Efficient mapping of lignin depolymerization product pools and quantification of specific monomers through rapid GPC-HPLC-UV/VIS analysis.

BACKGROUND: Growing research on lignin depolymerization to functionalized bio-aromatics has necessitated dedicated analysis techniques. However, immense variability in molecular weight and functional groups of the depolymerization products impedes fast analysis of a large number of samples while remaining in-depth enough for catalyst screening or reaction condition optimization. While GPC-HPLC-UV/VIS has been a promising technique, up until now, the information it provides is largely qualitative. By enabling quantification of key monomeric products and through further reduction of overall analysis time, this study aims to increase the potential of GPC-HPLC-UV/VIS for fast and in-depth characterization of lignin depolymerization product pools. RESULTS: Analysis of selected samples, isolated from GPC-HPLC-UV/VIS analyses of lignin depolymerization product pools, with gas chromatography (GC) equipped with an Orbitrap high-resolution accurate mass spectrometer (Orbitrap-HR/AM-MS) is successful in identifying the main low monomeric products. Moreover, these identifications are further substantiated through GPC-HPLC-UV/VIS analysis of standards. Furthermore, straight forward quantification of these products directly within GPC-HPLC-UV/VIS is successfully developed with limits of detection ≤0.05 mmol/L, which is at least on par with more complex analysis techniques. Additionally, several different reversed phase columns are assessed to reduce 2nd dimension (2D) time and, hence, overall analysis time while maintaining the possibility for quantification. A reduction in overall analysis time of about 30% as compared to the state-of-the-art is achieved by using a YMC Triart BIO C4 column as 2D. SIGNIFICANCE: Through the enhancements introduced in this study, GPC-HPLC-UV/VIS emerges as a unique technique for the analysis of lignin depolymerization product pools, which is capable of fast yet sufficiently in-depth analysis of a large volume of samples. This capability is indispensable for catalyst screening and fine-tuning reaction conditions.

Increasing the Robustness of SIFT-MS Volatilome Fingerprinting by Introducing Notional Analyte Concentrations.

Selected ion flow tube-mass spectrometry (SIFT-MS) is an analytical technique for volatile detection and quantification. SIFT-MS can be applied in a "white box" approach, measuring concentrations of target compounds, or as a "black box" fingerprinting technique, scanning all product ions during a full scan. Combining SIFT-MS full scan data acquired from multibatches or large-scale experiments remains problematic due to signal fluctuation over time. The standard approach of normalizing full scan data to the total signal intensity was insufficient. This study proposes a new approach to correct SIFT-MS fingerprinting data. In this concept, all of the product ions from a full scan are considered individual compounds for which notional concentrations can be calculated. Converting ion count rates into notional analyte concentrations accounts for any changes in the instrument parameters. The benefits of the proposed approach are demonstrated on three years of data from both multibatches and long-term experiments showing a significant reduction of system-induced fluctuations providing a better focus on the changes of interest.

Authentication of extra virgin Argan oil by selected-ion flow-tube mass-spectrometry fingerprinting and chemometrics.

Recognized for its nutritional and therapeutic use, extra-virgin Argan Oil (EVAO) is frequently adulterated. Selected-Ion Flow-Tube Mass Spectrometry (SIFT-MS) spectra were applied to quantify adulterants (i.e., Argan oil of lower quality (LQAO), olive oil (OO), and sunflower oil (SO)) in EVAO. Four data sets, i.e., using H3O+, NO+, O2+ reagent ions, and the combined data were considered. Soft independent modelling of class analogy (SIMCA), and partial least squares discriminant analysis (PLS-DA) were assessed to distinguish adulterated- from pure EVAO. The effectiveness of SIFT-MS associated with PLS and support vector machine (SVM) regression to quantify trace adulterants in EVAO was evaluated. Variable Importance in Projection (VIP), and interval-PLS (iPLS) were also investigated to extract useful features. Different models were built to predict the EVAO authenticity and the degree of adulteration. High accuracy was achieved. SIFT-MS spectra handled with the appropriate chemometric tools were found suitable for the quality evaluation of EVAO.

Fast screening of Depolymerized Lignin Samples Through 2D-Liquid Chromatography Mapping.

Lignin valorization and particularly its depolymerization into bio-aromatics, has emerged as an important research topic within green chemistry. However, screening of catalysts and reaction conditions within this field is strongly constrained by the lack of analytical techniques that allow for fast and detailed mapping of the product pools. This analytical gap results from the inherent product pool complexity and the focus of the state-of-the-art on monomers and dimers, overlooking the larger oligomers. In this work, this gap is bridged through the development of a quasi-orthogonal GPC-HPLC-UV/VIS method that is able to separate the bio-aromatics according to molecular weight (hydrodynamic volume) and polarity. The method is evaluated using model compounds and real lignin depolymerization samples. The resulting color plots provide a powerful graphical tool to rapidly assess differences in reaction selectivity towards monomers and dimers as well as to identify differences in the oligomers.

Feasibility study on exhaled-breath analysis by untargeted Selected-Ion Flow-Tube Mass Spectrometry in children with cystic fibrosis, asthma, and healthy controls: Comparison of data pretreatment and classification techniques.

Selected-Ion Flow-Tube Mass Spectrometry (SIFT-MS) has been applied in a clinical context as diagnostic tool for breath samples using target biomarkers. Exhaled breath sampling is non-invasive and therefore much more patient friendly compared to bronchoscopy, which is the golden standard for evaluating airway inflammation. In the actual pilot study, 55 exhaled breath samples of children with asthma, cystic-fibrosis and healthy individuals were included. Rather than focusing on the analysis of target biomarkers or on the identification of biomarkers, different data analysis strategies, including a variety of pretreatment, classification and discrimination techniques, are evaluated regarding their capacity to distinguish the three classes based on subtle differences in their full scan SIFT-MS spectra. Proper data-analysis strategies are required because these full scan spectra contain much external, i.e. unwanted, variation. Each SIFT-MS analysis generates three spectra resulting from ion-molecule reactions of analyte molecules with H3O+, NO+ and O2+. Models were built with Linear Discriminant Analysis, Quadratic Discriminant Analysis, Soft Independent Modelling by Class Analogy, Partial Least Squares - Discriminant Analysis, K-Nearest Neighbours, and Classification and Regression Trees. Perfect models, concerning overall sensitivity and specificity (100% for both) were found using Direct Orthogonal Signal Correction (DOSC) pretreatment. Given the uncertainty related to the classification models associated with DOSC pretreatments (i.e. good classification found also for random classes), other models are built applying other preprocessing approaches. A Partial Least Squares - Discriminant Analysis model with a combined pre-processing method considering single value imputation results in 100% sensitivity and specificity for calibration, but was less good predictive. Pareto scaling prior to Quadratic Discriminant Analysis resulted in 41/55 correctly classified samples for calibration and 34/55 for cross-validation. In future, the uncertainty with DOSC and the applicability of the promising preprocessing methods and models must be further studied applying a larger representative data set with a more extensive number of samples for each class. Nevertheless, this pilot study showed already some potential for the untargeted SIFT-MS application as a rapid pattern-recognition technique, useful in the diagnosis of clinical breath samples.

Noninvasive monitoring of fibre fermentation in healthy volunteers by analyzing breath volatile metabolites: lessons from the FiberTAG intervention study.

The fermentation of dietary fibre (DF) leads to the production of bioactive metabolites, the most volatile ones being excreted in the breath. The aim of this study was to analyze the profile of exhaled breath volatile metabolites (BVM) and gastrointestinal symptoms in healthy volunteers after a single ingestion of maltodextrin (placebo) versus chitin-glucan (CG), an insoluble DF previously shown to be fermented into short-chain fatty acids (SCFA) by the human microbiota in vitro. Maltodextrin (4.5 g at day 0) or CG (4.5 g at day 2) were added to a standardized breakfast in fasting healthy volunteers (n = 15). BVM were measured using selected ion flow tube mass spectrometry (SIFT-MS) throughout the day. A single ingestion of 4.5 g CG did not induce significant gastrointestinal discomfort. Untargeted metabolomics analysis of breath highlighted that 13 MS-fragments (among 408 obtained from ionizations of breath) discriminated CG versus maltodextrin acute intake in the posprandial state. The targeted analysis revealed that CG increased exhaled butyrate and 5 other BVM - including the microbial metabolites 2,3-butanedione and 3-hydroxybutanone - with a peak observed 6 h after CG intake. Correlation analyses with fecal microbiota (Illumina 16S rRNA sequencing) spotlighted Mitsuokella as a potential genus responsible for the presence of butyric acid, triethylamine and 3-hydroxybutanone in the breath. In conclusion, measuring BMV in the breath reveals the microbial signature of the fermentation of DF after a single ingestion. This protocol allows to analyze the time-course of released bioactive metabolites that could be proposed as new biomarkers of DF fermentation, potentially linked to their biological properties. Trial registration: Clinical Trials NCT03494491. Registered 11 April 2018 - Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT03494491.

Multimodal combination of GC × GC-HRTOFMS and SIFT-MS for asthma phenotyping using exhaled breath.

Chronic inflammatory lung diseases impact more than 300 million of people worldwide. Because they are not curable, these diseases have a high impact on both the quality of life of patients and the healthcare budget. The stability of patient condition relies mostly on constant treatment adaptation and lung function monitoring. However, due to the variety of inflammation phenotypes, almost one third of the patients receive an ineffective treatment. To improve phenotyping, we evaluated the complementarity of two techniques for exhaled breath analysis: full resolving comprehensive two-dimensional gas chromatography coupled to high-resolution time-of-flight mass spectrometry (GC × GC-HRTOFMS) and rapid screening selected ion flow tube MS (SIFT-MS). GC × GC-HRTOFMS has a high resolving power and offers a full overview of sample composition, providing deep insights on the ongoing biology. SIFT-MS is usually used for targeted analyses, allowing rapid classification of samples in defined groups. In this study, we used SIFT-MS in a possible untargeted full-scan mode, where it provides pattern-based classification capacity. We analyzed the exhaled breath of 50 asthmatic patients. Both techniques provided good classification accuracy (around 75%), similar to the efficiency of other clinical tools routinely used for asthma phenotyping. Moreover, our study provides useful information regarding the complementarity of the two techniques.

Analysis of mineral oil in food: results of a Belgian market survey.

Recently, migration of mineral oil components from food contact materials into various foods has been reported. The analysis of mineral oil in food is complicated since it consists of mineral oil saturated hydrocarbons (MOSH) comprising a complex mixture of linear, branched and cyclic compounds and variable amounts of mineral oil aromatic hydrocarbons (MOAH), mainly alkylated. Both MOSH and MOAH form 'humps' of unresolved peaks in the chromatograms with the same range of volatility. Since these two fractions have a different toxicological relevance, it is important to quantify them separately. Occurrence data on mineral oil are available only for a limited number of food groups and only from few countries. In Belgium, data on the contamination of food by mineral oil are lacking. In this contribution, an in-house validated online combination of liquid chromatography with gas chromatography (LC-GC) with flame ionisation detection (FID) was used for the quantification of MOSH and MOAH. Totally, 217 packed food samples were selected using a well-defined sampling strategy that targeted food categories which are highly consumed and categories suspected to contain mineral oil. For 19 samples, the method was not applicable. For the 198 remaining samples, MOSH was detected in 142 samples with concentrations up to 84.82 mg kg-1. For the MOAH fraction, there are 175 samples with a concentration below the limits of quantification (LOQ), while 23 samples had a higher concentration ranging from 0.6 to 2.24 mg kg-1. Finally, these results were compared with the action thresholds as proposed by the Scientific Committee (SciCom) of the Belgian Food Safety Agency (FAVV-AFSCA). Only one sample exceeded the threshold for MOSH, while the threshold for MOAH was exceeded in 23 samples. For the samples exceeding the action threshold, further investigation is needed to identify the contamination source.

Selected-ion flow-tube mass-spectrometry (SIFT-MS) fingerprinting versus chemical profiling for geographic traceability of Moroccan Argan oils.

This study investigated the effectiveness of SIFT-MS versus chemical profiling, both coupled to multivariate data analysis, to classify 95 Extra Virgin Argan Oils (EVAO), originating from five Moroccan Argan forest locations. The full scan option of SIFT-MS, is suitable to indicate the geographic origin of EVAO based on the fingerprints obtained using the three chemical ionization precursors (H3O+, NO+ and O2+). The chemical profiling (including acidity, peroxide value, spectrophotometric indices, fatty acids, tocopherols- and sterols composition) was also used for classification. Partial least squares discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), K-nearest neighbors (KNN), and support vector machines (SVM), were compared. The SIFT-MS data were therefore fed to variable-selection methods to find potential biomarkers for classification. The classification models based either on chemical profiling or SIFT-MS data were able to classify the samples with high accuracy. SIFT-MS was found to be advantageous for rapid geographic classification.

A metabolic fingerprinting approach based on selected ion flow tube mass spectrometry (SIFT-MS) and chemometrics: A reliable tool for Mediterranean origin-labeled olive oils authentication.

Selected Ion flow tube mass spectrometry (SIFT-MS) in combination with chemometrics was used to authenticate the geographical origin of Mediterranean virgin olive oils (VOOs) produced under geographical origin labels. In particular, 130 oil samples from six different Mediterranean regions (Kalamata (Greece); Toscana (Italy); Meknès and Tyout (Morocco); and Priego de Córdoba and Baena (Spain)) were considered. The headspace volatile fingerprints were measured by SIFT-MS in full scan with H3O+, NO+ and O2+ as precursor ions and the results were subjected to chemometric treatments. Principal Component Analysis (PCA) was used for preliminary multivariate data analysis and Partial Least Squares-Discriminant Analysis (PLS-DA) was applied to build different models (considering the three reagent ions) to classify samples according to the country of origin and regions (within the same country). The multi-class PLS-DA models showed very good performance in terms of fitting accuracy (98.90-100%) and prediction accuracy (96.70-100% accuracy for cross validation and 97.30-100% accuracy for external validation (test set)). Considering the two-class PLS-DA models, the one for the Spanish samples showed 100% sensitivity, specificity and accuracy in calibration, cross validation and external validation; the model for Moroccan oils also showed very satisfactory results (with perfect scores for almost every parameter in all the cases).

Rapid method for the simultaneous detection of boar taint compounds by means of solid phase microextraction coupled to gas chromatography/mass spectrometry.

Because of animal welfare issues, the voluntary ban on surgical castration of male piglets, starting January 2018 was announced in a European Treaty. One viable alternative is the fattening of entire male pigs. However, this can cause negative consumer reactions due to the occurrence of boar taint and possibly lead to severe economic losses in pig husbandry. In this study, headspace solid phase microextraction (HS-SPME) coupled to GC-MS was used in the development and optimization of a candidate method for fast and accurate detection of the boar taint compounds. Remarkably fast extraction (45s) of the boar taint compounds from adipose tissue was achieved by singeing the fat with a soldering iron while released volatiles were extracted in-situ using HS-SPME. The obtained method showed good performance characteristics after validation according to CD 2002/657/EC and ISO/IEC 17025 guidelines. Moreover, cross-validation with an in-house UHPLC-HR-Orbitrap-MS method showed good agreement between an in-laboratory method and the new candidate method for the fast extraction and detection of skatole and androstenone, which emphasizes the accuracy of this new SPME-GC-MS method. Threshold detection of the boar taint compounds on a portable GC-MS could not be achieved. However, despite the lack of sensitivity obtained on the latter instrument, a very fast method with run-to-run time of 3.5min for the detection of the boar taint compounds was developed.

Selected ion flow tube-mass spectrometry for online monitoring of submerged fermentations: a case study of sourdough fermentation.

Selected ion flow tube-mass spectrometry (SIFT-MS) has recently gained interest as an alternative method to traditional GC-MS for the detection of targeted volatile sample compounds, due to its ease of use, its speed and sensitivity, and its potential for real-time quantification. The feasibility of this technique was demonstrated using the case of the production of ethanol during sourdough fermentation. The potential of SIFT-MS as an online monitoring device for food fermentations was further demonstrated by the detection of acetoin in certain sourdough fermentations. This allowed discrimination between sourdough fermentation processes and illustrated the importance of real-time monitoring of food fermentations.

Rapeseed oil methyl ester pyrolysis: on-line product analysis using comprehensive two-dimensional gas chromatography.

Thermochemical conversion processes play a crucial role in all routes from fossil and renewable resources to base chemicals, fuels and energy. Hence, a fundamental understanding of these chemical processes can help to resolve the upcoming challenges of our society. A bench scale pyrolysis set-up has been used to study the thermochemical conversion of rapeseed oil methyl ester (RME), i.e. a mixture of fatty acid methyl esters. A GC×GC, equipped with both a flame ionization detector (FID) and a time-of-flight mass spectrometer (TOF-MS), allows quantitative and qualitative characterization of the reactor feed and product. Analysis of the latter is accomplished using a dedicated high temperature on-line sampling system. Temperature programmed analysis, starting at -40°C, permits effluent characterization from methane up to lignoceric acid methyl ester (C(25)H(50)O(2)), in a single run of the GC×GC. The latter combines a 100% dimethylpolysiloxane primary column with a 50% phenyl polysilphenylene-siloxane secondary column. Modulation is started when the oven temperature reaches 40°C, thus dividing the chromatogram in a conventional 1D and a comprehensive 2D part. The proposed quantification approach allows to combine the quantitative GC×GC analysis with 2 other on-line 1D GC analyses, resulting in a complete and detailed product composition including the measurement of CO, CO(2), formaldehyde and water. The GC×GC reveals that the product stream contains a huge variety of valuable products, such as linear alpha olefins, unsaturated esters and aromatics, that could not have been identified and quantified accurately with conventional 1D GC because of peak overlap.

On-line analysis of complex hydrocarbon mixtures using comprehensive two-dimensional gas chromatography.

This paper discusses the first setup for on-line qualitative and quantitative comprehensive two-dimensional gas chromatography (GC × GC) of complex hydrocarbon mixtures. A built-in 4-port 2-way valve allows switching between flame ionization detection (FID) and time-of-flight mass spectrometry (TOF-MS) between runs, without the need to cool down and vent the MS. Proper selection of GC carrier gas flow rates enables maximal agreement between the obtained chromatograms in both configurations. For on-line analysis of reactor effluents, a dedicated sampling system allows automatic sampling of the hot reactor effluent gases and immediate injection of the sample on the GC × GC. To determine a complete effluent composition in a single run of the GC × GC, a subzero oven starting temperature was employed. Modulation is started when the oven temperature reaches 40°C, thus dividing the chromatogram in a conventional 1D and a comprehensive 2D part. This work illustrates the mature and robust character of GC × GC, extending its capabilities from mere laboratory use to on-line routine analysis for industrial processes in the (petro-)chemical industry.

Evaluation of a dedicated gas chromatography-mass spectrometry method for the analysis of phenols in water.

The increasing need to routinely analyse phenolic hydrocarbons in aqueous samples was addressed by the development and implementation of a state-of-the-art, though relatively straightforward, analytical procedure. The proposed method is based on acetic anhydride derivatisation of the native phenols, liquid-liquid extraction of the corresponding phenyl acetate esters and subsequent analysis by GC-MS. The key feature and main strength of the method is located at the injection step which applies 'at-once' large volume injection with a programmable temperature vaporizer (PTV)-type injector. In the proposed method, the sensitivity gain inherent to the higher injection volume was used entirely to proportionally miniaturize, considerably accelerate and effectively simplify the otherwise tedious and time-consuming derivatisation/extraction step. Method performance, as expressed in terms of repeatability, reproducibility, linearity and accuracy, was found to be excellent. R.S.D. values, determined in the framework of an extensive reproducibility study, ranged between 1.47 and 9.02%. Detection limits were in the low ng/L range for all compounds with linear ranges extending up to two orders of magnitude. Method accuracy was determined by analyzing a certified reference material (PH- 1JM), spiked water samples and participating in a series of round robin tests and did not reveal any significant bias for the different compounds under investigation.