Quantitative Analysis Of Complex Mixtures Research Articles

Compositional Analysis of Complex Mixtures using Automatic MicroED Data Collection.

Quantitative analysis of complex mixtures, including compounds having similar chemical properties, is demonstrated using an automatic and high throughput approach to microcrystal electron diffraction (MicroED). Compositional analysis of organic and inorganic compounds can be accurately executed without the need of diffraction standards. Additionally, with sufficient statistics, small amounts of compounds in mixtures can be reliably detected. These compounds can be distinguished by their crystal structure properties prior to structure solution. In addition, if the crystals are of good quality, the crystal structures can be generated on the fly, providing a complete analysis of the sample. MicroED is an effective method for analyzing the structural properties of sub-micron crystals, which are frequently found in small-molecule powders. By developing and using an automatic and high throughput approach to MicroED, and with the use of SerialEM for data collection, data from thousands of crystals allow sufficient statistics to detect even small amounts of compounds reliably.

Determination of vacuum ultraviolet detector response factors by hyphenation with two-dimensional comprehensive gas chromatography with flame ionization detection.

Two-dimensional comprehensive gas chromatography is an established technique, employed for the characterization of complex samples. Broadband vacuum ultraviolet absorption spectroscopy detection has recently attracted a lot of attention as it is a universal detection technique characterized by good selectivity but also ease of use and amenability to coupling with two-dimensional comprehensive gas chromatography. Vacuum ultraviolet spectroscopy is particularly interesting due to the possibility of performing spectral decomposition for species that coelute in gas chromatography analysis. This detector has quantitative capabilities, however not all species absorb vacuum ultraviolet radiation the same. Unfortunately, vacuum ultraviolet relative response factors for compounds are not always available. Methods to rapidly measure vacuum ultraviolet relative response factors and generate a large database that would allow calibration free quantitative analysis of complex mixtures are therefore of great interest. In this work, a universal methodology that permits rapid measurement of vacuum ultraviolet relative response factors is reported. It involves flow modulated two-dimensional comprehensive gas chromatography with dual vacuum ultraviolet and flame ionization detection. In this set-up, flame ionization detection is employed as a quantitative reference allowing to scale vacuum ultraviolet responses of investigated compounds. This approach was validated by flow measurements and by comparing relative response factors obtained for model compounds with literature data.

Quantitative analysis of bisphenol analogue mixtures by terahertz spectroscopy using machine learning method

Quantitative analysis of complex mixtures is a great challenge for spectral analysis. Bisphenol A (BpA) is a chemical predominantly used in manufacturing and is being replaced by other analogs due to its potential toxicity. Reliability methods is hence crucial for identification and quantification of bisphenol mixtures. In this study we present an attractive strategy for composition determination of BpA incorporated in its analogue mixtures. Terahertz spectra of four bisphenol components are analyzed using machine learning method (SVR) to learn the underlying model of the frequency against the target concentration of BpA in mixtures. The learned mode predicts the concentrations of the unknown samples with decision coefficient R2 = 0.98. Absorption spectra for bisphenols mixtures were successfully reconstructed by a hold-out validation scheme. The results indicate the terahertz spectroscopy in combination with SVR is robust and accurate in mixture quantitative analysis and should play a significant role for industrial applications in the future.

In-House Standard Method for Molecular Characterization of Dissolved Organic Matter by FT-ICR Mass Spectrometry.

Electrospray ionization (ESI) coupled with Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) has been widely used for molecular characterization of dissolved organic matter (DOM). However, ESI FT-ICR MS generally has poor repeatability and reproducibility because of its inherent ionization mechanism and structural characteristics, which severely hindered its application in quantitative analysis of complex mixtures. In this article, we developed an in-house standard method for molecular characterization of DOM by ESI FT-ICR MS. Instead of obtaining reproducible results by determining the instrument parameters, we adopted an approach of object control on the mass spectrum to solve the problem of poor reproducibility. The mass peak shape, resolution, and relative intensity distribution of a natural organic matter standard were adjusted by optimizing the operating conditions to obtain a repeatable result. The quality control sample was run 26 times by the different operators in a 6-month-long period to evaluate the reproducibility. Results showed that the relative standard deviation (%) of repeatability and reproducibility are 1.02 and 2.35 for average H/C, respectively. The in-house standard method has been validated and successfully used for the characterization of more than 4000 DOM samples, which is transferable to other laboratories.

A new scattering correction method of different spectroscopic analysis for assessing complex mixtures

Physical properties such as particle size distribution and compactness have significant confounding effects on the spectral signals of complex mixtures, which multivariate linear calibration methods such as partial least-squares (PLS) cannot effectively model or correct. Therefore, these effects significantly deteriorate calibration models’ predictive abilities for spectral quantitative analysis of complex mixtures. Here, new scattering correction methods were proposed to estimate the additive and multiplicative parameters considering light scattering effects in each spectrum and hence mitigate the detrimental influence of additive and multiplicative effects on quantitative spectroscopic analysis of complex mixtures. Three different correction methods were proposed to estimate the addition coefficient based on two different underlying assumptions, namely, whether this coefficient is related to the wavelength. After addition coefficient elimination, the multiplicative parameter can be eliminated by a simple but very efficient spectral ratio method. Furthermore, linear models are built with key variables, and the predictive performance of these models is verified using the root-mean-square error of prediction datasets. The proposed methods were tested on one apple data set and two publicly available benchmark datasets (i.e., near-infrared spectral data of meat and powder mixture samples) and compared with some existing correction methods. The results showed that (1) additive effects of different types of samples can be eliminated by different methods and (2) these methods can appreciable improve quantitative spectroscopic analysis of complex mixture samples. This study indicates that accurate quantitative spectroscopic analysis of complex mixtures can be achieved through the combination of additive effect elimination and the spectral ratio method.

Compact, Automated, Inexpensive, and Field-Deployable Vacuum-Outlet Gas Chromatograph for Trace-Concentration Gas-Phase Organic Compounds.

The identification and quantification of gas-phase organic compounds, such as volatile organic compounds (VOCs), frequently use gas chromatography (GC), which typically requires high-purity compressed gases. We have developed a new instrument for trace-concentration measurements of VOCs and intermediate-volatility compounds of up to 14 carbon atoms in a fully automated (computer-free), independent, low-cost, compact GC-based system for the quantitative analysis of complex mixtures without the need for compressed, high-purity gases or expensive detectors. Through adsorptive analyte preconcentration, vacuum GC, photoionization detectors, and need-based water-vapor control, we enable sensitive and selective measurements with picogram-level limits of detection (i.e., under 15 ppt in a 4 L sample for most compounds). We validate performance against a commercial pressurized GC, including resolving challenging isomers of similar volatility, such as ethylbenzene and m/ p-xylene. We employ vacuum GC across the whole column with filtered air as a carrier gas, producing long-term system stability and performance over a wide range of analytes. Through theory and experiments, we present variations in analyte diffusivities in the mobile phase, analyte elution temperatures, optimal linear velocities, and separation-plate heights with vacuum GC in air at different pressures, and we optimize our instrument to exploit these differences. At 2-6 psia, the molecular diffusion coefficients are 6.4-2.1 times larger and the elution temperatures are 39-92 °C lower than with pressurized GC with helium (at 30 psig) depending on the molecular structure, and we find a wide range of optimal linear velocities (up to 60 cms-1) that are faster with broader tolerances than with pressurized-N2 GC.

HPLC-MS/MS Methods for Diacylglycerol and Sphingolipid Molecular Species in Skeletal Muscle.

HPLC-MS/MS has enabled the quantitative analysis of complex mixtures of lipid molecular species. Several separate analyses, using methods that have been optimized for individual lipid classes, provide good lipidomic profiles, but may not be desirable for laboratories constrained by available instrumentation and wanting a higher throughput. Here we describe two methods using binary gradient HiLiC HPLC and triple quadrupole MS that together provide a lipidomic profile for lipids of interest in type 2 diabetes research. Methods for analysis of molecular species of diacylglycerol, ceramide, dihydroceramide, sphingosine, glucosyl- and lactosylceramide, sphingomyelin, and acylcarnitine from skeletal muscle and primary culture cells are described.

A Method for the Quantitative Analysis of a Key Component in Complex Mixtures Using Raman Spectroscopy Based on Peak Decomposition.

Raman spectroscopy has been increasingly used in industrial production processes for analytical purposes. However, the quantitative analysis of complex mixtures based on their Raman spectra remains a problem, especially when not all of the spectra of pure components in the mixture are available. In this work, a method for the quantitative analysis of a key component in complex mixtures based on peak decomposition is introduced. The proposed method only requires the spectrum of the main component in the mixture before analysis. The spectra of the mixture are decomposed to the contributions of the main component and a group of parametric Lorentzian functions. Then, the representative peaks of the key component are extracted. Finally, a calibration model is established. This method is applied to a complex mixture containing m-xylene, o-xylene, p-xylene, ethylbenzene, methylbenzene, three trimethylbenzenes, non-aromatic hydrocarbons, etc. to determine the concentration of o-xylene in the mixture. Experiments demonstrate that the proposed method performs better than the commonly-used partial least squares (PLS), especially when the number of training samples is very small.

Click chemistry enables quantitative chiroptical sensing of chiral compounds in protic media and complex mixtures

Click reactions have become powerful synthetic tools with unique applications in the health and materials sciences. Despite the progress with optical sensors that exploit the principles of dynamic covalent chemistry, metal coordination or supramolecular assemblies, quantitative analysis of complex mixtures remains challenging. Herein, we report the use of a readily available coumarin conjugate acceptor for chiroptical click chirality sensing of the absolute configuration, concentration and enantiomeric excess of several compound classes. This method has several attractive features, including wide scope, fast substrate fixation without by-product formation or complicate equilibria often encountered in reversible substrate binding, excellent solvent compatibility, and tolerance of air and water. The ruggedness and practicality of this approach are demonstrated by comprehensive analysis of nonracemic monoamine samples and crude asymmetric imine hydrogenation mixtures without work-up. Click chemosensing addresses increasingly important time efficiency, cost, labor and chemical sustainability aspects and streamlines asymmetric reaction development at the mg scale.

Complete carbon analysis of sulfur‐containing mixtures using postcolumn reaction and flame ionization detection

Quantitative analysis of complex mixtures containing hundreds‐to‐thousands of organic compounds rich in heteroatoms, including oxygen, sulfur, and nitrogen, is a major challenge in the fuel, food, and chemical industries. In this work, a two‐stage (oxidation and methanation) catalytic process in a 3‐D–printed metal microreactor was evaluated for its capability to convert sulfur‐containing organic compounds to methane. The microreactor was inserted into a gas chromatograph between the capillary column and flame ionization detector. Catalytic conversion of all sulfur‐containing analytes to methane enabled carbon quantification without calibration, by the method identified as “quantitative carbon detection” or QCD. Quantification of tetrahydrothiophene, dimethyl sulfoxide, diethyl sulfide, and thiophene indicated complete conversion to methane at 450°C. Long‐term performance of a commercial microreactor was evaluated for 2000 consecutive injections of sulfur‐containing organic analytes. The sulfur processing capacity of the microreactor was identified experimentally, after which reduced conversion to methane was observed. © 2017 American Institute of Chemical Engineers AIChE J, 63: 5438–5444, 2017

Diagonal designs for a multi-component calibration experiment

Modern spectroscopic and sensor technologies combined with multivariate modelling are increasingly used for the quantitative analysis of complex mixtures. Their performance depends directly on the data design chosen for model training and validation. A well-balanced calibration experiment with the fewest samples possible presents additional challenges when several mixture components (factors) need to be calibrated on the same dataset and subsequently quantified from the same multivariate measurement. This practically important problem stays poorly addressed by the theory of experimental design. This theoretical work systematically formulates the requirements to an optimal calibration/validation dataset and introduces a new family of calibration designs, where the samples are placed along the diagonals of an experimental space that is a hypercube. Such placement is appropriate due to reasonable assumptions about the linear nature of analytical response. Suggested filling schemes allow economical diagonal designs with intrinsic validation to be built for multiple factors presented in as many levels as the number of samples. The most important practical cases of two and three factors are considered in detail, and generalization to higher dimensions is outlined. Diagonal designs of any complexity can be generated using a simple geometrical scheme or with a supplied script.

Three-point multivariate calibration models by correlation constrained MCR-ALS: A feasibility study for quantitative analysis of complex mixtures

When it comes to address quantitative analysis in complex mixtures, Partial Least Squares (PLS) is often referred to as a standard first-order multivariate calibration method. The set of samples used to build the PLS regression model should ideally be large and representative to produce reliable predictions. In practice, however, the large number of calibration samples is not always affordable and the choice of these samples should be handled with care as it can significantly affect the accuracy of the predictive model. Correlation constrained multivariate curve resolution (CC-MCR) is an alternative regression method for first-order datasets where, unlike PLS, calibration and prediction stages are performed iteratively and optimized under constraints until the decomposition meets the convergence criterion. Both calibration and test samples are fitted into a unique bilinear model so that the number of calibration samples is no longer a critical issue. In this paper we demonstrate that under certain conditions CC-MCR models can provide for reasonable predictions in quantitative analysis of complex mixtures even when only three calibration samples are employed. The latter are defined as samples having the minimum, the maximum and the average concentration, providing for a simple and rapid strategy to build reliable calibration model. The feasibility of three-point multivariate calibration approach was assessed with several case studies featuring mixtures of different analytes in presence of interfering species. Satisfactory predictions with relative errors in the range 3–15% were achieved and good agreement with classical PLS models built from a larger set of calibration samples was observed.

Stepped MSAll Relied Transition (SMART): An approach to rapidly determine optimal multiple reaction monitoring mass spectrometry parameters for small molecules

Multiple reaction monitoring (MRM) is a universal approach for quantitative analysis because of its high specificity and sensitivity. Nevertheless, optimization of MRM parameters remains as a time and labor-intensive task particularly in multiplexed quantitative analysis of small molecules in complex mixtures. In this study, we have developed an approach named Stepped MSAll Relied Transition (SMART) to predict the optimal MRM parameters of small molecules. SMART requires firstly a rapid and high-throughput analysis of samples using a Stepped MSAll technique (sMSAll) on a Q-TOF, which consists of serial MSAll events acquired from low CE to gradually stepped-up CE values in a cycle. The optimal CE values can then be determined by comparing the extracted ion chromatograms for the ion pairs of interest among serial scans. The SMART-predicted parameters were found to agree well with the parameters optimized on a triple quadrupole from the same vendor using a mixture of standards. The parameters optimized on a triple quadrupole from a different vendor was also employed for comparison, and found to be linearly correlated with the SMART-predicted parameters, suggesting the potential applications of the SMART approach among different instrumental platforms. This approach was further validated by applying to simultaneous quantification of 31 herbal components in the plasma of rats treated with a herbal prescription. Because the sMSAll acquisition can be accomplished in a single run for multiple components independent of standards, the SMART approach are expected to find its wide application in the multiplexed quantitative analysis of complex mixtures.

Quantitative trace analysis of complex mixtures using SABRE hyperpolarization.

Signal amplification by reversible exchange (SABRE) is an emerging nuclear spin hyperpolarization technique that strongly enhances NMR signals of small molecules in solution. However, such signal enhancements have never been exploited for concentration determination, as the efficiency of SABRE can strongly vary between different substrates or even between nuclear spins in the same molecule. The first application of SABRE for the quantitative analysis of a complex mixture is now reported. Despite the inherent complexity of the system under investigation, which involves thousands of competing binding equilibria, analytes at concentrations in the low micromolar range could be quantified from single-scan SABRE spectra using a standard-addition approach.

Methods of the decomposition of spectra of various origin in the analysis of complex mixtures

Different algorithms of the decomposition of spectral curves are compared by the precision of identification and quantitative analysis of complex mixtures. The available conventional methods of self-modeling curve resolution (SIMPLISMA, MCR-ALS) and algorithms implementing the independent component analysis (MILCA, SNICA) are used. The results are illustrated by a series of examples of different spectral signals (UV, IR, Raman, fluorescence).

Normalization and Statistical Analysis of Quantitative Proteomics Data Generated by Metabolic Labeling

Comparative proteomics is a powerful analytical method for learning about the responses of biological systems to changes in growth parameters. To make confident inferences about biological responses, proteomics approaches must incorporate appropriate statistical measures of quantitative data. In the present work we applied microarray-based normalization and statistical analysis (significance testing) methods to analyze quantitative proteomics data generated from the metabolic labeling of a marine bacterium (Sphingopyxis alaskensis). Quantitative data were generated for 1,172 proteins, representing 1,736 high confidence protein identifications (54% genome coverage). To test approaches for normalization, cells were grown at a single temperature, metabolically labeled with (14)N or (15)N, and combined in different ratios to give an artificially skewed data set. Inspection of ratio versus average (MA) plots determined that a fixed value median normalization was most suitable for the data. To determine an appropriate statistical method for assessing differential abundance, a -fold change approach, Student's t test, unmoderated t test, and empirical Bayes moderated t test were applied to proteomics data from cells grown at two temperatures. Inverse metabolic labeling was used with multiple technical and biological replicates, and proteomics was performed on cells that were combined based on equal optical density of cultures (providing skewed data) or on cell extracts that were combined to give equal amounts of protein (no skew). To account for arbitrarily complex experiment-specific parameters, a linear modeling approach was used to analyze the data using the limma package in R/Bioconductor. A high quality list of statistically significant differentially abundant proteins was obtained by using lowess normalization (after inspection of MA plots) and applying the empirical Bayes moderated t test. The approach also effectively controlled for the number of false discoveries and corrected for the multiple testing problem using the Storey-Tibshirani false discovery rate (Storey, J. D., and Tibshirani, R. (2003) Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. U.S.A. 100, 9440-9445). The approach we have developed is generally applicable to quantitative proteomics analyses of diverse biological systems.

Evaluation of Ultrafast 2D NMR for Quantitative Analysis

Recent ultrafast methods make it possible to obtain two-dimensional (2D) nuclear magnetic resonance (NMR) spectra in a fraction of a second. This paper presents the first evaluation of ultrafast 2D NMR for quantitative analysis. On the basis of optimized conditions presented in recent studies, two homonuclear ultrafast techniques, J-resolved spectroscopy and TOCSY, are evaluated on model mixtures in terms of repeatability, long time stability, and linearity. The results are compared to conventional 1D (1)H NMR spectroscopy. Repeatabilities better than 1% for ultrafast J-resolved spectra and better than 7% for TOCSY spectra are obtained. The long-term stability is better than 4% for J-resolved spectroscopy and between 2% and 11% for TOCSY. Moreover, both methods are characterized by excellent linearities. This new analytical method opens important perspectives for fast, precise, and accurate quantitative analysis of complex mixtures and for the quantitative study of short time scale phenomena.

Composition of Eucalyptus camaldulensis Volatiles Using Direct Thermal Desorption Coupled with Comprehensive Two-Dimensional Gas Chromatography--Time-of-Flight-Mass Spectrometry

The direct qualification and quantitation of the volatile organic components of four Eucalyptus camaldulensis fruit samples, obtained from different geographical areas in Turkey, is studied using a direct thermal desorption (DTD) technique coupled with comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry. It is found that the E. camaldulensis sample from Adrasan gave a slightly higher oil yield (1.18%) than the others. The number of components quantitatively identified from Adrasan, Belek, Kuyucak, and Cesme were 46, 54, 55, and 59, respectively. The main compounds found in the volatile oils were: aromadendrene (6.45-15.02%), eucalyptol (0.17-12.61%), gamma-gurjunene (8.40-10.08%), terpinolen (1.98-8.39%), spathulenol (1.42-8.34%), alpha-pinene (0.85-6.81%), ledene (0.94-6.72%), and longifonene (0.07-6.22%). The composition of the volatiles desorbed from samples from all four different areas varied qualitatively and quantitatively. All identified compounds were quantitated using total ion chromatogram peak areas. DTD is a good method for qualitative and/or quantitative analysis of complex mixtures, and in particular for quantitative analysis of plant samples, which can yield data without the traditional obligation for costly and time consuming extraction techniques.

Determination of Teucrium chamaedrys volatiles by using direct thermal desorption–comprehensive two-dimensional gas chromatography–time-of-flight mass spectrometry

The direct qualification and quantification of the volatile components of Teucrium chamaedrys was studied using a direct thermal desorption (DTD) technique with comprehensive two-dimensional (2D) gas chromatography–time-of-flight mass spectrometry (GC × GC–TOF/MS). The GC × GC separation chromatographically resolved hundreds of components within this sample, and with the separation coupled with TOF/MS for detection, high probability identifications were made for 68 compounds. The quantitative results were determined through the use of internal standards and the desorption of differing amounts of raw material in the injector. The highest yield of volatile compounds (0.39%, w/w) was obtained at 150 °C thermal desorption temperature using 1.0 mg of dried sample placed in a glass injector liner when studied over the range 1.0–7.0 mg. Lowest yield of 0.33% (w/w) was found for the largest sample size of 7.0 mg. Relative standard deviation (RSD) for 10 replicates at each size sample were in the range 3.9–21.6%. The major compounds identified were β-pinene, germacrene D, α-pinene, α-farnesene, α-gurjunene, γ-elemene and γ-cadinene. All identified compounds were quantified using total ion chromatogram (TIC) peak areas. DTD is a promising method for quantitative analysis of complex mixtures, and in particular for quantitative analysis of plant samples, which can yield data without the traditional obligation for costly and time-consuming extraction techniques.

Characterization of gangliosides by two-dimensional high-performance thin-layer chromatography

A method for the qualitative and quantitative analysis of complex mixtures of gangliosides by computer-assisted image densitometry is reported. Gangliosides were applied to silica gel high-performance thin-layer plates and developed in two dimensions using one of several solvent systems. The plates were developed with a resorcinol reagent and the gangliosides were visualized as bluish-purple bands or spots. The plates were scanned with a Biolmage Visage 110 image analyzer and saved to disk. The densities of the spots were determined and the quantities of gangliosides determined by comparison with an internal standard, GM 4. Two-dimensional chromatography using chloroform-methanol-0.2% CaC1 2 (50:40:10) in the first dimension and n-propanol-ammonia(aq.) (6:3) in the second dimension gave linear and reproducible results when analyzed by image densitometry. Gangliosides isolated from human plasma were resolved with the above method. We suggest that the use of image densitometry improves the accuracy of quantitative analyses of complex mixtures of gangliosides by thin-layer chromatography.