Band-target Entropy Minimization Research Articles

Mass spectral reconstruction of LC/MS data with entropy minimization

Liquid chromatography-mass spectrometry (LC/MS) is a major technique for the analysis of complex samples. Data processing for LC/MS untargeted analysis is very challenging due to the presence of asymmetric, co-eluted at trace-level peaks. We report a multivariate curve resolution approach based on entropy minimization to resolve LC/MS data. Our liquid chromatography band-target entropy minimization (lc-BTEM) approach enables automated resolution of complex LC/MS peaks without the requirement of any parameter inputs. We demonstrated the performance of our approach on a co-eluted and trace-level peaks in an American ginseng extract. Consequently, we were able to putatively annotate the presence of more than 50 ginsenosides. The extracted pure mass spectra and in-source fragments information further enabled the annotation of [M+H]+, [M+H-Neu]+ and [M-H]- ions. This approach is expected to benefit LC/MS untargeted analysis and data independent analysis of complex samples.

Adaptive band target entropy minimization: Optimization for the decomposition of spatially offset Raman spectra of bone

AbstractWe report a novel variant of band target entropy minimization (BTEM), adaptive band target entropy minimization (A‐BTEM), which offers an improved ability to accurately decompose mixed spectra obtained from complex multicomponent systems. Several key challenges have existed in the application of the basic BTEM approach to decompose spatially offset Raman spectra of bone underneath soft tissues and other multilayer systems demonstrating high collinearity between the spectra of individual components; these have included instabilities, signal mixing, and spectral artefacts that have precluded the reliable use of BTEM in such situations. By using automatic factor selection, an adaptive penalty function in addition to metaheuristic optimization strategies, we demonstrate that high‐quality spectral reconstructions of underlying pure component spectra can be obtained with typical correlation coefficients >0.996. Furthermore, we ascertain the behaviour of A‐BTEM with different input datasets, both synthetic and real, displaying varying signal‐to‐noise ratio, signal composition, and numbers of spectra. We thereby identify the multifarious parameter space in the application of A‐BTEM and the quality of data required for the most accurate spectral estimates of pure component spectra.

Lower limit of detection achieved by raw band-target entropy minimization (rBTEM) for trace and coeluted gas chromatography-mass spectrometry components

Full-scan gas chromatography-mass spectrometry (GC-MS) provides rapid untargeted screening and detection for trace and coeluting components. Even though modern mass spectrometers have sufficient sensitivity to detect signals of trace components, this does not translate to the ability of the data-processing approach to isolate interference signals and extract pure component spectra. The raw band-target entropy minimization (rBTEM) approach based on band-target entropy minimization was previously reported to enable accurate spectra reconstruction from severely coeluting and trace GC-MS components. In this study, we evaluate the limit of detection for the approach. This study is systematically compared to classical spectral deconvolution for measurements of chlorpyrifos pesticide standards sampled with solid-phase microextraction at various concentration levels in both distilled and seawater media. The rBTEM approach was able to detect chlorpyrifos at 0.5 ppb in distilled water and 1.0 ppb in seawater while the classical approach provided numerous false negative results. In addition, rBTEM consistently provided higher match scores for chlorpyrifos. Attaining accurate identification of trace and coeluting components from full-scan GC-MS analysis has immense potential impact in biomedical, agricultural, and environmental work.

Band target entropy minimization and target partial least squares for spectral recovery and quantitation

The resolution and quantitation of pure spectra of minority components in measurements of chemical mixtures without prior knowledge of the mixture is a challenging problem. In this work, a combination of band target entropy minimization (BTEM) and target partial least squares (T-PLS) was used to obtain estimates for single pure component spectra and to calibrate those estimates in a true, one-at-a-time fashion. This approach allows for minor components to be targeted and their relative amounts estimated in the presence of other varying components in spectral data. The use of T-PLS estimation is an improvement to the BTEM method because it overcomes the need to identify all of the pure components prior to estimation. Estimated amounts from this combination were found to be similar to those obtained from a standard method, multivariate curve resolution-alternating least squares (MCR-ALS), on a simple, three component mixture dataset. Studies from two experimental datasets demonstrate where the combination of BTEM and T-PLS was used to model the pure component spectra and to obtain concentration profiles of minor components, but MCR-ALS could not.

An optimized band-target entropy minimization for mass spectral reconstruction of severely co-eluting and trace-level components.

Gas chromatography-mass spectrometry (GC-MS) is a versatile analytical method but its data is usually complicated by the presence of severely co-eluting and trace-level components. In this work, we introduce an optimized band-target entropy minimization approach for the analysis of complex mass spectral data. This new approach enables an automated mass spectral analysis which does not require any user-dependent inputs. Moreover, the approach provides improved sensitivity and accuracy for mass spectral reconstruction of severely co-eluting and trace-level components. The accuracy of our approach is compared to the automatic mass spectral deconvolution and identification system (AMDIS) with two controlled mixtures and a sample of Eucalyptus essential oil. Our approach was able to putatively identify 130 compounds in Eucalyptus essential oil, which was 46% in excess of that identified by AMDIS. This new approach is expected to benefit GC-MS analysis of complex mixtures such as biological samples and essential oils, in which the data are often complicated by co-eluting and trace-level components. Graphical abstract ᅟ.

Systematic comparison and potential combination between multivariate curve resolution–alternating least squares (MCR‐ALS) and band‐target entropy minimization (BTEM)

AbstractThis work does a systematic comparative evaluation of 2 methods originating from different fields, both dedicated to the problem of curve resolution/unmixing: multivariate curve resolution–alternating least squares (MCR‐ALS) and band‐target entropy minimization (BTEM). The MCR‐ALS factorizes the data matrix into spectral and concentration profiles that satisfy constraints expressing physicochemical knowledge on the analyzed system. The BTEM reconstructs the pure components' spectral profiles as linear combinations of singular vectors that minimize the spectral entropy and contain specific peaks. Both methods were applied to 40 simulated and one real data set. The simulated data were generated from real spectral and concentration profiles that include different types of spectroscopy (mass spectrometry, Raman, and UV‐visible), data structures (random mixtures, images, and reaction system), and noise levels; the real data set was a Raman image of kidney calculus. For most data sets, both methods yielded accurate solutions, with a correlation between reference and resolved profiles >0.99. However, MCR‐ALS (here used with nonnegativity constraint only) was affected by rotational ambiguity in the recovery of spectral profiles coming from systems with high correlation or overlap in the concentration direction, whereas BTEM tended to distort UV‐visible spectra, a kind of measurement far in nature from low entropy conditions. MCR‐ALS solutions were more stable than BTEM to the increase in noise level. This work also explores the possibility of combining the 2 methods by performing them in sequence. The results show that this combination can significantly improve the outcome as compared to either method applied alone.

Zein film functionalized atomic force microscopy and Raman spectroscopic evaluations on surface differences between hard and soft wheat flour

Fundamental differences exist between the surfaces of hard and soft wheat flours. Ranging from surface roughness, chemical composition to cohesive forces, these differences could affect the flow behaviour of wheat flour. In an attempt to address these differences, we developed a zein film functionalized atomic force microscopy technique to evaluate the surface forces on compacts of hard, soft wheat flour, and its pure components, starch and gluten. Using the pure wheat components as the baseline, we analyzed the chemical compositional differences via Raman spectroscopy coupled with Band–Target Entropy Minimization (BTEM®) algorithm. The zein film functionalization distinguishes between wheat starch and gluten with stronger interactions on starch at least twice that of gluten. In general, soft wheat flour has higher adhesion forces than hard wheat flour of the same particle size. Higher starch content at the expense of protein in soft wheat flour seems to suggest the difference. However, the microstructural difference of starch granules on the compacted surface could describe more adequately the effect of particle size on the interaction forces.

Band target entropy minimization for retrieving the information of individual components from overlapping chromatographic data

Band target entropy minimization (BTEM) is a self-modeling curve resolution (SMCR) approach relying on non-negative criterion and minimization of Shannon entropy. In this study, BTEM algorithm was applied to retrieving the information of individual components from overlapping gas chromatography–mass spectrometry (GC–MS) data. The algorithm starts with dividing the whole data into bands along the retention time. In each band, singular value decomposition (SVD) is used to decompose the data into scores and loadings. Because the pure chromatographic signal possesses the lowest Shannon entropy, the chromatographic signal of each component can be constructed by optimizing the combination of the loadings with minimal Shannon entropy under non-negative criterion. To show the efficiency of the algorithm, a simulated four-component overlapping GC–MS data and an experimental GC–MS data of 18 organophosphorus pesticide mixture are investigated. The results show that both the chromatographic profiles and mass spectra of the components can be successfully extracted from the overlapping signals.

Qualitative Analyses of Open-Path Fourier Transform Spectra

Abstract Open-path Fourier transform infrared (OP/FT-IR) spectra were arranged into data matrices and then analyzed with target factor analysis (TFA) and band-target entropy minimization (BTEM), respectively, aiming to reconstruct the spectra of interested molecules. Five sessions of continuous OP/FT-IR monitoring were carried out around farms. For each session, the spectra were arranged row-wisely in the order of measuring time, which yielded five matrices for data analysis. The analyses results showed that both TFA and BTEM could reconstruct spectral features of target molecules from the spectra data matrix, but performance of the two methods differed slightly. TFA can retrieve spectral features of target molecules in the presence of interferences, and the reconstructed spectrum is similar to corresponding reference. BTEM can implement such spectral retrieval without the reference spectrum. This paper presents not only the application of BTEM method to qualitative analyses of OP/FT-IR spectra, but also a thorough comparison between TFA and BTEM, which is helpful to qualitative analysis of complex multi-component systems.

Structural and chemical analysis of native and malted barley kernels by polarized Raman spectroscopy (PRS)

In this work, barley kernel sections embedded in resin were mapped using polarized Raman spectroscopy (PRS) for evaluation of both chemical and structural changes in endosperm upon malting. A multivariate analysis that includes Band-Target Entropy Minimization (BTEM) was used to resolve the individual spectrum of components present on sections and to reconstruct images based on their relative signal. BTEM-resolved spectra matched with several individual model compound spectra that allowed the identification of protein, starch, non-starch carbohydrates, esterified ferulic acid and embedding media. Raman images also revealed chemical changes in the cell walls within starchy endosperm and inside cells due to enzymatic reactions in malting. The anisotropic response of the C–O–C Raman band associated with the orientation of ordered structures inside starch granules remains despite a notorious shape distortion of large starch granules.

Simultaneous determination of several crystal structures from powder mixtures: the combination of powder X-ray diffraction, band-target entropy minimization and Rietveld methods

Crystal structure determination is the key to a detailed understanding of crystalline materials and their properties. This requires either single crystals or high-quality single-phase powder X-ray diffraction data. The present contribution demonstrates a novel method to reconstruct single-phase powder diffraction data from diffraction patterns of mixtures of several components and subsequently to determine the individual crystal structures. The new method does not require recourse to any database of known materials but relies purely on numerical separation of the mixture data into individual component diffractograms. The resulting diffractograms can subsequently be treated like single-phase powder diffraction data,i.e.indexing, structure solution and Rietveld refinement. This development opens up a host of new opportunities in materials science and related areas. For example, crystal structures can now be determined at much earlier stages when only impure samples or polymorphic mixtures are available.

Decomposition of in vivo spatially offset Raman spectroscopy data using multivariate analysis techniques

The decomposition of spatially offset Raman spectra for complex multilayer systems, such as biological tissues, requires advanced techniques such as multivariate analyses. Often, in such situations, the decomposition methods can reach their limits of accuracy well before the limits imposed by signal‐to‐noise ratios. Consequently, more effective reconstruction methods could yield more accurate results with the same data set. In this study we process spatially offset Raman spectroscopy (SORS) data with three different multivariate techniques (band‐target entropy minimization (BTEM), multivariate curve resolution and parallel factor analysis (PARAFAC)) and compare their performance when analysing a spectrally challenging plastic model system and an even more challenging problem, the analysis of human bone transcutaneously in vivo. For the in vivo measurements, PARAFAC's requirement of multidimensional orthogonal data is addressed by recording SORS spectra both at different spatial offsets and at different anatomical points, the latter providing added dimensionality through the variation of skin/soft tissue thickness.The BTEM and PARAFAC methods performed the best on the plastic system with the BTEM more faithfully reconstructing the major Raman bands and PARAFAC the smaller more heavily overlapped features. All three methods succeeded in reconstructing the bone spectrum from the transcutaneous data and gave good figures for the phosphate‐to‐carbonate ratio (within 2% of excised human tibia bone); the PARAFAC gave the most accurate figure for the mineral‐to‐collagen ratio (20% less than excised human tibia bone).Previous studies of excised bones have shown that certain bone diseases (such as osteoarthritis, osteoporosis and osteogenesis imperfecta) are accompanied by compositional abnormalities that can be detected with Raman spectroscopy, the utility of a technique which could reconstruct bone spectra accurately is manifest. The results have relevance on the use of SORS in general. © 2014 Crown copyright. Journal of Raman Spectroscopy published by John Wiley & Sons, Ltd.

Combining in situ FTIR spectroscopy, BTEM analysis, bulk density measurements and DFT for two Diels–Alder reactions. A general approach for partial molar volume and reaction volume analyses

Two organic reactions, namely the reaction of cyclopentadiene (CPD) and dimethyl acetylene dicarboxylate (DMAD) and the dimerization reaction of CPD were conducted in toluene at 298.1 K at atmospheric pressure and measured by in situ FTIR spectroscopy. Band-target entropy minimization (BTEM) analyses were employed for obtaining the pure component spectra of the solutes and their corresponding concentrations. The solute concentrations and the bulk density data were used to evaluate the partial molar volumes of the solute constituents. The partial molar volumes evaluated from this multi-component approach were in good agreement with those determined from independent binary solution measurements. The corresponding volumes of reaction for the CPD + DMAD and dimerization CPD + CPD reactions were determined to be −37 ± 3 cm3 mol−1 and −31 ± 2 cm3 mol−1, respectively. For completeness, DFT calculations were performed and used to rationalize the vibration modes corresponding to the BTEM spectral estimates as well as to provide predictions of the molar volumes of the solutes. The present study demonstrates the usefulness of a combined spectroscopic, signal processing, bulk density measurement and DFT approach to the determination of partial molar volumes and volumes of reaction directly from the multi-component reactive systems.

Determining the pure component spectra of trace organometallic intermediates by combined application of in situ Raman spectroscopy and band-target entropy minimization analysis

Trace organometallic intermediates arising from complex organic syntheses are usually quite difficult to detect spectroscopically. In situ FTIR and in situ NMR are the only techniques that are used with any regularity for such studies. In this contribution, high-pressure in situ Raman spectroscopic measurements were performed for the rhodium catalyzed hydroformylation of 3,3-dimethylbut-1-ene using Rh4(CO)12 as catalyst precursor at 298K – a reaction extensively studied previously by more sensitive in situ FTIR. The Raman spectroscopic measurements were analyzed using the band-target entropy minimization (BTEM) algorithm. As expected, the pure component spectra of dissolved CO, 3,3-dimethylbut-1-ene, and 4,4-dimethylpentanal were easily recovered. In addition, the pure component spectra of the precursor Rh4(CO)12 and the intermediate RCORh(CO)4 (R=(CH3)3CCH2CH2) were successfully reconstructed – even though the mean concentrations of both species were on the order of 150ppm. The BTEM estimate of the Raman spectrum of RCORh(CO)4 is reported for the first time. This Raman spectrum is consistent with the DFT predicted spectrum. This study represents the first combined application of Raman spectroscopy and BTEM analysis to a homogeneously catalyzed metal-mediated reaction. The potential and limitations of this general approach are discussed.

Application of mid-infrared chemical imaging and multivariate chemometrics analyses to characterise a population of microalgae cells

A suite of multivariate chemometrics methods was applied to a mid-infrared imaging dataset of a eustigmatophyte, marine Nannochloropsis sp. microalgae strain. This includes the improved leader–follower cluster analysis (iLFCA) to interrogate spectra in an unsupervised fashion, a resonant Mie optical scatter correction algorithm (RMieS-EMSC) that improves data linearity, the band-target entropy minimization (BTEM) self-modeling curve resolution for recovering component spectra, and a multi-linear regression (MLR) for estimating relative concentrations and plotting chemical maps of component spectra. A novel Alpha-Stable probability calculation for microalgae cellular lipid-to-protein ratio Λi is introduced for estimating population characteristics.

Characterizing Diffusion and Transport in Microfluidics Channels: A Combined Raman Microscopy and Band-Target Entropy Minimization Study

With the increasing use of microfluidics, there is a need for a rather general experimental approach in order to monitor and characterize transport effects. Indeed, micro-fabrication methods have allowed the inclusion of numerous new structures and devices within microfluidics channels, and such alterations in flow patterns should impact solute transport characteristics. In the present contribution, Raman microscopy is combined with band-target entropy minimization analysis (BTEM) in order to rapidly assess and map concentration profiles in various regions of a microfluidics device. Two isotopomers, CHCl(3) and CDCl(3), are contacted under laminar conditions. Special consideration is given to the point of contact between the two liquids, transport in straight sections, transport in curved sections, and wall effects. Break-through curves confirmed that stagnation of fluid at the wall is not occurring, despite substantial wall roughness. Since the methods used in the present study are quite general, they should be useful in rapidly accessing transport effects when fluids (also in conjunction with colloids, suspensions, and solids) are contacted in the presence of both simple as well as complex geometries.

Electronic Excitation of [(μ4‐η2‐alkyne)Rh4(CO)8(μ‐CO)2]: An In Situ UV/Vis Spectroscopy, Spectral Reconstruction and DFT Study

Reactions of three alkynes, namely, 1-heptyne, 3-hexyne and 1-phenyl-1-butyne, with [Rh(4)(CO)(9)(μ-CO)(3)] are performed in anhydrous hexane under argon atmosphere with multiple perturbations of alkynes and [Rh(4)(CO)(9)(μ-CO)(3)]. The reactions are monitored by in situ UV/Vis spectroscopy, and the collected electronic spectra are further analyzed with the band-target entropy minimization (BTEM) family of algorithms to reconstruct the pure component spectra. Three BTEM estimates of [(μ(4)-η(2)-alkyne)Rh(4)(CO)(8)(μ-CO)(2)], in addition to that of [Rh(4)(CO)(9)(μ-CO)(3)], are successfully reconstructed from the experimental spectra. Time-dependent density functional theory (TD-DFT) predicted spectra at the PBE0/DGDZVP level are consistent with the corresponding BTEM estimates. The present study demonstrates that: 1) the BTEM family of algorithms is successful in analyzing multi-component UV/Vis spectra and results in good spectral estimates of the trace organometallics present; and 2) the subsequent DFT/TD-DFT methods provide an interpretation of the nature of the electronic excitation and can be used to predict the electronic spectra of similar transition organometallic complexes.

Detailed spectroscopic analysis of complex multi‐component materials using a combination of Raman mapping with BTEM

AbstractThe combined application of Raman microscopy and self‐modeling curve resolution techniques can address a wide range of material characterization problems. In particular, the combination of Raman microscopy and the Band‐Target Entropy Minimization (BTEM) algorithm has been applied to various organic, inorganic, pharmaceutical and bio‐material related problems. In the present contribution, the principles behind this type of analysis are reviewed, followed by a number of case‐by‐case studies. For each of these examples, a Raman microscopic mapping measurement (consisting of 100 s up to 1000 s of spectra) is performed, followed by BTEM analysis which provides the underlying pure component spectra of the constituents present in the system without the use of any a priori information. In most cases, outstanding signal‐to‐noise ratios for components at the 0.1‐1.0 % level can be obtained, and sometimes trace constituents can also be detected. Subsequently, the identity of the components can be determined by comparison to spectral libraries. Finally, the reconstructed pure component spectra can be further used to obtain the spatial distribution of the constituents present in the sample. Copyright © 2012 John Wiley & Sons, Ltd.

Kinetic study of a complex triangular reaction system in alkaline aqueous-ethanol medium using on-line transmission FTIR spectroscopy and BTEM analysis

The kinetics of the base-catalyzed reaction of methyl 4-hydroxybenzoate in aqueous-ethanol solvent medium was studied and analyzed via combined on-line transmission FTIR spectroscopy and Band-Target Entropy Minimization (BTEM) technique. This reaction is considered complex since it involves simultaneous hydrolysis and ethanolysis reactions of methyl 4-hydrozybenzoate (MP) to form ethyl 4-hydroxybenzoate (EP) as an intermediate and sodium 4-hydroxybenzoate as a final product. The pure component spectra of the reactive species involved in the reaction were reconstructed using BTEM technique. Their corresponding real concentrations were calculated and subsequently used for analyzing the kinetics of this triangular reaction system. The effects of temperature and solvent mixture compositions were studied. In general, the results show that the rates of both hydrolysis and ethanolysis reactions increase with temperature. Addition of ethanol to the solvent mixture also reduces the rates of the hydrolysis reactions. The effect of solvent mixture on the rate of ethanolysis reaction is more complex and influenced by at least two competing factors, namely the concentration of ethoxide ion in the solution and the stabilization effect on the reactant. The enthalpy and entropy activation parameters, ΔH‡ and ΔS‡, of both the hydrolysis and ethanolysis reactions were determined using the Eyring equation and the activation parameters confirm the associative nature in the elementary steps in these reactions. Finally, it is shown that the dominant synthetic pathway in this triangular system changes from direct hydrolysis of methyl 4-hydrozybenzoate to the indirect pathway via ethanolysis and then hydrolysis depending on the solvent mixture composition.

From Stoichiometric to Catalytic Binuclear Elimination in Rh–W Hydroformylations. Identification of Two New Heterobimetallic Intermediates

Cyclopentene hydroformylation, both stoichiometric and catalytic, is performed starting with Rh4(CO)12 and CpW(CO)3H as precursors. Synergism is observed. Isotopic labeling experiments confirm 30% of the product formation occurs via a catalytic binuclear elimination reaction. Two new intermediates are spectroscopically identified using band target entropy minimization and DFT, namely, RhW(CO)7Cp and CpW(CO)3H-C5H9CORh(CO)4. The latter intermediate appears to be hydrogen bonded via two simultaneous η5-C5H4–H···O═C interactions, one with the CO group on the acyl moiety and the other with a CO on rhodium.