxide nanoparticles (nanoceria) have been investigated with pronounced interest due to a wide range of biomedical and industrial applications that arises from its remarkable redox catalytic properties. However, there is no understanding of how to control the formation of these two types of nanoceria to obtain Ce3+/Ce4+ ratios required in various applications. In this work, using a soluble borate glass, nanoceria with specific ratios of Ce3+/Ce4+ are created and extracted via controlled glass-melting parameters. Glass embedded with nanoceria as well as nanoceria extracted from the glass were studied via XANES and fitted with the Multivariate Curve Resolution (MCR) technique to calculate the ratio of Ce3+/Ce4+. Results show that mixed-valence nanoceria with specific ratios are hermetically sealed within the glass for long durations. When the glass dissolves, the mixed-valence nanoceria are released, and the extracted nanoceria have unchanged Ce3+/Ce4+ ratios. Furthermore, TEM investigation on released nanoceria show that the nanoceria consist of several different structures. Although nanocrystal structures of Ce7O12, Ce11O20, and Ce2O3 contribute to the reduced state, a new quasi-stable phase of CeO1.66 has been observed as well.

Estimating more than two pure component spectra from only two mixture spectra using two-dimensional correlation

A procedure is described to estimate the pure component spectra of mixtures from only a pair of available spectra even when there are more than two component species present in the system. In contrast, traditional multivariate curve resolution (MCR) technique cannot be used for such a case. The method relies on the use of two-trace two-dimensional (2T2D) correlation spectroscopy. Asynchronous 2T2D spectrum is used to identify the characteristic bands most strongly associated with the individual mixture component species. Correlation coefficients derived from the synchronous 2T2D spectrum are used to obtain a set of correlative filtering functions to distribute the spectral intensity of the average spectrum among the estimates of the pure component spectra. Efficacy of the method was demonstrated using a pair of ATR IR spectra obtained for two solution mixtures containing three main ingredients with very similar compositions. Relatively congested and overlapped spectral region was used first for the demonstration, and reasonable resolution was accomplished yielding a set of the estimates of pure component spectra with most of the expected pertinent features included. The analysis was then extended to a broader spectral region containing well-isolated spectral signatures of individual components for positive validation. While traditional MCR technique seems to perform better with a large number of spectra, this technique can be effectively used in conjunction with MCR to improve its stability and performance, especially under some challenging conditions.

Application of MCR-ALS with EFA on FT-IR spectra of lipid bilayers in the assessment of phase transition temperatures: Potential for discernment of coupled events

Temperature-dependent transmission FT-IR spectroscopy and DSC measurements were conducted on lipid multibilayers constituted from 1,2-dipalmitoyl-sn-glycero-3-phosphocholine. Lipid multibilayers made from 1,2-dipalmitoyl-sn-glycero-3-phosphoethanolamine, which do not form a ripple phase, were examined as a reference. Spectra were analyzed using multivariate curve resolution technique with alternating least squares and evolving factor analysis (MCR-ALS with EFA) and lipid phase transition temperatures were determined. Polar parts of lipid molecules exert greater response on a ripple phase formation than non-polar ones. However, vibrational signatures of hydrocarbon chains with intramolecular origins display certain qualitative differences that pave the way for future work oriented on uncoupling the events that drive ripple phase formation.

Characterization of the unimodality constraint as an effective chemistry-based condition in resolving of chemical processes data

Multivariate curve resolution (MCR) techniques are powerful methods for qualitative and quantitative analyses of spectral data. However, MCR methods suffer from non-unique solutions - instead of a unique solution usually a continuum of possible pure component decomposition exists. This ambiguity is covered in a low-dimensional way by the so-called area feasible solutions (AFS). In the quantitative purpose, the rotational ambiguity has a negative effect on the accuracy of the results. By applying additional constraints on the profiles the range of feasible solutions can considerably improve the reliability of quantitative results. Another approach to analyze the size of the continuum of possible pure component profiles is to compute the extrema of the signal contribution function (SCF). The SCF can also be applied under additional constraints.In this study we analyze the restriction of the AFS and the change of the SCF-extrema under additional unimodality constraints. An interesting effect is stated for two examples namely that the unimodality constraint can divide a single subset of the AFS into two parts. In such cases the SCF cannot resolve the ambiguity completely. The effect of the unimodality constraints has been systematically investigated on two- and three-component simulation data sets. This phenomenon are also studied for an experimental data set related to the spectrophotometric titration of a mixture of ligand (3,7-diazanonanedioic acid diamide) and Cu2+ with varying degrees of acidity. Imposing of unimodality constraints as an effective chemically meaningful limitation on the feasible solutions of MCR methods is strongly recommended.

The Interaction of Hemoglobin With Sodium Dodecyl Sulfate in Presence of Ascorbic Acid

Background: Hemoglobin (Hb), oxygen, carbon dioxide, and electron transporter of the body, may enter to an oxidation process that can convert oxyhemoglobin (oxyHb) to methemoglobin (metHb) and hemichrome. Surfactants can facilitate oxidation process that may accumulate hemichrome in red blood cells. Methods: In the present study, the interaction of purified Hb with sodium dodecyl sulfate (SDS, 0-5 mM) and ascorbic acid (AA, 0-5 mM) was evaluated by UV-Vis spectroscopy and multivariate curve resolution techniques. Results: Reconstructed spectral and concentration profiles showed three forms of Hb name as oxyHb, metHb, and hemichrome with lack of fit values less than 1.85%. AA hindered the oxidation process of Hb. Conclusion: A decrease in critical micelle concentration of SDS in the presence of AA and interaction of AA with hydrogen peroxide, which is produced during the interaction of Hb with SDS, are two reasons for diminution in the oxidation process of Hb when accompanied with AA.

Using Gas Chromatography–mass Spectrometry in Conjunction with Chemometrics Technique for Characterization of Iranian Essential Oils Myrtus communis L.

Myrtus communis L. essential oils were extracted by simultaneous distillation extraction method and studied with gas chromatography–mass spectrometry (GC–MS). The obtained mass spectra were investigated by searching in NIST library, and 54 components were identified, but due to several problems such as varying baseline due to column bleed, low signal-to-noise ratio of peaks, and co-elution, some of components did not have satisfactory match factors. Chemometric resolution techniques were used to resolve these problems. The number of identified components was expanded to 67 using combination of GC–MS with resolution technique. In addition, the presence of worthful components such as α-Pinene (36.19%), 1,8-Cineole (11.44%), d-Limonene (10.04%), Linalool (7.76%), α-Terpineol (5.51%), linalyl acetate (4.19%) and α-Terpineol acetate (2.61%) in Myrtle essential oils have been verified. The results of this study indicate that joining of hyphenated chromatographic methods and multivariate curve resolution techniques can be useful for comprehensive, rapid, and accurate analysis of complex natural product such as Iranian Myrtle essential oils.

Trilinear self‐modeling curve resolution using Borgen‐Rajkó plot

AbstractModern analytical instruments provide measurement data arrays with full of hidden and redundant information. Multivariate curve resolution (MCR) techniques decompose data set to physic‐chemically meaningful abstract profiles. On the other hand, for such data matrices, Borgen‐Rajkó self‐modeling curve resolution (SMCR) techniques reveal all possible solutions analytically under the minimal assumption. Although Lawton‐Sylvestre (LS) and Borgen methods have been proposed for the non‐negative curve resolution of two‐component and three‐component systems, there is still a great deal of interest to include further restrictions on the Borgen‐Rajkó SMCR. As modern hyphenated analytical instruments produce multiway (eg, three‐way) arrays, multiway analysis (eg, trilinear decomposition) was received much more popularity by chemists.This contribution is decicated to the extension of the Borgen algorithm to the trilinear data sets. The Borgen method incorporating trilinearity constraint is approached in an analogy to the trilinear Lawton‐Sylvester method. The proposed analytical triadic decomposition is applied to the simulated three‐way arrays with full rank and rank overlap (a type of rank deficiency) loadings. Finally, the proposed algorithm is further exemplified with a three‐component spectrofluorimetric study of acid‐base equilibrium of the pyridoxine. Investigating feasible regions of the simulated and experimental three‐component arrays reveal interesting additional information.

A conceptual view to the area correlation constraint in multivariate curve resolution

Multivariate curve resolution (MCR) techniques are widely employed to study chemical systems and unravel underlying physic-chemical information. Different constraints have been introduced to reduce the extent of uncertainty in the results of MCR-ALS. It is important to study the effect of each constraint on the range of feasible solutions in the curve-resolution studies. As each constraint uses specific information, constraints have different effects on the amount of uncertainty.In the present contribution, condition which resulted in uniqueness applying area correlation constraint (ACC) was illustrated. Additionally, the effect of area correlation constraint (relative known-value constraints) on the amount of uncertainty in the resolved profiles was investigated and visualized in detail. Besides, some rules were established for determining the required number and composition of calibration samples. To illustrate how ACC leads to the uniqueness of a MCR result in the presence of validation samples, the extent of rotational ambiguity was evaluated by analyzing different simulated and experimental data sets. The latter involves the quantification of Phenylalanine and Tyrosine in a second-order excitation-emission fluorescence data sets.

Identification and quantification of single constituents in groundwater with Fourier-transform infrared spectroscopy and Positive Matrix Factorization

Fourier-transform infrared (FTIR) spectra of environmental samples usually comprise superposed pure component spectra of the individual constituents present. Thus, evaluation of band intensities and reconstruction of FTIR spectra by inverse techniques is often prone to ambiguity. Frequently applied multivariate curve resolution techniques (PCA, PCR, PLS) reduce the complexity in the observed spectra by isolating recurring components. However, due to mathematical constraints, such components usually do not resemble the physical spectra of the single constituents in mixed samples, which is, by contrast, principally possible with Positive Matrix Factorization (PMF). In the present study, we tested if PMF is appropriate to conduct quantitative FTIR spectroscopy on environmental samples. We quantified the concentration of humic acid-coated goethite (HA-Goe) in groundwater from two sites, which was sampled before and after HA-Goe was injected into the aquifers. PMF could successfully reconstruct the superposed FTIR spectra of the solids from freeze-dried groundwater, with explained variances of >0.99 and mean residuals of <0.01 (normalized on maximum absorbance). Due to normalization on an internal standard (potassium ferrocyanide), PMF allowed for the quantitative interpretation of compounds that were represented by dedicated PMF components (HA-Goe, kaolinite, montmorillonite, organic matter) even without a priori knowledge on the sample composition. The obtained HA-Goe concentrations agreed very well with those calculated from Fe concentrations determined with ICP-OES (correlation coefficients>0.96). Due to i) the use of objective criteria, which quantified the accuracy and uncertainty of each PMF solution, ii) repetitive PMF runs with randomized initial conditions and iii) physically meaningful PMF-components that resemble the FTIR spectra of single compounds in heterogeneously composed samples, the application of expert knowledge is replicable and less dependent on the operator than in other approaches of quantitative FTIR spectroscopy.

Synthesis and photodegradation studies of analogues of muscle relaxant 1,4-dihydropyridine compounds.

This paper describes the synthesis of 1,4-dihydropyridine compounds (DHPs) endowed with good muscle relaxant activity and stability to light. Six new condensed DHPs were synthesized by the microwave irradiation method. A long-chain ester moiety [2-(methacryloyloxy)ethyl] and various substituents on the phenyl ring were demonstrated to affect the muscle relaxant activity occurring in isolated rabbit gastric fundus smooth muscle strips. Forced photodegradation conditions were applied to the molecules according to the ICH rules. The degradation profile of the drugs was monitored by spectrophotometry coupled with the multivariate curve resolution technique. Formation of the oxidized pyridine derivative was observed for all the studied DHPs, except for one compound, which showed very fast degradation and formation of a second photo-product. Pharmacological tests on the molecules showed a good muscle relaxing effect, with a mechanism similar to that of nifedipine, however, proving to be more stable to light.

Mechanisms of Chromate, Selenate, and Sulfate Adsorption on Al-Substituted Ferrihydrite: Implications for Ferrihydrite Surface Structure and Reactivity

Ferrihydrite is a nanocrystalline Fe (hydr)oxide and important sink for environmental contaminants. Although Fe (hydr)oxides are rarely pure in natural systems, little is known about the effects of structural impurities such as Al on the surface properties and reactivity of ferrihydrite. In this study, we characterized the adsorption mechanisms of chromate, selenate, and sulfate on Al-substituted ferrihydrite (0, 6, 12, 18, and 24 mol % Al) using in situ attenuated total reflection Fourier transform infrared spectroscopy. Spectral data sets recorded as a function of pH were processed using a multivariate curve resolution technique to identify which types of surface species form and to generate their concentration profiles as a function of pH and Al content. Results show a significant increase in relative fraction of outer-sphere complexes for all three oxyanions with increasing Al substitution. In addition, the effect of Al substitution is found to be mechanism-specific in the case of chromate, with bidentate complexes disproportionately suppressed over monodentate complexes at higher Al contents. Overall, our findings have important implications for the fate of chromate, selenate, and sulfate in subsurface environments and offer new insight into the surface reactivity of Al-ferrihydrite.

A multiresolution approach for the convergence acceleration of multivariate curve resolution methods

Modern computerized spectroscopic instrumentation can result in high volumes of spectroscopic data. Such accurate measurements rise special computational challenges for multivariate curve resolution techniques since pure component factorizations are often solved via constrained minimization problems. The computational costs for these calculations rapidly grow with an increased time or frequency resolution of the spectral measurements.The key idea of this paper is to define for the given high-dimensional spectroscopic data a sequence of coarsened subproblems with reduced resolutions. The multiresolution algorithm first computes a pure component factorization for the coarsest problem with the lowest resolution. Then the factorization results are used as initial values for the next problem with a higher resolution. Good initial values result in a fast solution on the next refined level. This procedure is repeated and finally a factorization is determined for the highest level of resolution. The described multiresolution approach allows a considerable convergence acceleration. The computational procedure is analyzed and is tested for experimental spectroscopic data from the rhodium-catalyzed hydroformylation together with various soft and hard models.

Elimination of chromatographic and mass spectrometric problems in GC-MS analysis of Lavender essential oil by multivariate curve resolution techniques: Improving the peak purity assessment by variable size moving window-evolving factor analysis.

In analysis of complex natural matrices by gas chromatography-mass spectrometry (GC-MS), many disturbing factors such as baseline drift, spectral background, homoscedastic and heteroscedastic noise, peak shape deformation (non-Gaussian peaks), low S/N ratio and co-elution (overlapped and/or embedded peaks) lead the researchers to handle them to serve time, money and experimental efforts. This study aimed to improve the GC-MS analysis of complex natural matrices utilizing multivariate curve resolution (MCR) methods. In addition, to assess the peak purity of the two-dimensional data, a method called variable size moving window-evolving factor analysis (VSMW-EFA) is introduced and examined. The proposed methodology was applied to the GC-MS analysis of Iranian Lavender essential oil, which resulted in extending the number of identified constituents from 56 to 143 components. It was found that the most abundant constituents of the Iranian Lavender essential oil are α-pinene (16.51%), camphor (10.20%), 1,8-cineole (9.50%), bornyl acetate (8.11%) and camphene (6.50%). This indicates that the Iranian type Lavender contains a relatively high percentage of α-pinene. Comparison of different types of Lavender essential oils showed the composition similarity between Iranian and Italian (Sardinia Island) Lavenders.