Automated pipeline for classifying Aroclors in soil by gas chromatography/mass spectrometry using modulo compressed two-way data objects

Signal Processing Methods to Interpret Polychlorinated Biphenyls in Airborne Samples

The main contribution of this interdisciplinary work is a robust computational framework to autonomously discover and quantify previously unknown associations between well-known (target) and potentially unknown (non-target) toxic industrial air pollutants. In this work, the variability of polychlorinated biphenyl (PCB) data is evaluated using a combination of statistical, signal processing, and graph-based informatics techniques to interpret the raw instrument signal from gas chromatography-mass spectrometry (GC/MS/MS) data sets. Specifically, minimum mean-squared techniques from the adaptive signal processing literature are extended to detect and separate coeluted (overlapped) peaks in the raw instrument signal. A graph-based visualization is provided which bridges two complementary approaches to quantitative pollution studies: (i) peak-cognizant target analysis (limits data analysis to few well-known compounds) and (ii) chemometric analysis (statistical large-scale data analysis) that is agnostic of specific compounds. Further, peak fitting techniques based on L2 error minimization are employed to autonomously calculate the amount of each PCB present with a normalized mean square error of -18.4851 dB. Graph-based visualization of associations between known and unknown compounds are developed through principal component analysis and both fuzzy c-means (FCM) and k-means clustering techniques are implemented and compared. The efficiency of these methods are compared using 150 air samples analyzed for individual PCBs with GC/MS/MS against traditional target-only techniques that perform analysis across only the known (target) PCBs. Parameter optimization techniques are employed to evaluate the relative contribution of PCB signals against ten potential source signals representing legacy signatures from historical manufacture of Aroclors and modern sources of PCBs produced as by products of pigment and polymer manufacturing. Aroclors 1232, 1254, 1016, and 1221 as well as non-Aroclor 3, 3', dichlorobiphenyl (PCB 11) were found in many of the samples as unique source signals that describe PCB mixtures in air samples collected from Chicago, IL.

A Computational Tool for Accelerated Analysis of Oligomeric Proanthocyanidins in Plants

A computational tool was developed to facilitate proanthocyanidin analysis using data collected by ultra-high-performance liquid chromatography-diode array detection-high resolution accurate mass-mass spectrometry (UHPLC-DAD-HRAM-MS). Both identification and semi-quantitation of proanthocyanidins can be achieved by the developed computational tool. It can extract proanthocyanidin chromatographic peaks, deconvolute the isotopic patterns of A-type, B-type, and multi-charged proanthocyanidins ions, and predict proanthocyanidin structures. Proanthocyanidins were quantified by an external calibration curve of catechin and molar relative response factors (MRRFs) of proanthocyanidins. Quantitation results including concentrations of total proanthocyanidins, individual proanthocyanidins, and proanthocyanidins with different degrees of polymerization and different types of linkage were calculated by the program and exported into an Excel spreadsheet automatically. The program was applied to the analysis of seven plant materials including apple, cranberry, dark chocolate, grape seed extract, jujube, litchi, and mangosteen. The identification results were compared with the results obtained by manual processing. The program can greatly save the time needed for the data analysis of proanthocyanidins.

Field Analysis of Polychlorinated Biphenyls (PCBs) in Soil Using Solid-Phase Microextraction (SPME) and a Portable Gas Chromatography-Mass Spectrometry System

An expedited field analysis method was developed for the determination of polychlorinated biphenyls (PCBs) in soil matrices using a portable gas chromatography-mass spectrometry (GC-MS) instrument. Soil samples of approximately 0.5 g were measured with a portable scale and PCBs were extracted by headspace solid-phase microextraction (SPME) with a 100 µm polydimethylsiloxane (PDMS) fiber. Two milliliters of 0.2 M potassium permanganate and 0.5 mL of 6 M sulfuric acid solution were added to the soil matrices to facilitate the extraction of PCBs. The extraction was performed for 30 min at 100 ℃ in a portable heating block that was powered by a portable generator. The portable GC-MS instrument took less than 6 min per analysis and ran off an internal battery and helium cylinder. Six commercial PCB mixtures, Aroclor 1016, 1221, 1232, 1242, 1248, 1254, and 1260, could be classified based on the GC chromatograms and mass spectra. The detection limit of this method for Aroclor 1260 in soil matrices is approximately 10 ppm, which is sufficient for guiding remediation efforts in contaminated sites. This method was applicable to the on-site analysis of PCBs with a total analysis time of 37 min per sample. However, the total analysis time could be improved to less than 7 min per sample by conducting the rate-limiting extraction step for different samples in parallel.

DIFFERENTIATION OF AURANTII FRUCTUS IMMATURUS AND FRUCTUS PONICIRI TRIFOLIATAE IMMATURUS BY FLOW-INJECTION WITH ULTRAVIOLET SPECTROSCOPIC DETECTION AND PROTON NUCLEAR MAGNETIC RESONANCE USING PARTIAL LEAST-SQUARES DISCRIMINANT ANALYSIS

Two simple fingerprinting methods, flow-injection coupled to ultraviolet spectroscopy and proton nuclear magnetic resonance, were used for discriminating between Aurantii fructus immaturus and Fructus poniciri trifoliatae immaturus. Both methods were combined with partial least-squares discriminant analysis. In the flow-injection method, four data representations were evaluated: total ultraviolet absorbance chromatograms, averaged ultraviolet spectra, absorbance at 193, 205, 225, and 283 nm, and absorbance at 225 and 283 nm. Prediction rates of 100% were achieved for all data representations by partial least-squares discriminant analysis using leave-one-sample-out cross-validation. The prediction rate for the proton nuclear magnetic resonance data by partial least-squares discriminant analysis with leave-one-sample-out cross-validation was also 100%. A new validation set of data was collected by flow-injection with ultraviolet spectroscopic detection two weeks later and predicted by partial least-squares discriminant analysis models constructed by the initial data representations with no parameter changes. The classification rates were 95% with the total ultraviolet absorbance chromatograms datasets and 100% with the other three datasets. Flow-injection with ultraviolet detection and proton nuclear magnetic resonance are simple, high throughput, and low-cost methods for discrimination studies.

FlavonQ: an automated data processing tool for profiling flavone and flavonol glycosides with ultra-high-performance liquid chromatography-diode array detection-high resolution accurate mass-mass spectrometry

Profiling flavonoids in natural products poses a great challenge due to the diversity of flavonoids, the lack of commercially available standards, and the complexity of plant matrixes. The increasingly popular use of ultra-high-performance liquid chromatography-diode array detection-high resolution accurate mass-mass spectrometry (UHPLC-HRAM-MS) for the analysis of flavonoids has provided more definitive information but also vastly increased amounts of data. Thus, mining of the UHPLC-HRAM-MS data is a very daunting, labor-intensive, and expertise-dependent process. An automated data processing tool, FlavonQ, was developed that can transfer field-acquired expertise into data analysis and facilitate flavonoid research. FlavonQ is an "expert system" designed for automated data analysis of flavone and flavonol glycosides, two important subclasses of flavonoids. FlavonQ is capable of data format conversion, peak detection, flavone and flavonol glycoside peak extraction, flavone and flavonol glycoside identification, and production of quantitative results. An expert system was applied to the determination of flavone and flavonol glycosides in nine different plants with an average execution time of less than 1 min. The results obtained by FlavonQ were in good agreement with those determined conventionally by a flavonoid expert.

Use of fuzzy chromatography mass spectrometric (FCMS) fingerprinting and chemometric analysis for differentiation of whole-grain and refined wheat (T. aestivum) flour

A fuzzy chromatography mass spectrometric (FCMS) fingerprinting method combined with chemometric analysis has been established for rapid discrimination of whole-grain flour (WF) from refined wheat flour (RF). Bran, germ, endosperm, and WF from three local cultivars or purchased from a grocery store were studied. The state of refinement (whole vs. refined) of wheat flour was differentiated successfully by use of principal-components analysis (PCA) and soft independent modeling of class analogy (SIMCA), despite potential confounding introduced by wheat class (red vs. white; hard vs. soft) or resources (different brands). Twelve discriminatory variables were putatively identified. Among these, dihexoside, trihexoside, apigenin glycosides, and citric acid had the highest peak intensity for germ. Variable line plots indicated phospholipids were more abundant in endosperm. Samples of RF and WF from three cultivars (Hard Red, Hard White, and Soft White) were physically mixed to furnish 20, 40, 60, and 80 % WF of each cultivar. SIMCA was able to discriminate between 100 %, 80 %, 60 %, 40 %, and 20 % WF and 100 % RF. Partial least-squares (PLS) regression was used for prediction of RF-to-WF ratios in the mixed samples. When PLS models were used the relative prediction errors for RF-to-WF ratios were less than 6 %. Graphical Abstract Workflow of targeting discriminatory compounds by use of FCMS and chemometric analysis.

Application of chemometrics to resolve overlapping mass spectral peak clusters between trichloroethylene and its deuterated internal standard

RATIONALE

Using one or two (2) H-atom-labeled analogs as internal standards (ISs) may cause a 'cross-contribution' problem (i.e., the overlap of ions from the IS and the analyte) especially for halogenated volatile organic compounds (VOCs). However, in this situation the overlapping peak clusters of the analyte and ISs can be resolved by multivariate chemometric methods such as classical least-squares (CLS) and inverse least-squares (ILS).

METHODS

Trichloroethylene (TCE) and its internal standard, deuterated TCE (TCE-d), as model compounds, were analyzed using portable gas chromatography/mass spectrometry. CLS and ILS were applied to resolve overlapping TCE and TCE-d mass spectral signals and evaluated for the determination of TCE. CLS and ILS models were constructed and used to predict concentration ratios of TCE to TCE-d. Calibration samples were prepared by adding TCE at different concentrations and TCE-d at 300 ng mL(-1) as an IS.

RESULTS

The calibration curve was linear over a range of 10-1000 ng mL(-1) with a coefficient of determination (R(2)) of 0.993. A validation data set collected 2 weeks later was used to further test the model robustness. Lower prediction errors and higher correlation coefficients were obtained from TCE/TCE-d ratios predicted by the CLS model.

CONCLUSIONS

This paper describes the first application of CLS to deconvolute overlapping peaks between an analyte and its corresponding isotopic internal standard for quantification. The proposed method enables simple isotopic analogs of analytes (one H or C atom is isotopically labeled) to be used as internal standards for analytes with isotopic distributions. It has wide application because of the environmental impact and prevalence of halogenated VOCs, especially when analytes have isotopic distributions that overlap with an internal standard or when sophisticated isotopic analogs of the analytes with three or more (2)H- or/and (13)C-atoms are prohibitively expensive or even impossible.

Rapid discrimination of slimming capsules based on illegal additives by electronic nose and flash gas chromatography

The discrimination of counterfeit and/or illegally manufactured medicines is an important task in the pharmaceutical industry for pharmaceutical safety. In this study, 22 slimming capsule samples with illegally added sibutramine and phenolphthalein were analyzed by electronic nose and flash gas chromatography. To reveal the difference among the different classes of samples, principal component analysis and linear discriminant analysis were employed to analyze the data acquired from electronic nose and flash gas chromatography, respectively. The samples without illegal additives can be discriminated from the ones with illegal additives by using electronic nose or flash gas chromatography data individually. To improve the performance of classification, a data fusion strategy was applied to integrate the data from electronic nose and flash gas chromatography data into a single model. The results show that the samples with phenolphthalein, sibutramine and both can be classified well by using fused data.

Simultaneous quantification of Aroclor mixtures in soil samples by gas chromatography/mass spectrometry with solid phase microextraction using partial least-squares regression

Multivariate partial least-squares (PLS) method was applied to the quantification of two complex polychlorinated biphenyls (PCBs) commercial mixtures, Aroclor 1254 and 1260, in a soil matrix. PCBs in soil samples were extracted by headspace solid phase microextraction (SPME) and determined by gas chromatography/mass spectrometry (GC/MS). Decachlorinated biphenyl (deca-CB) was used as internal standard. After the baseline correction was applied, four data representations including extracted ion chromatograms (EIC) for Aroclor 1254, EIC for Aroclor 1260, EIC for both Aroclors and two-way data sets were constructed for PLS-1 and PLS-2 calibrations and evaluated with respect to quantitative prediction accuracy. The PLS model was optimized with respect to the number of latent variables using cross validation of the calibration data set. The validation of the method was performed with certified soil samples and real field soil samples and the predicted concentrations for both Aroclors using EIC data sets agreed with the certified values. The linear range of the method was from 10μgkg(-1) to 1000μgkg(-1) for both Aroclor 1254 and 1260 in soil matrices and the detection limit was 4μgkg(-1) for Aroclor 1254 and 6μgkg(-1) for Aroclor 1260. This holistic approach for the determination of mixtures of complex samples has broad application to environmental forensics and modeling.

Comparison of three algorithms for the baseline correction of hyphenated data objects

Three novel two-way baseline correction algorithms, that is, orthogonal basis (OB), fuzzy optimal associative memory (FOAM), and polynomial fitting (PF), were evaluated with high performance liquid chromatography-mass spectrometry (HPLC-MS) and gas chromatography/mass spectrometry (GC/MS) data objects. Among these algorithms, both OB and FOAM are two-way baseline correction algorithms, which reconstruct the entire two-way backgrounds from blank data objects, while the PF algorithm is a pseudo-two-way method, which models each ion chromatogram baseline with a third-order polynomial. The performance of baseline correction methods was first evaluated with respect to the signal-to-noise ratios (SNRs) of 4 major peaks of the HPLC-MS total ion current (TIC) chromatograms of celery seed extracts. Then, the effect of baseline correction on pattern recognition was evaluated by using 42 two-way headspace (HS) solid phase microextraction (SPME) GC/MS data objects of 7 polychlorinated biphenyl (PCB) mixture standard solutions. Two types of classifiers, that is, a fuzzy rule-building expert system (FuRES) and partial least-squares-discriminant analysis (PLS-DA) were evaluated in parallel. Bootstrapped Latin partitions (BLPs) were used to give an unbiased and generalized evaluation of the classification accuracy. Results indicate that SNRs of major peaks of the TIC chromatogram representative of two-way HPLC-MS data objects are increased by baseline correction. In addition, higher prediction accuracies can be obtained by performing baseline correction on the entire GC/MS data set prior to pattern recognition. It is also found that proper data transformation is able to improve the performance of baseline correction. This report is the first of two-way baseline correction methods for hyphenated chromatography/mass spectrometry data objects. Both the orthogonal basis and FOAM baseline correction methods are novel in-house algorithms and proved to be generally effective for two-way baseline correction in the present study. Polynomial fitting is a conventional baseline correction method for one-way data objects and is applied to two-way data objects for the first time. It is applicable when blank data objects are unavailable.