Chromatographic fingerprinting by comprehensive two-dimensional chromatography: Fundamentals and tools

Effectiveness of modified atmosphere and vacuum packaging in preserving the volatilome of Stelvio PDO cheese over time

We aimed to assay the effectiveness of vacuum or modified atmosphere packaging in preserving the organoleptic characteristics of already ripened slices of Stelvio Protected Designation of Origin cheese during 3 months of storage. A multi-omics panel, including metagenomic and metabolomic analyses, was implemented together with physicochemical and sensory analyses. Among the 177 volatiles identified, 30 out of the 50 potent odorants were found to be prevalent, regardless of packaging. Isovaleric acid showed the highest relative intensity in all samples. Caproic and caprylic acids always increased during storage, while metabolites such as dodecane and 2,3-butanediol always decreased. Slow proteolysis occurred during storage, but did not differentiate cheese samples. The type of packaging differentiated the microbiota and volatile profile, with modified atmosphere packaging keeping the volatilome more stable. Out of the 50 potent odorants, 9 were relevant to sample discrimination, with 8-nonen-2-one, 2-nonanone, and caproic acid being more abundant in stored samples.

Analysis of GC × GC fingerprints from medicinal materials using a novel contour detection algorithm: A case of Curcuma wenyujin

This study introduces an innovative contour detection algorithm, PeakCET, designed for rapid and efficient analysis of natural product image fingerprints using comprehensive two-dimensional gas chromatogram (GC × GC). This method innovatively combines contour edge tracking with affinity propagation (AP) clustering for peak detection in GC × GC fingerprints, the first in this field. Contour edge tracking significantly reduces false positives caused by "burr" signals, while AP clustering enhances detection accuracy in the face of false negatives. The efficacy of this approach is demonstrated using three medicinal products derived from Curcuma wenyujin. PeakCET not only performs contour detection but also employs inter-group peak matching and peak-volume percentage calculations to assess the compositional similarities and differences among various samples. Furthermore, this algorithm compares the GC × GC fingerprints of Radix/Rhizoma Curcumae Wenyujin with those of products from different botanical origins. The findings reveal that genetic and geographical factors influence the accumulation of secondary metabolites in various plant tissues. Each sample exhibits unique characteristic components alongside common ones, and variations in content may influence their therapeutic effectiveness. This research establishes a foundational data-set for the quality assessment of Curcuma products and paves the way for the application of computer vision techniques in two-dimensional (2D) fingerprint analysis of GC × GC data.

Solid-phase microextraction coupled to comprehensive multidimensional gas chromatography for food analysis

Solid-phase microextraction and comprehensive multidimensional gas chromatography represent two milestone innovations that occurred in the field of separation science in the 1990s. They have a common root in their introduction and have found a perfect coupling in their evolution and applications. This review will focus on food analysis, where the paradigm has changed significantly over time, moving from a targeted analysis, focusing on a limited number of analytes at the time, to a more holistic approach for assessing quality in a larger sense. Indeed, not only some major markers or contaminants are considered, but a large variety of compounds and their possible interaction, giving rise to the field of foodomics. In order to obtain such detailed information and to answer more sophisticated questions related to food quality and authenticity, the use of SPME-GC × GC-MS has become essential for the comprehensive analysis of volatile and semi-volatile analytes. This article provides a critical review of the various applications of SPME-GC × GC in food analysis, emphasizing the crucial role this coupling plays in this field. Additionally, this review dwells on the importance of appropriate data treatment to fully harness the results obtained to draw accurate and meaningful conclusions.

Gain deeper insights into traditional Chinese medicines using multidimensional chromatography combined with chemometric approaches

Traditional Chinese medicines (TCMs) possess a rich historical background, unique theoretical framework, remarkable therapeutic efficacy, and abundant resources. However, the modernization and internationalization of TCMs have faced significant obstacles due to their diverse ingredients and unknown mechanisms. To gain deeper insights into the phytochemicals and ensure the quality control of TCMs, there is an urgent need to enhance analytical techniques. Currently, two-dimensional (2D) chromatography, which incorporates two independent separation mechanisms, demonstrates superior separation capabilities compared to the traditional one-dimensional (1D) separation system when analyzing TCMs samples. Over the past decade, new techniques have been continuously developed to gain actionable insights from complex samples. This review presents the recent advancements in the application of multidimensional chromatography for the quality evaluation of TCMs, encompassing 2D-gas chromatography (GC), 2D-liquid chromatography (LC), as well as emerging three-dimensional (3D)-GC, 3D-LC, and their associated data-processing approaches. These studies highlight the promising potential of multidimensional chromatographic separation for future phytochemical analysis. Nevertheless, the increased separation capability has resulted in higher-order data sets and greater demands for data-processing tools. Considering that multidimensional chromatography is still a relatively nascent research field, further hardware enhancements and the implementation of chemometric methods are necessary to foster its robust development.

Chemical Fingerprint Analysis and Content Determination of Horned Gallnut and Bellied Gallnut in Galla Chinensis

To establish an objective and comprehensive methodology to analyse the connections and differences between horned gallnut (HG) and bellied gallnut (BG) in Galla Chinensis (GC). The HPLC fingerprints from 15 batches of HG and 15 batches of BG were performed, and chemometric analysis including similarity analysis (SA), hierarchical clustering analysis (HCA), principal component analysis (PCA), and orthogonal partial least squares discrimination analysis (OPLS-DA) was also set up. The results showed that the similarity of all batch samples was more than 0.9. In fingerprint analysis, 8 distinct common peaks were detected, among which gallic acid (GA), 1,3,6-tri-O-galloyl-β-D-glucose (TGG), and 1,2,3,4,6-O-galloyl-D-glucose (PGG) were identified by comparing with the standard compounds. Meanwhile, samples were clearly grouped into two classifications corresponding to HG and BG. This study demonstrated that HPLC fingerprints coupled with chemometric analysis could be applied to discriminate HG and BG and evaluate the qualities of HG and BG rapidly, which provided a certain experimental basis for the selection of GC raw materials and subsequent use.

Investigation on molecular characteristics of organic compounds during a full-scale landfill leachate treatment process based on non-targeted analysis

In this study, a new methodology for evaluating full-scale landfill leachate treatment processes by non-targeted analysis using comprehensive two-dimensional gas chromatography quadrupole time-of-flight mass spectrometry (GC × GC-QTOF-MS) was proposed. The method revealed the chemical complexity of organic compounds in landfill leachate samples at the molecular level and evaluated the removal efficiency of the anaerobic-anoxic-oxic (A2O) - membrane bioreactor (MBR) - nanofiltration (NF) treatment process in conjunction with multi-level classification of organic compounds. Results showed that the results of non-targeted analysis combined with multi-level classification of organic compounds had a significant correlation with the conventional water quality parameters and can be used to evaluate the treatment process. A total of 2508 organic compounds were detected in 6 samples. 17 emerging contaminants (ECs) with known potentially hazards were detected, including Diisobutyl Phthalate (DIBP), which is toxic to male reproduction and development, and 4-Tert-Butylphenol, which causes endocrine disruption in animals. The removal rate of organic compounds by this full-scale landfill leachate treatment processes reached 79.14%. The anaerobic tank played a crucial role with 64.98% contribution. For compounds, the removal rate of heterocyclics was as high as 94.67%, and the removal rate of aliphatics was poor, only 63.49%. This treatment process had almost perfect removal effect on the steroids in alicyclics and phenols in aromatics, but poor treatment effect on saturated alkanes in aliphatics and naphthenes in alicyclics. This study provides a methodology for accurate assessment of the molecular level of treatment processes, new insights for process optimization in waste treatment plants, and data support for the detection of emerging contaminants. The environmental hazards of landfill leachate can be further evaluated in the future in conjunction with ecotoxicity assessment studies.

Theoretical modeling and machine learning-based data processing workflows in comprehensive two-dimensional gas chromatography-A review

In recent years, comprehensive two-dimensional gas chromatography (GC × GC) has been gradually gaining prominence as a preferred method for the analysis of complex samples due to its higher peak capacity and resolution power compared to conventional gas chromatography (GC). Nonetheless, to fully benefit from the capabilities of GC × GC, a holistic approach to method development and data processing is essential for a successful and informative analysis. Method development enables the fine-tuning of the chromatographic separation, resulting in high-quality data. While generating such data is pivotal, it does not necessarily guarantee that meaningful information will be extracted from it. To this end, the first part of this manuscript reviews the importance of theoretical modeling in achieving good optimization of the separation conditions, ultimately improving the quality of the chromatographic separation. Multiple theoretical modeling approaches are discussed, with a special focus on thermodynamic-based modeling. The second part of this review highlights the importance of establishing robust data processing workflows, with a special emphasis on the use of advanced data processing tools such as, Machine Learning (ML) algorithms. Three widely used ML algorithms are discussed: Random Forest (RF), Support Vector Machine (SVM), and Partial Least Square-Discriminate Analysis (PLS-DA), highlighting their role in discovery-based analysis.

Lactiplantibacillus plantarum, lactiplantibacillus pentosus and inulin meal inclusion boost the metagenomic function of broiler chickens

BACKGROUND

The inclusion of alternative ingredients in poultry feed is foreseen to impact poultry gut microbiota. New feeding strategies (probiotics/prebiotics) must be adopted to allow sustainable productions. Therefore, the current study aimed to use metagenomics approaches to determine how dietary inclusion of prebiotic (inulin) plus a multi-strain probiotic mixture of Lactiplantibacillus plantarum and Lactiplantibacillus pentosus affected microbiota composition and functions of the gastro-intestinal tract of the broilers during production. Fecal samples were collected at the beginning of the trial and after 5, 11 and 32 days for metataxonomic analysis. At the end of the trial, broilers were submitted to anatomo-pathological investigations and caecal content was subjected to volatilome analysis and DNAseq.

RESULTS

Probiotic plus prebiotic inclusion did not significantly influence bird performance and did not produce histopathological alterations or changes in blood measurements, which indicates that the probiotic did not impair the overall health status of the birds. The multi-strain probiotic plus inulin inclusion in broilers increased the abundance of Blautia, Faecalibacterium and Lachnospiraceae and as a consequence an increased level of butyric acid was observed. In addition, the administration of probiotics plus inulin modified the gut microbiota composition also at strain level since probiotics alone or in combination with inulin select specific Faecalibacterium prausnitzi strain populations. The metagenomic analysis showed in probiotic plus prebiotic fed broilers a higher number of genes required for branched-chain amino acid biosynthesis belonging to selected F. prausnitzi strains, which are crucial in increasing immune function resistance to pathogens. In the presence of the probiotic/prebiotic a reduction in the occurrence of antibiotic resistance genes belonging to aminoglycoside, beta-lactamase and lincosamide family was observed.

CONCLUSIONS

The positive microbiome modulation observed is particularly relevant, since the use of these alternative ingredients could promote a healthier status of the broiler's gut.

Biogenic Solution Map Based on the Definition of the Metabolic Correlation Distance between 4-Dimensional Fingerprints

Accurate discrimination and classification of unknown species are the basis to predict its characteristics or applications to make correct decisions. However, for biogenic solutions that are ubiquitous in nature and our daily lives, direct determination of their similarities and disparities by their molecular compositions remains a scientific challenge. Here, we explore a standard and visualizable ontology, termed "biogenic solution map", that organizes multifarious classes of biogenic solutions into a map of hierarchical structures. To build the map, a novel 4-dimensional (4D) fingerprinting method based on data-independent acquisition data sets of untargeted metabolomics is developed, enabling accurate characterization of complex biogenic solutions. A generic parameter of metabolic correlation distance, calculated based on averaged similarities between 4D fingerprints of sample groups, is able to define "species", "genus", and "family" of each solution in the map. With the help of the "biogenic solution map", species of unknown biogenic solutions can be explicitly defined. Simultaneously, intrinsic correlations and subtle variations among biogenic solutions in the map are accurately illustrated. Moreover, it is worth mentioning that samples of the same analyte but prepared by alternative protocols may have significantly different metabolic compositions and could be classified into different "genera".

Augmented visualization by computer vision and chromatographic fingerprinting on comprehensive two-dimensional gas chromatographic patterns: Unraveling diagnostic signatures in food volatilome

Computer Vision is an approach of Artificial Intelligence (AI) that conceptually enables "computers and systems to derive useful information from digital images" giving access to higher-level information and "take actions or make recommendations based on that information". Comprehensive two-dimensional chromatography gives access to highly detailed, accurate, yet unstructured information on the sample's chemical composition, and makes it possible to exploit the AI concepts at the data processing level (e.g., by Computer Vision) to rationalize raw data explorations. The goal is the understanding of the biological phenomena interrelated to a specific/diagnostic chemical signature. This study introduces a novel workflow for Computer Vision based on pattern recognition algorithms (i.e., combined untargeted and targeted UT fingerprinting) which includes the generation of composite Class Images for representative samples' classes, their effective re-alignment and registration against a comprehensive feature template followed by Augmented Visualization by comparative visual analysis. As an illustrative application, a sample set originated from a Research Project on artisanal butter (from raw sweet cream to ripened butter) is explored, capturing the evolution of volatile components along the production chain and the impact of different microbial cultures on the finished product volatilome. The workflow has significant advantages compared to the classical one-step pairwise comparison process given the ability to realign and pairwise compare both targeted and untargeted chromatographic features belonging to Class Images resembling chemical patterns from many different samples with intrinsic biological variability.

Comprehensive analysis of floral scent and fatty acids in nectar of Silene nutans through modern analytical gas chromatography techniques

The aim of this work was to show the potential of multidimensional gas chromatography combined with mass spectrometry and suitable chemometrics means based on untargeted and profiling data analysis to strengthen the information provided by floral scent and nectar fatty acids of four genetically differentiated lineages (E1, W1, W2, and W3) of the nocturnal moth-pollinated herb Silene nutans. Volatile organic compounds emitted by flowers were trapped for a total of 42 samples by in-vivo sampling dynamic head space for analysing floral scent by untargeted approach, while 37 samples of nectar were collected for analysing fatty acids through profiling analysis. The resulting data from floral scent analysis were aligned and compared using a tile-based methodology followed by data mining to access high-level information. Based on floral scent and nectar fatty acid results, it was possible to distinguish E1 from the W lineages, and W3 from W1 and W2. This work puts the bases for a larger study aiming to clarify the existence of prezygotic barriers involved in speciation among lineages of S. nutans, and thus the possible implication of different flower scents and nectar compositions in this phenomenon.

Comprehensive Two-Dimensional Gas Chromatography as a Bioanalytical Platform for Drug Discovery and Analysis

Over the last decades, comprehensive two-dimensional gas chromatography (GC×GC) has emerged as a significant separation tool for high-resolution analysis of disease-associated metabolites and pharmaceutically relevant molecules. This review highlights recent advances of GC×GC with different detection modalities for drug discovery and analysis, which ideally improve the screening and identification of disease biomarkers, as well as monitoring of therapeutic responses to treatment in complex biological matrixes. Selected recent GC×GC applications that focus on such biomarkers and metabolite profiling of the effects of drug administration are covered. In particular, the technical overview of recent GC×GC implementation with hyphenation to the key mass spectrometry (MS) technologies that provide the benefit of enhanced separation dimension analysis with MS domain differentiation is discussed. We conclude by highlighting the challenges in GC×GC for drug discovery and development with perspectives on future trends.

Extending the breadth of saliva metabolome fingerprinting by smart template strategies and effective pattern realignment on comprehensive two-dimensional gas chromatographic data

Comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (GC × GC-TOFMS) is one the most powerful analytical platforms for chemical investigations of complex biological samples. It produces large datasets that are rich in information, but highly complex, and its consistency may be affected by random systemic fluctuations and/or changes in the experimental parameters. This study details the optimization of a data processing strategy that compensates for severe 2D pattern misalignments and detector response fluctuations for saliva samples analyzed across 2 years. The strategy was trained on two batches: one with samples from healthy subjects who had undergone dietary intervention with high/low-Maillard reaction products (dataset A), and the second from healthy/unhealthy obese individuals (dataset B). The combined untargeted and targeted pattern recognition algorithm (i.e., UT fingerprinting) was tuned for key process parameters, the signal-to-noise ratio (S/N), and MS spectrum similarity thresholds, and then tested for the best transform function (global or local, affine or low-degree polynomial) for pattern realignment in the temporal domain. Reliable peak detection achieved its best performance, computed as % of false negative/positive matches, with a S/N threshold of 50 and spectral similarity direct match factor (DMF) of 700. Cross-alignment of bi-dimensional (2D) peaks in the temporal domain was fully effective with a supervised operation including multiple centroids (reference peaks) and a match-and-transform strategy using affine functions. Regarding the performance-derived response fluctuations, the most promising strategy for cross-comparative analysis and data fusion included the mass spectral total useful signal (MSTUS) approach followed by Z-score normalization on the resulting matrix.

Two-dimensional chromatography in screening of bioactive components from natural products

INTRODUCTION

Screening and analysis of bioactive components from natural products is a fundamental part of new drug development and innovation. Two-dimensional (2D) chromatography has been demonstrated to be an effective method for screening and preparation of specific bioactive components from complex natural products.

OBJECTIVE

To collect details of application of 2D chromatography in screening of natural product bioactive components and to outline the research progress of different separation mechanisms and strategies.

METHODOLOGY

Three screening strategies based on 2D chromatography are reviewed, including traditional separation-based screening, bioactivity-guided screening and affinity chromatography-based screening. Meanwhile, in order to cover these aspects, selections of different separation mechanisms and modes are also presented.

RESULTS

Compared with traditional one-dimensional (1D) chromatography, 2D chromatography has unique advantages in terms of peak capacity and resolution, and it is more effective for screening and identifying bioactive components of complex natural products.

CONCLUSION

Screening of natural bioactive components using 2D chromatography helps separation and analysis of complex samples with greater targeting and relevance, which is very important for development of innovative drug leads.

Multidimensional Gas Chromatography Moves Forward

In this extended special feature to celebrate the 35th anniversary edition of LCGC Europe, leading figures from the separation science community explore contemporary trends in separation science and identify possible future developments.

Multidimensional Gas Chromatography: Benefits and Considerations for Current and Prospective Users

Two-dimensional gas chromatography (GC×GC) offers improved separation power for complex samples containing hundreds to thousands of analytes. However, several considerations must be made to determine whether multidimensional gas chromatography (MDGC) is the logical instrument choice to answer a particular scientific question, including, but not limited to, whether the analysis is targeted or non-targeted, the number of analytes of interest, and the presence of interferences that are coeluted, as well as any potential regulatory or industrial constraints. Currently, MDGC remains daunting for many users because of data complexity and the limited tools commercially available, which are critical for improving the accessibility of MDGC. Herein, we discuss considerations that may assist analysts, laboratory managers, regulatory agents, instrument and software vendors, and those interested in understanding the applicability of 2D-GC for the scientific question being investigated.

Effect of Inoculation with Lentilactobacillus buchneri and Lacticaseibacillus paracasei on the Maize Silage Volatilome: The Advantages of Advanced 2D-Chromatographic Fingerprinting Approaches

In this study, the complex volatilome of maize silage samples conserved for 229 d, inoculated with Lentilactobacillus buchneri (Lbuc) and Lacticaseibacillus paracasei (Lpar), is explored by means of advanced fingerprinting methodologies based on comprehensive two-dimensional gas chromatography and time-of-flight mass spectrometry. The combined untargeted and targeted (UT) fingerprinting strategy covers 452 features, 269 of which were putatively identified and assigned within their characteristic classes. The high amounts of short-chain free fatty acids and alcohols were produced by fermentation and led to a large number of esters. The impact of Lbuc fermentation was not clearly distinguishable from the control samples; however, Lpar had a strong and distinctive signature that was dominated by propionic acid and 1-propanol characteristic volatiles. The approach provides a better understanding of silage stabilization mechanisms against the degradative action of yeasts and molds during the exposure of silage to air.

Comprehensive approach of methods for microstructural analysis and analytical tools in lignocellulosic biomass assessment - A review

The trend in the modern world is to replace fossil fuels with green energy sources in order to reduce their environmental impact. The biorefinery industry, within this premise, needs to establish quantitative and qualitative analytical methods to better understand lignocellulosic biomass composition and structure. This paper presents chemical techniques (chromatography, thermal analysis, HRMS, FTIR, NIR, and NMR) and physicochemical techniques (XRD, optical and electron microscopy techniques - Confocal fluorescence, Raman, SPM, AFM, SEM, and TEM) for the microstructural characterization of lignocellulosic biomass and its derivatives. Each of these tools provides different and complementary information regarding molecular and microstructural composition of lignocellulosic biomass. Understanding these properties is essential for the design and operation of associated biomass conversion processing facilities. PAT, monitored in real-time, ensures an economical and balanced mass-energy process. This review aimed to help researchers select the most suitable analytical technique with which to investigate biomass feedstocks with recalcitrant natures.

Single-Crystalline Covalent Organic Frameworks as High-Performance Liquid Chromatographic Stationary Phases for Positional Isomer Separation

Vigorously developing new stationary phases to meet the requirements for the separation of positional isomers that have similar physicochemical properties is still an urgent topic in separation science. Herein, a single-crystalline covalent organic framework (COF-300) packed column for the separation of positional isomers in high-performance liquid chromatography (HPLC) was reported for the first time. Benefitting from its regular shape, excellent chemical and thermal stability, microporous feature, and strong hydrophobicity of single-crystalline COF-300, the single-crystalline COF-300-packed column showed excellent resolution for the separation of positional isomers, including nitroaniline, dichlorobenzene, dibromobenzene, diiodobenzene, diethylbenzene, chloronitrobenzene, bromonitrobenzene, and iodonitrobenzene isomers, which cannot be all separated on commercial columns and a polycrystalline COF-300-packed column. Especially, the resolution values for m-/p-diiodobenzene and o-/m-diiodobenzene were 4.45 and 2.53. Moreover, the alkylbenzene, monosubstituted aromatics, polycyclic aromatic hydrocarbons, and the mixture of ethylbenzene and styrene were also baseline separated on the single-crystalline COF-300-packed column. This successful application not only confirmed the great potential of single-crystalline COFs in HPLC separation of positional isomers but also pioneered the utilization of single-crystalline COFs in separation science.

Comprehensive two-dimensional gas chromatography with mass spectrometry: an advanced bioanalytical technique for clinical metabolomics studies

This review highlights the current state of knowledge in the development of GC × GC-MS for the analysis of clinical metabolites. Selected applications are described as well as our perspectives on current challenges and potential future directions.

Chromatographic Fingerprinting and Food Identity/Quality: Potentials and Challenges

Chromatograms are a valuable source of information about the chemical composition of the food being analyzed. Sometimes, this information is not explicit and appears in a hidden or not obvious way. Thus, the use of chemometric tools and data-mining methods to extract it is required. The fingerprint provided by a chromatogram offers the possibility to perform both identity and quality testing of foodstuffs. This perspective is aimed at providing an updated opinion of chromatographic fingerprinting methodology in the field of food authentication. Furthermore, the limitations, its absence in official analytical methods, and the future directions of this methodology are discussed.

Untargeted profiling and differentiation of geographical variants of wine samples using headspace solid-phase microextraction flow-modulated comprehensive two-dimensional gas chromatography with the support of tile-based Fisher ratio analysis

The volatile fraction of food, also called the food volatilome, is increasingly used to develop new fingerprinting approaches. The characterization of the food volatilome is important to achieve desired flavor profiles in food production processes, or to differentiate different products, with winemaking being one popular area of interest. In the present research, headspace solid-phase microextraction (HS SPME) coupled to flow-modulated comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (FM GC×GC-TOFMS) was used to characterize geographical-based differences in the volatilome of five white "Grillo" wines (of Sicilian origin), comprising the five sample classes. All wines were produced with the same vinification method in 2019. To minimize the influence of minor bottle-to-bottle differences, three bottles of the same wine were randomly selected, and three samples were collected per bottle, resulting in nine sample replicates per wine. Particular emphasis was devoted to the operational conditions of a novel low duty cycle flow modulator. A fast FM GC×GC-TOFMS method with a modulation period of 700 ms and a re-injection period of 80 ms was developed. Following, the instrumental software was exploited to identify class-distinguishing analytes in the dataset via tile-based Fisher ratio analysis (i.e., ChromaTOF Tile). A tile size of 10 modulations (7 s) on the first dimension and 45 spectra (300 ms) on the second dimension was used to encompass average peak widths and to account for minor retention time shifting. Off-line software was used to apply an ANOVA test. A p-value of 0.01 was applied in order to select the most important class-distinguishing analytes, which were input to principal component analysis (PCA). The PCA scores plot showed distinct clustering of the wines according to geographical origin, although the loadings revealed that only a few analytes were necessary to differentiate the wines. However, a comprehensive flavor profile assessment underscored the importance of all the information output by the ChromaTOF Tile software.

Extended characterization of petroleum aromatics using off-line LC-GC-MS

Characterization of crude oil remains a challenge for analytical chemists. With the development of multi-dimensional chromatography and high-resolution mass spectrometry, an impressive number of compounds can be identified in a single sample. However, the large diversity in structure and abundance makes it difficult to obtain full compound coverage. Sample preparation methods such as solid-phase extraction and SARA-type separations are used to fractionate oil into compound classes. However, the molecular diversity within each fraction is still highly complex. Thus, in the routine analysis, only a small part of the chemical space is typically characterized. Obtaining a more detailed composition of crude oil is important for production, processing and environmental aspects. We have developed a high-resolution fractionation method for isolation and preconcentration of trace aromatics, including oxygenated and nitrogen-containing species. The method is based on semi-preparative liquid chromatography. This yields high selectivity and efficiency with separation based on aromaticity, ring size and connectivity. By the separation of the more abundant aromatics, i.e., monoaromatics and naphthalenes, trace species were isolated and enriched. This enabled the identification of features not detectable by routine methods. We demonstrate the applicability by fractionation and subsequent GC-MS analysis of 14 crude oils sourced from the North Sea. The number of tentatively identified compounds increased by approximately 60 to 150% compared to solid-phase extraction and GC × GC-MS. Furthermore, the method was used to successfully identify an extended set of heteroatom-containing aromatics (e.g., amines, ketones). The method is not intended to replace traditional sample preparation techniques or multi-dimensional chromatography but acts as a complementary tool. An in-depth comparison to routine characterization techniques is presented concerning advantages and disadvantages.

Chromatographic Fingerprinting Enables Effective Discrimination and Identitation of High-Quality Italian Extra-Virgin Olive Oils

The challenging process of high-quality food authentication takes advantage of highly informative chromatographic fingerprinting and its identitation potential. In this study, the unique chemical traits of the complex volatile fraction of extra-virgin olive oils from Italian production are captured by comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry and explored by pattern recognition algorithms. The consistent realignment of untargeted and targeted features of over 73 samples, including oils obtained by different olive cultivars (n = 24), harvest years (n = 3), and processing technologies, provides a solid foundation for sample identification and discrimination based on production region (n = 6). Through a dedicated multivariate statistics workflow, identitation is achieved by two-level partial least-square (PLS) regression, which highlights region diagnostic patterns accounting between 58 and 82 of untargeted and targeted compounds, while sample classification is performed by sequential application of soft independent modeling for class analogy (SIMCA) models, one for each production region. Samples are correctly classified in five of the six single-class models, and quality parameters [i.e., sensitivity, specificity, precision, efficiency, and area under the receiver operating characteristic curve (AUC)] are equal to 1.00.

Fingerprinting and Determination of Hepatotoxic Constituents in Polygoni Multiflori Radix Praeparata of Different Producing Places by HPLC

Polygoni Multiflori Radix Praeparata (PMRP) is used as Chinese herbal medicine with long history. However, reports about PMRP hepatotoxicity have increased recently, and producing area might be one reason. This article aims to figure out the relationship between producing area and hepatotoxic ingredients in PMRP. HPLC fingerprint for PMRP was established and the contents of gallic acid, trans-stilbene glycoside (TSG), emodin-8-O-β-D-glucoside (EG), emodin and physcion were determined. Clustering heatmap was implemented by TCMNPAS software，and principal component analysis was implemented by SPSS and SIMCA-P software. Hepatotoxic constituents' contents of PMRP from separate producing area were different. PMRP from Guangxi had the highest content of gallic acid, TSG, EG, emodin and physcion, followed by Hubei, Guangdong, Guizhou, Yunnan. PMRP from Henan had the lowest contents of hepatotoxic components. Hepatotoxic components' contents of PMRP in southern were higher than central China. This study carried out a preliminary qualitative and quantitative investigation on the PMRP from different producing places, which provided a basis for safe medication of PMRP.

Delineating the extra-virgin olive oil aroma blueprint by multiple headspace solid phase microextraction and differential-flow modulated comprehensive two-dimensional gas chromatography

Comprehensive two-dimensional gas chromatography with parallel mass spectrometry and flame ionization detection (GC × GC-MS/FID) enables effective chromatographic fingerprinting of complex samples by comprehensively mapping untargeted and targeted components. Moreover, the complementary characteristics of MS and FID open the possibility of performing multi-target quantitative profiling with great accuracy. If this synergy is applied to the complex volatile fraction of food, sample preparation is crucial and requires appropriate methodologies capable of providing true quantitative results. In this study, untargeted/targeted (UT) fingerprinting of extra-virgin olive oil volatile fractions is combined with accurate quantitative profiling by multiple headspace solid phase microextraction (MHS-SPME). External calibration on fifteen pre-selected analytes and FID predicted relative response factors (RRFs) enable the accurate quantification of forty-two analytes in total, including key-aroma compounds, potent odorants, and olive oil geographical markers. Results confirm good performances of comprehensive UT fingerprinting in developing classification models for geographical origin discrimination, while quantification by MHS-SPME provides accurate results and guarantees data referability and results transferability over years. Moreover, by this approach the extent of internal standardization procedure inaccuracy, largely adopted in food volatiles profiling, is measured. Internal standardization yielded an average relative error of 208 % for the fifteen calibrated compounds, with an overestimation of + 538% for (E)-2-hexenal, the most abundant yet informative volatile of olive oil, and a -89% and -80% for (E)-2-octenal and (E)-2-nonenal respectively, analytes with a lower HS distribution constant. Compared to existing methods based on 1D-GC, the current procedure offers better separation power and chromatographic resolution that greatly improve method specificity and selectivity and results in lower LODs and LOQs, high calibration performances (i.e., R² and residual distribution), and wider linear range of responses. As an artificial intelligence smelling machine, the MHS-SPME-GC × GC-MS/FID method is here adopted to delineate extra-virgin olive oil aroma blueprints; an objective tool with great flexibility and reliability that can improve the quality and information power of each analytical run.

Chromatographic Fingerprinting Strategy to Delineate Chemical Patterns Correlated to Coffee Odor and Taste Attributes

Coffee cupping includes both aroma and taste, and its evaluation considers several different attributes simultaneously to define flavor quality and therefore requires complementary data from aroma and taste. This study investigates the potential and limits of a data-driven approach to describe the sensory quality of coffee using complementary analytical techniques usually available in routine quality control laboratories. Coffee flavor chemical data from 155 samples were obtained by analyzing volatile (headspace-solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS)) and nonvolatile (liquid chromatography-ultraviolet/diode array detector (LC-UV/DAD)) fractions, as well as from sensory data. Chemometric tools were used to explore the data sets, select relevant features, predict sensory scores, and investigate the networks between features. A comparison of the Q model parameter and root-mean-squared error prediction (RMSEP) highlights the variable influence that the nonvolatile fraction has on prediction, showing that it has a higher impact on describing acid, bitter, and woody notes than on flowery and fruity. The data fusion emphasized the aroma contribution to driving sensory perceptions, although the correlative networks highlighted from the volatile and nonvolatile data deserve a thorough investigation to verify the potential of odor-taste integration.

Chemical fingerprinting strategies based on comprehensive two-dimensional gas chromatography combined with gas chromatography-olfactometry to capture the unique signature of Piemonte peppermint essential oil (Mentha x piperita var Italo-Mitcham)

Accurate, reliable, and informative mapping of untargeted and targeted components across many samples is here performed by combining off-line GC-Olfactometry (GC-O) and comprehensive two-dimensional gas chromatography (GC×GC) coupled to time-of-flight mass spectrometry with variable ionization energy (TOF MS featuring Tandem Ionization™). In particular, untargeted and targeted (UT) features patterns are processed by chromatographic fingerprinting, giving differential priority to potent odorants' retention-times regions. Distinguishing peppermint essential oil (EO) from Piedmont (Italy - Mentha × piperita L. var. Italo-Mitcham - Menta di Pancalieri EO), with its unique sensory fingerprint (i.e., freshness and long-lasting sweetness), from high-quality peppermint EOs produced in other areas poses a great challenge. Chromatographic UT fingerprinting provided a great chemical dimensionality by mapping more than 350 peak-regions at 70 eV and 135 at 12 eV. From them, 95 components were identified and responses compared to available literature. Then, potent odorants, detected by GC-O using the aroma extraction dilution analysis (AEDA), were tracked over the chromatographic space and tentatively identified. With the highest flavor dilution (FD), 1,8-cineole (eucalyptus, fresh, camphoraceous); menthone (minty, herbaceous); and menthofuran (minty, musty, petroleum-like) were highlighted. Responsible for creamy and coumarinic notes were the diasteroisomers of (3,6)-dimethyl-4,5,6,7-tetrahydrobenzo[b]-furan-2(3H)-one (i.e., menthofurolactones), detected in higher relative abundance in Pancalieri EOs. By prioritizing the investigation of volatiles on higher LogFD retention regions, including 131 untargeted/targeted features, Pancalieri EOs were separately clustered from United States samples. Besides pre-targeted analytes, additional untargeted features were post-processed for identification within marker chemicals. Myrtenyl methyl ether, ethyl 3-methyl butanoate, propyl-2-methylbutanoate, and (E)-2-hexenal were putatively identified. Of the "unknown - knowns" with diagnostic roles, all metadata were collected including low energy spectra at 12 eV, which were found to be highly complementary to 70 eV spectra.