Local resolution of two-way data from multicomponent equilibria

A self-modeling curve resolution (SMCR) method is proposed to calculate concentration and spectral profiles for the two-way spectral data from an equilibrium containing several chemical components. The proposed method has three main distinctive steps: (i) fixed size moving window evolving factor analysis (FSMWEFA) is used to identify the selective and zero concentration regions for a desired component, (ii) orthogonal projection resolution (OPR) is used to calculate its concentration profile and (iii) the component striping is done directly to resolve other components. The results of simulated and real polyprotic acid dissociation equilibria showed that the proposed combined method performs well even in situation when the successive stepwise equilibrium constants are close to each other. The applicability of method for resolving the triprotic acid system with rank deficiency due full spectral overlapping of two involved chemical species also is shown.     

A two-dimensional immune algorithm for resolution of overlapping two-way chromatograms

A two-dimensional immune algorithm is proposed for resolving the multicomponent overlapping two-way data matrices. The method is a development of the one-dimensional immune algorithm proposed elsewhere. When the inner product of vectors is expanded to the similar operation on matrices, the 1D immune algorithm can be expanded to the 2D algorithm which is suitable for the analysis of two-way data matrices. Both simulated and experimental two-way data sets were investigated by the method, and the results prove that the 2D immune algorithm is an effective tool for resolving the overlapping two-way signals. The effect of noise on the recoveries is also discussed.     

A two-dimensional immune algorithm for resolution of overlapping two-way chromatograms

A two-dimensional immune algorithm is proposed for resolving the multicomponent overlapping two-way data matrices. The method is a development of the one-dimensional immune algorithm proposed elsewhere. When the inner product of vectors is expanded to the similar operation on matrices, the 1D immune algorithm can be expanded to the 2D algorithm which is suitable for the analysis of two-way data matrices. Both simulated and experimental two-way data sets were investigated by the method, and the results prove that the 2D immune algorithm is an effective tool for resolving the overlapping two-way signals. The effect of noise on the recoveries is also discussed.     

Choice of procedures for preparative chromatography

Quantitative criteria, necessary and sufficient, were developed basing on the analysis of asymtotic solutions of border equations in elution sorption dynamics for realization of selectivity inversion for components in a chromatographic system due to the effect of kinetic selectivity. Analysis is suggested for experiments, where the new approach to realization of chromatographic processes using effect of kinetic selectivity allows optimization of preparative separation of biologically active substances. The approach suggested implies shortening of the experiment duration for separation processes, which can be crucial in most systems where components to be isolated are labile, or the process economics suffers considerably due to mobile phase, or energy consumption.     

Multivariate curve resolution in liquid chromatography—resolving two-way multi-component data using a Varimax extended rotation

The Varimax extended rotations (VER) have been proposed as a new method to mathematically resolve severely overlapped peaks in chromatographic experiments that produce bilinear data. VER employs a four-step procedure to determine the relative concentration and identity of the components that comprise a severely overlapped chromatographic peak. In the first step, the data are pre-processed to ensure that they are in a form suitable for multivariate curve resolution. The second step involves principal component analysis, which reduces the dimensionality of the data matrix while simultaneously retaining the information present in the data. In the third step, a new coordinate system is developed for the data using a Varimax rotation followed by a so-called extended rotation, which assists in identifying the so-called pure regions in the peak. Identifying these regions is crucial to rotating the concentration and spectral matrices towards a solution. The fourth step utilizes alternating least squares (ALS) to improve the estimates of the concentration and spectral profiles of each component. Results from real and simulated data are used to illustrate the efficacy and simplicity of the proposed method.     

A robust,general automatic phase correction algorithm for high‐resolution NMR data

The trends towards rapid NMR data acquisition, automated NMR spectrum analysis, and data processing and analysis by more naïve users combine to place a higher burden on data processing software to automatically process these data. Downstream data analysis is compromised by poor processing, and the automated processing algorithms must therefore be robust and accurate. We describe a new algorithm for automatic phase correction of frequency‐domain, high‐resolution NMR spectra. We show this to be reliable for data derived from a wide variety of typical NMR usages. We therefore conclude that the method will have wide‐spread applicability and a positive impact on automated spectral processing and analysis. Copyright © 2017 John Wiley & Sons, Ltd.     

No-alignment-strategies for exploring a set of two-way data tables obtained from capillary electrophoresis-mass spectrometry

Hyphenated techniques such as capillary electrophoresis-mass spectrometry (CE-MS) or high-performance liquid chromatography with diode array detection (HPLC-DAD), etc., are known to produce a huge amount of data since each sample is characterized by a two-way data table. In this paper different ways of obtaining sample-related information from a set of such tables are discussed. Working with original data requires alignment techniques due to time shifts caused by unavoidable variations in separation conditions. Other pre-processing techniques have been suggested to facilitate comparison among samples without prior peak alignment, for example, 'binning' and/or 'blurring' the data along the time dimension. All these techniques, however, require optimization of some parameters, and in this paper an alternative parameter-free method is proposed. The individual data tables (X) are represented as Gram matrices (XXT), where the summation is taken over the time dimension. Hence the possible variations in time scale are eliminated, while the time information is at least partly preserved by the correlation structure between the detection channels. For comparison among samples, a similarity matrix is constructed and explored by principal component analysis and hierarchical clustering. The Gram matrix approach was tested and compared to some other methods using 'binned' and 'blurred' data for a data set with CE-MS runs on urine samples. In addition to data exploration by principal component analysis and hierarchical clustering, a discriminant partial least squares model was constructed to discriminate between the samples that were taken with and without the prior intake of a drug. The result showed that the proposed method is at least as good as the others with respect to cluster identification and class prediction. A distinct advantage is that there is no need for parameter optimization, while a potential drawback is the large size of the Gram matrices for data with high mass resolution.     

Discovery of mass spectral characteristics and automatic identification of wax esters from gas chromatography mass spectrometry data

The mass spectral characteristics of wax esters were systemically summarized and interpreted through data mining of their standard mass spectra taken from NIST standard mass spectral library. Combining with the rules of retention indices described in the previous study, an automatic system was subsequently developed to identify the structural information for wax esters from GC/MS data. After tested and illustrated by both simulated and real GC/MS data, the results indicate that this system could identify wax esters except the polyunsaturated ones and the mass spectral characteristics are useful and effective information for identification of wax esters.     

用遗传算法优化的约束背景双线性化方法处理含未知背景组分干扰的色谱二维谱图体系

数值遗传算法是全局优化方法, 本文将其引入约束背景双线性化问题的优化求解过程, 以避免陷入局部最优。用本方法处理了模拟数据和两个实际含未知背景干扰的色谱二维谱图体系, 并探讨了如何提高遗传算法在优化平台区域的寻优速度,结果令人满意。     

Speciation of silicon and phosphorous using liquid chromatography coupled with sector field high resolution ICP-MS

Sector-field high resolution inductively coupled plasma mass spectrometry (HR-ICP-MS) has been used, at R=3000, to resolve spectral interferences caused by N₂⁺ and CO⁺ on ²⁸Si⁺, and NOH⁺ and NO⁺ on ³¹P⁺, thereby facilitating the speciation of these elements. Polydimethylsiloxanes (PDMS), ranging from 162 g mol⁻¹ to 16 500 g mol⁻¹, and their silanol breakdown products, have been separated by size exclusion and reverse phase chromatography, respectively, and detected using HR-ICP-MS. Detection limits (as Si) of between 12–30 ng ml⁻¹ and 0.1–4 ng ml⁻¹ were obtained for the PDMS and silanol compounds, respectively. Quantitative and reproducible methods have been developed for the analysis of four common organophosphorous pesticides in blood plasma, and inorganic phosphates in food, with detection limits of between 0.9–2 ng ml⁻¹ and 1–39 ng ml^−1, respectively.     

Multivariate curve resolution provides a high-throughput data processing pipeline for pyrolysis-gas chromatography/mass spectrometry

We present a data processing pipeline for Pyrolysis-Gas Chromatography/Mass Spectrometry (Py-GC/MS) data that is suitable for high-throughput analysis of lignocellulosic samples. The aproach applies multivariate curve resolution by alternate regression (MCR-AR) and automated peak assignment. MCR-AR employs parallel processing of multiple chromatograms, as opposed to sequential processing used in prevailing applications. Parallel processing provides a global peak list that is consistent for all chromatograms, and therefore does not require tedious manual curation. We evaluated this approach on wood samples from aspen and Norway spruce, and found that parallel processing results in an overall higher precision of peak area from integrated peaks. To further increase the speed of data processing we evaluated automated peak assignment solely based on basepeak mass. This approach gave estimates of the proportion of lignin (as syringyl-, guaiacyl and p-hydroxyphenyl-type lignin) and carbohydrate polymers in the wood samples that were in high agreement with those where peak assignments were based on full spectra. This method establishes Py-GC/MS as a sensitive, robust and versatile high-throughput screening platform well suited to a non-specialist operator.     

Hard modeling methods for the curve resolution of data from liquid chromatography with a diode array detector and on-flow liquid chromatography with nuclear magnetic resonance spectroscopy

Hard modeling methods have been performed on data from high-performance liquid chromatography with a diode array detector (LC-DAD) and on-flow liquid chromatography with 1H nuclear magnetic spectroscopy (LC-NMR). Four methods have been used to optimize parameters to model concentration profiles, three of which belong to classical optimization methods (the simplex method of Nelder-Mead, sequential quadratic programming approach, and Levenberg-Marquardt method), and the fourth is the application of genetic algorithms using real-value encoding. Only classical methods worked well for LC-DAD data, while all of the methods produced good results when LC-NMR data were divided into small spectral windows of peak clusters and parameters were optimized over each window.     

Rapid automatic identification and quantification of compounds in complex matrices using comprehensive two-dimensional gas chromatography coupled to high resolution time-of-flight mass spectrometry with a peak sentinel tool

Comprehensive two-dimensional gas chromatography coupled to mass spectrometry (GC × GC–MS) is a powerful tool for comprehensive analysis of organic pollutants. In this study, we developed a powerful analytical method using GC × GC for rapid and accurate identification and quantification of compounds in environmental samples with complex matrices. Specifically, we have developed an automatic peak sentinel tool, T-SEN, with free programming software, R. The tool, which consists of a simple algorithm for on peak finding and peak shape identification, allows rapid screening of target compounds, even for large data sets from GC × GC coupled to high resolution time of flight mass spectrometry (HRTOFMS). The software tool automatically assigns and quantifies compounds that are listed in user databases. T-SEN works on a typical 64 bit workstation, and the reference calculation speed is 10–20 min for approximately 170 compounds for peak finding (five ion count setting) and integration from 1–2 GB of sample data acquired by GC × GC–HRTOFMS. We analyzed and quantified 17 PCDD/F congeners and 24 PCB congeners in a crude lake sediment extract by both GC × GC coupled to quadrupole mass spectrometry (qMS) and GC × GC–HRTOFMS with T-SEN. While GC × GC–qMS with T-SEN resulted in false identification and inaccurate quantification, GC × GC–HRTOFMS with T-SEN provided correct identification and accurate quantification of compounds without sample pre-treatment. The differences between the values measured by GC × GC–HRTOFMS with T-SEN and the certified values for the certified reference material ranged from 7.3 to 36.9% for compounds with concentrations above the limit of quantification. False positives/negatives were not observed, except for when co-elution occurred. The technique of GC × GC–HRTOFMS in combination with T-SEN provides rapid and accurate screening and represents a powerful new approach for comprehensive analysis.     

Algorithms for automatic interpretation of high resolution mass spectra

Automated interpretation of high-resolution mass spectra in a reliable and efficient manner represents a highly challenging computational problem. This work aims at developing methods for reducing a high-resolution mass spectrum into its monoisotopic peak list, and automatically assigning observed masses to known fragment ion masses if the protein sequence is available. The methods are compiled into a suite of data reduction algorithms which is called MasSPIKE (Mass Spectrum Interpretation and Kernel Extraction). MasSPIKE includes modules for modeling noise across the spectrum, isotopic cluster identification, charge state determination, separation of overlapping isotopic distributions, picking isotopic peaks, aligning experimental and theoretical isotopic distributions for estimating a monoisotopic peak's location, generating the monoisotopic mass list, and assigning the observed monoisotopic masses to possible protein fragments. The method is tested against a complex top-down spectrum of bovine carbonic anhydrase. Results of each of the individual modules are compared with previously published work.     

Second-order multivariate calibration procedures applied to high-performance liquid chromatography coupled to fast-scanning fluorescence detection for the determination of fluoroquinolones

Different second-order multivariate calibration algorithms, namely parallel factor analysis (PARAFAC), N-dimensional partial least-squares (N-PLS) and multivariate curve resolution-alternating least-squares (MCR-ALS) have been compared for the analysis of four fluoroquinolones in aqueous solutions, including some human urine samples (additional four fluoroquinolones were simultaneously determined by univariate calibration). Data were measured in a short time with a chromatographic system operating in the isocratic mode. The detection system consisted of a fast-scanning spectrofluorimeter, which allows one to obtain second-order data matrices containing the fluorescence intensity as a function of retention time and emission wavelength. The developed approach enabled us to determine eight analytes, some of them with overlapped profiles, without the necessity of applying an elution gradient, and thus significantly reducing both the experimental time and complexity. The study was employed for the discussion of the scopes of the applied second-order chemometric tools. The quality of the proposed technique coupled to each of the evaluated algorithms was assessed on the basis of the figures of merit for the determination of fluoroquinolones in the analyzed water and urine samples. Univariate calibration of four analytes led to limits of detection in the range 20–40 ng mL⁻¹ and root mean square errors for the validation samples in the range 30–60 ng mL⁻¹ (corresponding to relative prediction errors of 3–8%). The ranges for second-order multivariate calibration (using PARAFAC and N-PLS) of the remaining four analytes were: limit of detection, 2–8 ng mL⁻¹, root mean square errors, 3–50 ng mL⁻¹ and relative prediction errors, 1–5%.     

Dissimilarity analysis and automatic identification of monomethylalkanes from gas chromatography mass spectrometry data 1. Principle and protocols

Monomethylalkanes are common but important components in many naturally occurring and synthetic organic materials. Generally, this kind of compounds is routinely analyzed by gas chromatography mass spectrometry (GC–MS) and identified by the retention pattern or similarity matching to the reference mass spectral library. However, these identification approaches rely on the limited standard database or costly standard compounds. When unknown monomethylalkane is absent from the reference library, these approaches might be less useful. In this study, based on the fragmentation rules and empirical observation, many interesting mass spectral characteristics of monomethylalkanes were discovered and employed to infer the number of carbon atoms and methylated position. Combined with the retention pattern, a protocol was described for the identification of monomethylalkane analyzed by GC–MS. After tested by simulated data and GC–MS data of the gasoline sample, it was demonstrated that the developing approach could automatically and correctly identify monomethylalkanes in complicated GC–MS data.     

An automatic system for multidimensional integrated protein chromatography

An automatic system for multidimensional integrated protein chromatography was designed for simultaneous separation of multiple proteins from complex mixtures, such as human plasma and tissue lysates. This computer-controlled system integrates several chromatographic columns that work independently or cooperatively with one another to achieve efficient high throughputs. The pipelines can be automatically switched either to another column or to a collection container for each UV-detected elution fraction. Environmental contamination is avoided due to the closed fluid paths and elimination of manual column change. This novel system was successfully used for simultaneous preparation of five proteins from the precipitate of human plasma fraction IV (fraction IV). The system involved gel filtration, ion exchange, hydrophobic interaction, and heparin affinity chromatography. Human serum albumin (HSA), transferrin (Tf), antithrombin-III (AT-III), alpha 1-antitrypsin (α1-AT), and haptoglobin (Hp) were purified within 3 h. The following recovery and purity were achieved: 95% (RSD, 2.8%) and 95% for HSA, 80% (RSD, 2.0%) and 99% for Tf, 70% (RSD, 2.1%) and 99% for AT-III, 65% (RSD, 2.0%) and 94% for α1-AT, and 50% (RSD, 1.0%) and 90% for Hp. The results demonstrate that this novel multidimensional integrated chromatography system is capable of simultaneously separating multiple protein products from the same raw material with high yield and purity and it has the potential for a wide range of multi-step chromatography separation processes.     

Multipurpose cold injector for high resolution gas chromatography

The multipurpose cold injector described in this paper represents a solution for an universal sampling system for high resolution gas chromatography. The system is modular and is built around the Carlo Erba cold on-column injector provided with secondary cooling. An auxiliary module, easily fixable on the bottom of the on-column injector, creates a temperature programmable vaporizing chamber making the system also suitable for cold split-splitless injections or solvent venting prior to the sample transfer into the capillary. The system can be operated manually or in a fully automatic mode using the auto-sampler for cold on-column injections. The experimental data illustrate its benefits and limitations.     

Combining targeted and nontargeted data analysis for liquid chromatography/high‐resolution mass spectrometric analyses

Increasing importation of food and the diversity of potential contaminants have necessitated more analytical testing of these foods. Historically, mass spectrometric methods for testing foods were confined to monitoring selected ions (SIM or MRM), achieving sensitivity by focusing on targeted ion signals. A limiting factor in this approach is that any contaminants not included on the target list are not typically identified and retrospective data mining is limited. A potential solution is to utilize high‐resolution MS to acquire accurate mass full‐scan data. Based on the instrumental resolution, these data can be correlated to the actual mass of a contaminant, which would allow for identification of both target compounds and compounds that are not on a target list (nontargets). The focus of this research was to develop software algorithms to provide rapid and accurate data processing of LC/MS data to identify both targeted and nontargeted analytes. Software from a commercial vendor was developed to process LC/MS data and the results were compared to an alternate, vendor‐supplied solution. The commercial software performed well and demonstrated the potential for a fully automated processing solution.