Estimation of rotation ambiguity in multivariate curve resolution with charged particle swarm optimization (cPSO‐MCR)

Rotation ambiguity (RA) in multivariate curve resolution (MCR) is an undesirable case, when the physicochemical constraints are not sufficiently strong to provide a unique resolution of the data matrix of the mixtures into spectra and concentration profiles of individual chemical components. RA is often met in MCR of overlapped chromatographic peaks, kinetic and equilibrium data, and fluorescence two‐dimensional spectra. In case of RA, a single candidate solution has little practical value. So, the whole set of feasible solutions should be characterized somehow. It is a quite intricate task in a general case. In the present paper, a method was proposed to estimate RA with charged particle swarm optimization (cPSO), a population‐based algorithm. The criteria for updating the particles were modified, so that the swarm converged to the steady state, which spanned the set of feasible solutions. The performance of cPSO‐MCR was demonstrated on test functions, simulated datasets, and real‐world data. Good accordance of the cPSO‐MCR results with the analytical solutions (Borgen plots) was observed. cPSO‐MCR was also shown to be capable of estimating the strength of the constraints and of revealing RA in noisy data. As compared with analytical methods, cPSO‐MCR is simpler to implement, expands to more than three chemical compounds, is immune to noise, and can be easily adapted to virtually all types of constraints and objective functions (constraint based or residue based). cPSO‐MCR also provides natural visual information about the level of RA in spectra and concentration profiles, similar to the methods of two extreme solutions (e.g., MCR‐BANDS). Copyright © 2014 John Wiley & Sons, Ltd.     

Issues pertaining to the analysis of buprenorphine and its metabolites by gas chromatography–mass spectrometry

“Substitution therapy” and the use of buprenorphine (B) as an agent for treating heroin addiction continue to gain acceptance and have recently been implemented in Taiwan. Mature and widely utilized gas chromatography–mass spectrometry (GC–MS) technology can complement the low cost and highly sensitive immunoassay (IA) approach to facilitate the implementation of analytical tasks supporting compliance monitoring and pharmacokinetic/pharmacogenetic studies. Issues critical to GC–MS analysis of B and norbuprenorphine (NB) (free and as glucuronides), including extraction, hydrolysis, derivatization, and quantitation approaches were studied, followed by comparing the resulting data against those derived from IA and two types of liquid chromatography–tandem mass spectrometry (LC–MS/MS) methods. Commercial solid-phase extraction devices, highly effective for recovering all metabolites, may not be suitable for the analysis of free B and NB; acetyl-derivatization products exhibit the most favorable chromatographic, ion intensity, and cross-contribution characteristics for GC–MS analysis. Evaluation of IA, GC–MS, and LC–MS/MS data obtained in three laboratories has proven the 2-aliquot GC–MS protocol effective for the determination of free B and NB and their glucuronides.     

High-throughput approach for analysis of multicomponent gas chromatographic–mass spectrometric signals

Hyphenated techniques such as gas chromatography–mass spectrometry (GC–MS) or high-performance liquid chromatography–mass spectrometry (LC–MS) produce a large amount of data in a form of two-way data matrix. It has been a great challenge to furthest extract the useful information from the data. In this work, a chemometric approach based on a modification of adaptive immune algorithm (AIA) was proposed for a high-throughput analysis of the multicomponent overlapping GC–MS signals. With the proposed method, the chromatographic profile of each component in an overlapping signal can be extracted independently and sequentially along the retention time. In order to show the efficiency of the method, a stimulated GC–MS data of six components with background and an experimental GC–MS data of 40 pesticides were investigated. It was found that the multicomponent overlapping GC–MS signals could be fast and accurately resolved. Furthermore, the quantitative property of the extracted information was also investigated. The correlation coefficients (r) between the peak area and the added volumes of the sample are in the range 0.9658–0.9953.     

Multivariate curve resolution combined with gas chromatography to enhance analytical separation in complex samples: A review

This review describes the major advantages and pitfalls of iterative and non-iterative multivariate curve resolution (MCR) methods combined with gas chromatography (GC) data using literature published since 2000 and highlighting the most important combinations of GC coupled to mass spectrometry (GC–MS) and comprehensive two-dimensional gas chromatography with flame ionization detection (GC × GC-FID) and coupled to mass spectrometry (GC × GC–MS). In addition, a brief summary of some pre-processing strategies will be discussed to correct common issues in GC, such as retention time shifts and baseline/background contributions. Additionally, algorithms such as evolving factor analysis (EFA), heuristic evolving latent projection (HELP), subwindow factor analysis (SFA), multivariate curve resolution-alternating least squares (MCR-ALS), positive matrix factorization (PMF), iterative target transformation factor analysis (ITTFA) and orthogonal projection resolution (OPR) will be described in this paper. Even more, examples of applications to food chemistry, lipidomics and medicinal chemistry, as well as in essential oil research, will be shown. Lastly, a brief illustration of the MCR method hierarchy will also be presented.     

Liquid chromatography–atmospheric pressure photoionization tandem mass spectrometry for analysis of 36 halogenated flame retardants in fish

A comprehensive, sensitive and high-throughput liquid chromatography–atmospheric pressure photoionization tandem mass spectrometry (LC–APPI-MS/MS) method has been developed for analysis of 36 halogenated flame retardants (HFRs). Under the optimized LC conditions, all of the HFRs eluted from the LC column within 14 min, while maintaining good chromatographic separation for the isomers. Introduction of the pre-heated dopant to the APPI source decreased the background noise fivefold, which enhanced sensitivity. An empirical equation was proposed to describe the relation between the ion intensity and dopant flow. The excellent on-column instrument detection limits averaged 4.7 pg, which was similar to the sensitivity offered by gas chromatography–high-resolution mass spectrometry (GC–HRMS). This method was used to analyze a series of fish samples. Good agreement was found between the results for PBDEs from LC–APPI-MS/MS and GC–HRMS.     

Comprehensive analysis of chromatographic data by using PARAFAC2 and principal components analysis

The most straightforward method to analyze an obtained GC–MS dataset is to integrate those peaks that can be identified by their MS profile and to perform a Principal Component Analysis (PCA). This procedure has some important drawbacks, like baseline drifts being scarcely considered or the fact that integration boundaries are not always well defined (long tails, co-eluted peaks, etc.). To improve the methodology, and therefore, the chromatographic data analysis, this work proposes the modeling of the raw dataset by using PARAFAC2 algorithm in selected areas of the GC profile and using the obtained well-resolved chromatographic profiles to develop a further PCA model. With this working method, not only the problems arising from instrumental artifacts are overcome, but also the detection of new analytes is achieved as well as better understanding of the studied dataset is obtained. As a positive consequence of using the proposed working method human time and work are saved. To exemplify this methodology the aroma profile of 36 apples being ripened were studied. The benefits of the proposed methodology (PARAFAC2 + PCA) are shown in a practitioner perspective, being able to extrapolate the conclusions obtained here to other hyphenated chromatographic datasets.     

A fast method for the identification of Mycobacterium tuberculosis in sputum and cultures based on thermally assisted hydrolysis and methylation followed by gas chromatography–mass spectrometry

A fast gas chromatography–mass spectrometry (GC–MS) method with minimum sample preparation is described for early diagnosis of tuberculosis (TB). The automated procedure is based on the injection of sputum samples which are then methylated inside the GC injector using thermally assisted hydrolysis and methylation (THM). The THM–GC–MS procedure was optimized for the injection of sputum samples. For the identification of Mycobacterium tuberculosis the known marker tuberculostearic acid (TBSA) and other potential markers were evaluated. Hexacosanoic acid in combination with TBSA was found to be specific for the presence of M. tuberculosis. For validation of the method several sputum samples with different viscosities spiked with bacterial cultures were analyzed. Finally, 18 stored sputum samples collected in Vietnam from patients suspected to suffer from TB were re-analyzed in Amsterdam by microscopy after decontamination/concentration and using the new THM–GC–MS method. No false positives were found by THM–GC–MS and all patients who were diagnosed with TB were also found positive using our newly developed THM–GC–MS method. These results show that the new fast and sensitive THM–GC–MS method holds great potential for the diagnosis of TB.     

Analysis of quinocide in unprocessed primaquine diphosphate and primaquine diphosphate tablets using gas chromatography–mass spectrometry with supersonic molecular beams

Malaria is one of the most widespread and deadly diseases on the planet. Every year, about 500 million new cases are diagnosed, and the annual death toll is about 3 million. Primaquine has strong antiparasitic effects against gametocytes and can therefore prevent the spread of the parasite from treated patients to mosquitoes. It is also used in radical cures and prevents relapse. Consequently, primaquine is an often-used drug. In this study the separation of unprocessed primaquine from the contaminant quinocide based on gas chromatography–mass spectrometry with supersonic molecular beam (SMB) is presented and 7.5 mg primaquine diphosphate tablets were analyzed. We present a novel method for fast determination of quinocide which is an isomer of primaquine as the main contaminant in unprocessed primaquine and in its medical form as tablets by gas chromatography–mass spectrometry with SMB (also named supersonic GC–MS). Supersonic GC–MS provides enhanced molecular ion without any ion source related peak tailing plus extended range of compounds amenable for GC–MS analysis. In addition, major isomer mass spectral effects were revealed in the mass spectra of primaquine and quinocide which facilitated the unambiguous identification of quinocide in primaquine tablets. Fast GC–MS analysis is demonstrated with less then 2 min elution time of the drug and its main contaminants.     

A new approach to untargeted integration of high resolution liquid chromatography–mass spectrometry data

Because of its high sensitivity and specificity, hyphenated mass spectrometry has become the predominant method to detect and quantify metabolites present in bio-samples relevant for all sorts of life science studies being executed. In contrast to targeted methods that are dedicated to specific features, global profiling acquisition methods allow new unspecific metabolites to be analyzed. The challenge with these so-called untargeted methods is the proper and automated extraction and integration of features that could be of relevance. We propose a new algorithm that enables untargeted integration of samples that are measured with high resolution liquid chromatography–mass spectrometry (LC–MS). In contrast to other approaches limited user interaction is needed allowing also less experienced users to integrate their data. The large amount of single features that are found within a sample is combined to a smaller list of, compound-related, grouped feature-sets representative for that sample. These feature-sets allow for easier interpretation and identification and as important, easier matching over samples. We show that the automatic obtained integration results for a set of known target metabolites match those generated with vendor software but that at least 10 times more feature-sets are extracted as well. We demonstrate our approach using high resolution LC–MS data acquired for 128 samples on a lipidomics platform. The data was also processed in a targeted manner (with a combination of automatic and manual integration) using vendor software for a set of 174 targets. As our untargeted extraction procedure is run per sample and per mass trace the implementation of it is scalable. Because of the generic approach, we envision that this data extraction lipids method will be used in a targeted as well as untargeted analysis of many different kinds of TOF-MS data, even CE- and GC–MS data or MRM. The Matlab package is available for download on request and efforts are directed toward a user-friendly Windows executable.     

Analytical performance of a triple quadrupole mass spectrometer compared to a high resolution mass spectrometer for the analysis of polybrominated diphenyl ethers in fish

Polybrominated diphenyl ethers (PBDEs) are a class of flame retardants used globally in many consumer products and industrial applications. Traditionally, gas chromatography–high resolution mass spectrometry (GC–HR-MS) is the method of choice for analysis of PBDEs in environmental samples because it offers high sensitivity and selectivity, resulting in less interferences. However, the specificity offered by gas chromatography-triple quadrupole tandem mass spectrometry (GC–QQQ-MS/MS), operated in selected reaction monitoring mode, provides a more affordable alternative to GC–HR-MS for the analysis of PBDEs in complex environmental samples. In this study, an analytical method was developed for the analysis of 41 PBDE congeners in fish using GC–QQQ-MS/MS. Results from the analysis of three fish species [lake trout (Salvelinus namaycush), yellow perch (Perca flavescens), and round goby (Neogobius melanostomus)] using GC–QQQ-MS/MS were compared with those obtained by GC–HR-MS. These species were selected because they represent varying levels of lipid-rich matrix and contaminant loads. Instrumental limits of detection for the GC–QQQ-MS/MS ranged from 0.04 pg to 41 pg, whereas those for the GC–HR-MS ranged from 5 pg to 85 pg. The PBDE values obtained from these two methods were highly correlated, R² values >0.7, for all three fish species, supporting the suitability of GC–QQQ-MS/MS for analysis of PBDEs in fish with varying fat content.     

Quantification of endocrine disruptors and pesticides in water by gas chromatography–tandem mass spectrometry. Method validation using weighted linear regression schemes

A multi-residue methodology based on a solid phase extraction followed by gas chromatography–tandem mass spectrometry was developed for trace analysis of 32 compounds in water matrices, including estrogens and several pesticides from different chemical families, some of them with endocrine disrupting properties. Matrix standard calibration solutions were prepared by adding known amounts of the analytes to a residue-free sample to compensate matrix-induced chromatographic response enhancement observed for certain pesticides. Validation was done mainly according to the International Conference on Harmonisation recommendations, as well as some European and American validation guidelines with specifications for pesticides analysis and/or GC–MS methodology. As the assumption of homoscedasticity was not met for analytical data, weighted least squares linear regression procedure was applied as a simple and effective way to counteract the greater influence of the greater concentrations on the fitted regression line, improving accuracy at the lower end of the calibration curve. The method was considered validated for 31 compounds after consistent evaluation of the key analytical parameters: specificity, linearity, limit of detection and quantification, range, precision, accuracy, extraction efficiency, stability and robustness.     

Gas chromatography–mass spectrometry screening methods for select UV filters,synthetic musks,alkylphenols, an antimicrobial agent,and an insect repellent in fish

Two screening methods have been developed for simultaneous determination of ten extensively used personal care products (PCPs) and two alkylphenol surfactants in fish. The methods consisted of extraction, clean-up, derivatization and analysis by gas chromatography–mass spectrometry with selected ion monitoring (GC–SIM–MS) or gas chromatography–tandem mass spectrometry (GC–MS/MS) techniques. Among solvents tested to assess recovery of target compounds from 1-g tissue homogenates, acetone was selected as optimal for extracting compounds with dissimilar physicochemical properties from fish tissue. Initial experiments confirmed that GC–SIM–MS could be applied for analysis of lean fillet tissue (<1% lipid) without gel-permeation chromatography (GPC), and this approach was applied to assess the presence of target analytes in fish fillets collected from a regional effluent-dominated stream in Texas, USA. Benzophenone, galaxolide, tonalide, and triclosan were detected in 11 of 11 environmental samples at concentrations ranging from; 37 to 90, 234 to 970, 26 to 97, and 17 to 31 ng/g, respectively. However, performance of this analytical approach declined appreciably with increasing lipid content of analyzed tissues. Successful analysis of samples with increased lipid content was enabled by adding GPC to the sample preparation protocol and monitoring analytes with tandem mass spectrometry. Both analytical approaches were validated using fortified fillet tissue collected from locations expected to be minimally impacted by anthropogenic influences. Average analyte recoveries ranged from 87% to 114% with RSDs <11% and from 54% to 107% with RSDs <20% for fish tissue containing <1% and 4.9% lipid, respectively. Statistically derived method detection limits (MDLs) for GC–SIM–MS and GC–MS/MS methodologies ranged from 2.4 to 16 ng/g, and 5.1 to 397 ng/g, respectively.     

Liquid chromatography–tandem quadrupole mass spectrometry and comprehensive two-dimensional gas chromatography–time-of-flight mass spectrometry measurement of targeted metabolites of Methylobacterium extorquens AM1 grown on two different carbon sources

Complementary methods using liquid chromatography–tandem quadrupole mass spectrometry (LC–MS/MS) and comprehensive two-dimensional gas chromatography–time-of-flight mass spectrometry (GC × GC–TOF-MS) were developed and applied to determine targeted metabolites involved in central carbon metabolism [including tricarboxylic acid cycle, serine cycle, ethylmalonyl-coenzyme A (ethylmalonyl-CoA) pathway and poly-β-hydroxybutyrate cycle] of the bacterium Methylobacterium extorquens AM1 grown on two carbon sources, ethylamine (C2) and succinate (C4). Nucleotides, acyl-CoAs and a few volatile metabolites in cell extracts of M. extorquens AM1 were readily separated using either hydrophilic interaction liquid chromatography or reversed-phase liquid chromatography, and detected with good sensitivity by MS/MS. However, volatile intermediates within a low mass range (<300 m/z), especially at low abundance (such as glyoxylic acid and others <500 nM), were more effectively analyzed by GC × GC–TOF-MS which often provided better sensitivity, resolution and reproducibility. The complementary nature of the LC-based and GC-based methods allowed the comparison of 39 metabolite concentrations (the lowest level was at 139.3 nM). The overlap between the LC-based and GC-based methods of seven metabolites provided a basis to check for consistency between the two methods, and thus provided some validation of the quantification accuracy. The abundance change of 20 intermediates further suggested differences in pathways linked to C2 and C4 metabolism.     

Towards obtaining more information from gas chromatography–mass spectrometric data of essential oils: An overview of mean field independent component analysis

Mean field independent component analysis (MF-ICA) along with other chemometric techniques was proposed for obtaining more information from multi-component gas chromatographic–mass spectrometric (GC–MS) signals of essential oils (mandarin and lemon as examples). Using these techniques, some fundamental problems during the GC–MS analysis of essential oils such as varying baseline, presence of different types of noise and co-elution have been solved. The parameters affecting MF-ICA algorithm were screened using a 2⁵ factorial design. The optimum conditions for MF-ICA algorithm were followed by deconvolution of complex GC–MS peak clusters. The number of independent components (ICs) (chemical constituents) in each peak cluster was estimated using morphological score method. Eigenvalue profiles of evolving factor analysis (EFA) and pure variables from orthogonal projection approach (OPA) were used as initial mixing matrix (chromatograms) in iterative process. The resolved mass spectra were satisfactorily identified using NIST mass spectral search system. Finally, the results of optimized MF-ICA were compared with those obtained using multivariate curve resolution-alternating least square (MCR-ALS), multivariate curve resolution-objective function minimization (MCR-FMIN) and heuristic evolving latent projection (HELP) methods. It is demonstrated that MF-ICA can be used as an alternative method for a quick and accurate analysis of real multi-component problematic systems such as essential oils.     

Determination of amino acids in selenium-enriched yeast by gas chromatography–mass spectrometry after microwave assisted hydrolysis

A simple, rapid microwave digestion procedure for protein hydrolysis preceding the determination of amino acids in yeast using gas chromatography–mass spectrometry (GC–MS) is described. Protein hydrolysis was performed in a focused microwave using 4 M methanesulfonic acid (MAS). Amino acids were derivatized with methyl chlorofomate (MCF) and extracted into chloroform prior to GC–MS analysis. The microwave parameters, including power, temperature and heating time, were optimized. It was found that temperature and heating time were the most influential factors. A total of 17 amino acids were determined in selenium-enriched yeast with use of standard addition calibration. Limits of detection and quantitation (LODs/LOQs) of the amino acids measured were in the sub-nmol range, suitable for monitoring of amino acids in yeast and other food products.     

Development of a candidate certified reference material of cypermethrin in green tea

This paper presents the preparation of a candidate certified reference material (CRM) of cypermethrin in green tea, GLHK-11-01a according to the requirements of ISO Guide 34 and 35. Certification of the material was performed using a newly developed isotope dilution mass spectrometry (IDMS) approach, with gas chromatography high resolution mass spectrometry (GC–HRMS) and gas chromatography–tandem mass spectrometry (GC–MS/MS). Statistical analysis (one-way ANOVA) showed excellent agreement of the analytical data sets generated from the two mass spectrometric detections. The characterization methods have also been satisfactorily applied in an Asia-Pacific Metrology Program (APMP) interlaboratory comparison study. Both the GC–HRIDMS and GC–IDMS/MS methods proved to be sufficiently reliable and accurate for certification purpose. The certified value of cypermethrin in dry mass fraction was 148 μg kg⁻¹ and the associated expanded uncertainty was 14 μg kg⁻¹. The uncertainty budget was evaluated from sample in homogeneity, long-term and short-term stability and variability in the characterization procedure. GLHK-11-01a is primarily developed to support the local and wider testing community on need basis in quality assurance work and in seeking accreditation.     

Comments on a recently published paper ‘some surprising properties of multivariate curve resolution‐alternating least squares (MCR‐ALS) algorithms’

Some of the results given in a recently published paper in this journal concerning some surprising properties of the multivariate curve resolution‐alternating least squares (MCR‐ALS) method are discussed. My results showed that the surprising properties of MCR‐ALS refer only to the slow linear convergence properties of ALS algorithms and to rounding error computer calculations. Results obtained by MCR‐ALS for the first data example were correct and no significant differences were observed in the resolved profiles. In the second more complex data example, large rotation ambiguities were present for the spectrum profile of the very minor second component which was not correctly estimated by MCR‐ALS. However, even in this case, the subspaces spanned by the MCR‐ALS solutions were also very close to the correct ones apart from slow convergence properties of the MCR‐ALS algorithm in this case. Copyright © 2009 John Wiley & Sons, Ltd.     

Determination of benzenic and halogenated volatile organic compounds in animal-derived food products by one-dimensional and comprehensive two-dimensional gas chromatography–mass spectrometry

Animal-derived products are particularly vulnerable to contamination by volatile organic compounds (VOCs). These lipophilic substances, which are generated by an increasing number of sources, are easily transferred to the atmosphere, water, soil, and plants. They are ingested by livestock and become trapped in the fat fraction of edible animal tissues. The aim of this work was to determine the occurrence, risk for human health and entryways of benzenic and halogenated VOCs (BHVOCs) in meat products, milks and sea foods using gas chromatography– mass spectrometry (GC–MS) techniques. In the first part, the occurrence and levels of the BHVOCs in animal products were studied. One muscle and three fat tissues were analysed by GC–Quad/MS in 16 lambs. Of 52 BHVOCs identified, 46 were found in the three fat tissues and 29 in all four tissues, confirming that VOCs are widely disseminated in the body. Twenty-six BHVOCs were quantified in fat tissues, and risk for consumer health was assessed for six of these compounds regulated by the US Environmental Protection Agency (EPA). The BHVOC content was found to be consistent with previous reports and was below the maximum contaminant levels set by the EPA. In the second part, the performance of GC×GC–TOF/MS for comprehensively detecting BHVOCs and showing their entryways in animal-derived food chains was assessed. Meat, milk and oysters were analysed by GC–Quad/MS and GC×GC–TOF/MS. For all these products, at least a 7-fold increase in the contaminants detected was achieved with the GC×GC–TOF/MS technique. The results showed that the production surroundings, through animal feeding or geographical location, were key determinants of BHVOC composition in the animal products.     

One- and two-dimensional gas chromatography–mass spectrometry and high performance liquid chromatography–diode-array detector fingerprints of complex substances: A comparison of classification performance of similar,complex Rhizoma Curcumae samples with the aid of chemometrics

Many complex natural or synthetic products are analysed either by the GC–MS (gas chromatography–mass spectrometry) or HPLC–DAD (high performance liquid chromatography–diode-array detector) technique, each of which produces a one-dimensional fingerprint for a given sample. This may be used for classification of different batches of a product. GC–MS and HPLC–DAD analyses of complex, similar substances represented by the three common types of the TCM (traditional Chinese medicine), Rhizoma Curcumae were analysed in the form of one- and two-dimensional matrices firstly with the use of PCA (Principal component analysis), which showed a reasonable separation of the samples for each technique. However, the separation patterns were rather different for each analytical method, and PCA of the combined data matrix showed improved discrimination of the three types of object; close associations between the GC–MS and HPLC–DAD variables were observed. LDA (linear discriminant analysis), BP-ANN (back propagation-artificial neural networks) and LS-SVM (least squares-support vector machine) chemometrics methods were then applied to classify the training and prediction sets. For one-dimensional matrices, all training models indicated that several samples would be misclassified; the same was observed for each prediction set. However, by comparison, in the analysis of the combined matrix, all models gave 100% classification with the training set, and the LS-SVM calibration also produced a 100% result for prediction, with the BP-ANN calibration closely behind. This has important implications for comparing complex substances such as the TCMs because clearly the one-dimensional data matrices alone produce inferior results for training and prediction as compared to the combined data matrix models. Thus, product samples may be misclassified with the use of the one-dimensional data because of insufficient information.