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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., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Background: Comprehensive two-dimensional gas chromatography (GC×GC) combined with time-of-flight (TOF) MS is the most informative analytical approach for chemical characterization of the complex food volatilome. Key analytical features include separation power and resolution enhancement, improved sensitivity, and structured separation patterns from chemically correlated analytes., Objective: In this study, we explore the complex extra-virgin olive oil volatilome by combining headspace (HS) solid-phase microextraction (SPME), applied under HS linearity conditions to GC×GC-TOF MS and featuring hard and soft ionization in tandem., Method: Multiple analytical dimensions are combined in a single run and evaluated in terms of chemical dimensionality, method absolute and relative sensitivity, identification reliability provided by spectral signatures acquired at 70 and 12 eV, and dynamic and linear range of response provided by soft ionization., Results: Method effectiveness is validated on a sample set of oils from Picual olives at different ripening stages. Ripening markers [3,4-diethyl-1,5-hexadiene (RS/SR), 3,4-diethyl-1,5-hexadiene (meso), (5Z)-3-ethyl-1,5-octadiene, (5E)-3-ethyl-1,5-octadiene, (E, Z)-3,7-decadiene and (E, E)-3,7-decadiene, (Z)-2-hexenal, (Z)-3-hexenal and (Z)-3-hexenal, (E)-2-pentenal, (Z)-2-pentenal, 1-pentanol, 1-penten-3-ol, 3-pentanone, and 1-penten-3-one] and quality indexes [(Z)-3-hexenal/nonanal, (Z)-3-hexenal/octane, (E)-2-pentenal/nonanal, and (E)-2-pentenal/octane] are confirmed for their validity in HS linearity conditions., Conclusions: For the complex olive oil volatilome, the proposed approach offers concrete advantages for the validation of the informative role of existing analytes while suggesting new potential markers to be studied in larger sample sets., Highlights: The accurate fingerprinting of volatiles by HS-SPME operating in HS linearity conditions followed by GC×GC-TOF MS featuring tandem ionization gives the opportunity to improve the quality of analytical data and reliability of results., (© AOAC INTERNATIONAL 2020. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

This review focuses on the role that comprehensive two-dimensional gas chromatography can play within the investigation workflows of food-omics and related disciplines and subdisciplines, including food metabolomics, nutrimetabolomics, sensomics, and food safety. After a short introductory survey, discussing the intriguing context of system biology and integrationist approaches of investigation, the concepts of analytical dimensions and the key characteristics of comprehensive two-dimensional gas chromatography are introduced. Through a selection of relevant examples, the boosting role of comprehensive two-dimensional gas chromatography within food-omics is described, providing to the reader evidence of how comprehensive multidimensional separations based platforms have introduced new concepts and tools in the analytical measurement of complex biological samples., (© 2021 Wiley-VCH GmbH.)

The volatile fraction of hazelnuts encrypts information about: cultivar/geographical origin, post-harvest treatments, oxidative stability and sensory quality. However, sensory features could be buried under other dominant chemical signatures posing challenges to an effective classification based on pleasant/unpleasant notes. Here a novel workflow that combines Untargeted and Targeted (UT) fingerprinting on comprehensive two-dimensional gas-chromatographic patterns is developed to discriminate spoiled hazelnuts from those of acceptable quality. By flash-profiling, six hazelnut classes are defined: Mould, Mould-rancid-solvent, Rancid, Rancid-stale, Rancid-solvent, and Uncoded KO. Chromatographic fingerprinting on composite 2D chromatograms from samples belonging to the same class (i.e., composite class-images) enabled effective selection of chemical markers: (a) octanoic acid that guides the sensory classification being positively correlated to mould; (b) ƴ-nonalactone, ƴ-hexalactone, acetone, and 1-nonanol that are decisive to classify OK and rancid samples; (c) heptanoic and hexanoic acids and ƴ-octalactone present in high relative abundance in rancid-solvent and rancid-stale samples., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Comprehensive two-dimensional gas chromatography coupled with mass spectrometric detection (GC × GC-MS) offers an information-rich basis for effective chemical fingerprinting of food. However, GC × GC-MS yields 2D-peak patterns (i.e., sample 2D fingerprints) whose consistency may be affected by variables related to either the analytical platform or to the experimental parameters adopted for the analysis. This study focuses on the complex volatile fraction of extra-virgin olive oil and addresses 2D-peak patterns variations, including MS signal fluctuations, as they may occur in long-term studies where pedo-climatic, harvest year, or shelf life changes are studied. The 2D-pattern misalignments are forced by changing chromatographic settings and MS acquisition. All procedural steps, preceding pattern recognition by template matching, are analyzed and a rational workflow defined to accurately realign patterns and analytes metadata. Signal-to-noise ratio (SNR) detection threshold, reference spectra extraction, and similarity match factor threshold are critical to avoid false-negative matches. Distance thresholds and polynomial transform parameters are key for effective template matching. In targeted analysis (supervised workflow) with optimized parameters, method accuracy reaches 92.5% (i.e., % of true-positive matches) while for combined untargeted and targeted ( UT) fingerprinting (unsupervised workflow), accuracy reaches 97.9%. Response normalization also is examined, evidencing good performance of multiple internal standard normalization that effectively compensates for discriminations occurring during injection of highly volatile compounds. The resulting workflow is simple, effective, and time efficient.

This paper presents current developments and future perspectives on the spread of advanced analytical tasks in the field of food volatile analysis. The topics outlined comprise (a) recent advances on miniaturized sampling techniques; (b) the potential and challenges of multidimensional gas chromatography coupled with mass spectrometric detection for volatile identification and quantitation in samples with complex matrices;, ((c) the potential of comprehensive two-dimensional gas chromatography in fingerprinting studies, in particular for classifying complex samples in routine analysis; and (d) the key role of dedicated software tools for data elaboration with comprehensive two-dimensional separations.)

This study investigates chemical information of volatile fractions of high-quality cocoa (Theobroma cacao L. Malvaceae) from different origins (Mexico, Ecuador, Venezuela, Columbia, Java, Trinidad, and Sao Tomè) produced for fine chocolate. This study explores the evolution of the entire pattern of volatiles in relation to cocoa processing (raw, roasted, steamed, and ground beans). Advanced chemical fingerprinting (e.g., combined untargeted and targeted fingerprinting) with comprehensive two-dimensional gas chromatography coupled with mass spectrometry allows advanced pattern recognition for classification, discrimination, and sensory-quality characterization. The entire data set is analyzed for 595 reliable two-dimensional peak regions, including 130 known analytes and 13 potent odorants. Multivariate analysis with unsupervised exploration (principal component analysis) and simple supervised discrimination methods (Fisher ratios and linear regression trees) reveal informative patterns of similarities and differences and identify characteristic compounds related to sample origin and manufacturing step.

Comprehensive two-dimensional gas chromatography (GC × GC) is the most effective multidimensional separation technique for in-depth investigations of complex samples of volatiles (VOC) in food. However, each analytical run produces dense, multi-dimensional data, so elaboration and interpretation of chemical information is challenging. This study exploits recent advances of GC × GC-MS chromatographic fingerprinting to study VOCs distributions from Extra Virgin Olive Oil (EVOO) samples of a single botanical origin (Picual), cultivated in well-defined plots in Granada (Spain), and harvested at different maturation stages. A new integrated work-flow, fully supported by dedicated and automated software tools, combines untargeted and targeted (UT) approaches based on peak-region features to achieve the most inclusive fingerprinting. Combined results from untargeted and targeted methods are consistent, reliable, and informative on discriminant features (analytes) correlated with optimal ripening of olive fruits and sensory quality of EVOOs. The great flexibility of the UT fingerprinting here adopted enables retrospective analysis with great confidence and provides data to validate the transferability of ripening indicators ((Z)-3-hexenal, (Z)-2-hexenal, (E)-2-pentenal, nonanal, 6-methyl-5-hepten-2-one, octane) to external samples sets. Direct image comparison, based on visual features, also is investigated for quick and effective pair-wise investigations. Its implementation with reliable metadata generated by UT fingerprinting confirms the maturity of 2D data elaboration tools and makes advanced image processing a real perspective., (Copyright © 2016 Elsevier B.V. All rights reserved.)

This study examined how advanced fingerprinting methods (i.e., non-targeted methods) provide reliable and specific information about groups of samples based on their component distribution on the GC x GC chromatographic plane. The volatile fractions of roasted hazelnuts (Corylus avellana L.) from nine different geographical origins, comparably roasted for desirable flavor and texture, were sampled by headspace-solid phase micro extraction (HS-SPME) and then analyzed by GC x GC-qMS. The resulting patterns were processed by: (a) "chromatographic fingerprinting", i.e., a pattern recognition procedure based on retention-time criteria, where peaks correspondences were established through a comprehensive peak pattern covering the chromatographic plane; and (b) "comprehensive template matching" with reliable peak matching, where peak correspondences were constrained by retention time and MS fragmentation pattern similarity criteria. Fingerprinting results showed how the discrimination potential of GC x GC can be increased by including in sample comparisons and correlations all the detected components and, in addition, provide reliable results in a comparative analysis by locating compounds with a significant role. Results were completed by a chemical speciation of volatiles and sample profiling was extended to known markers whose distribution can be correlated to sensory properties, geographical origin, or the effect of thermal treatment on different classes of compounds. The comprehensive approach for data interpretation here proposed may be useful to assess product specificity and quality, through measurable parameters strictly and consistently correlated to sensory properties and origin., (Copyright 2010 Elsevier B.V. All rights reserved.)

Comprehensive two-dimensional gas chromatography (GC×GC) coupled with mass spectrometry (MS) is one of the most powerful analytical techniques now available for detailed analysis, identification and quantitation of medium-to-high complexity mixtures. However, the number of application methods that combine fingerprinting and/or profiling with quantitation of informative volatile analytes (targets) is still limited. Possible reasons are related to the huge amount of information to handle and to the availability of reference standards for calibration. Although quantitative analysis by GC×GC is complex it has important advantages: (i) to assess data/results over an extended time frame, varied instrumentation and different laboratories; (ii) to interpret the biological role of (potential) biomarkers; (iii) to evaluate the impact of potent odourants; and/or (iv) to define product quality, e.g. relative to a reference standard. In this study, a GC×2GC-MS/FID platform consisting of one primary column (1D) coupled to two parallel secondary columns (2D) having an identical inner diameter, stationary phase chemistry and film thickness, which, in turn, are connected to two detectors: a fast quadrupole MS and a FID, was adopted for quantitative profiling of essential oils (EOs). Two medium complexity EOs (i.e. Mentha and Lavandula species) that pose different quantitation challenges were taken as examples and a selection of quality markers subjected to an extensive method performance evaluation (e.g. method validation). Experimental results confirmed the platform's reliability in terms of: linearity, precision and quantitation accuracy. In addition, predicted FID Relative Response Factors (RRFs) based on combustion enthalpies were adopted to extend quantitation to all identified analytes. The experimental data demonstrated the accuracy of the predicted RRFs, supporting their adoption in quantitation of EO markers. This approach is of particular interest for those applications where reference standards are not (easily) available and/or regulated. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]