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Cordero C.,University of Turin | Liberto E.,University of Turin | Bicchi C.,University of Turin | Rubiolo P.,University of Turin | And 3 more authors.
Journal of Chromatography A | Year: 2010

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 × 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 × 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 × 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. © 2010 Elsevier B.V.


Reichenbach S.E.,University of Nebraska - Lincoln | Tian X.,University of Nebraska - Lincoln | Cordero C.,University of Turin | Tao Q.,GC Image, LLC
Journal of Chromatography A | Year: 2012

This review surveys different approaches for generating features from comprehensive two-dimensional chromatography for non-targeted cross-sample analysis. The goal of non-targeted cross-sample analysis is to discover relevant chemical characteristics (such as compositional similarities or differences) from multiple samples. In non-targeted analysis, the relevant characteristics are unknown, so individual features for all chemical constituents should be analyzed, not just those for targeted or selected analytes. Cross-sample analysis requires matching the corresponding features that characterize each constituent across multiple samples so that relevant characteristics or patterns can be recognized. Non-targeted, cross-sample analysis requires generating and matching all features across all samples. Applications of non-targeted cross-sample analysis include sample classification, chemical fingerprinting, monitoring, sample clustering, and chemical marker discovery. Comprehensive two-dimensional chromatography is a powerful technology for separating complex samples and so is well suited for non-targeted cross-sample analysis. However, two-dimensional chromatographic data is typically large and complex, so the computational tasks of extracting and matching features for pattern recognition are challenging. This review examines five general approaches that researchers have applied to these difficult problems: visual image comparisons, datapoint feature analysis, peak feature analysis, region feature analysis, and peak-region feature analysis. © 2011 Elsevier B.V.


Reichenbach S.E.,University of Nebraska - Lincoln | Tian X.,University of Nebraska - Lincoln | Tao Q.,GC Image, LLC | Ledford Jr. E.B.,Zoex Corporation | And 2 more authors.
Talanta | Year: 2011

Abstract: This paper describes informatics for cross-sample analysis with comprehensive two-dimensional gas chromatography (GCxGC) and high-resolution mass spectrometry (HRMS). GCxGC-HRMS analysis produces large data sets that are rich with information, but highly complex. The size of the data and volume of information requires automated processing for comprehensive cross-sample analysis, but the complexity poses a challenge for developing robust methods. The approach developed here analyzes GCxGC-HRMS data from multiple samples to extract a feature template that comprehensively captures the pattern of peaks detected in the retention-times plane. Then, for each sample chromatogram, the template is geometrically transformed to align with the detected peak pattern and generate a set of feature measurements for cross-sample analyses such as sample classification and biomarker discovery. The approach avoids the intractable problem of comprehensive peak matching by using a few reliable peaks for alignment and peak-based retention-plane windows to define comprehensive features that can be reliably matched for cross-sample analysis. The informatics are demonstrated with a set of 18 samples from breast-cancer tumors, each from different individuals, six each for Grades 1-3. The features allow classification that matches grading by a cancer pathologist with 78% success in leave-one-out cross-validation experiments. The HRMS signatures of the features of interest can be examined for determining elemental compositions and identifying compounds. © 2010 Elsevier B.V. All rights reserved.


Latha I.,University of Nebraska - Lincoln | Reichenbach S.E.,University of Nebraska - Lincoln | Tao Q.,GC Image, LLC
Journal of Chromatography A | Year: 2011

Comprehensive two-dimensional gas chromatography (GC×GC) is a powerful technology for separating complex samples. The typical goal of GC×GC peak detection is to aggregate data points of analyte peaks based on their retention times and intensities. Two techniques commonly used for two-dimensional peak detection are the two-step algorithm and the watershed algorithm. A recent study [4] compared the performance of the two-step and watershed algorithms for GC×GC data with retention-time shifts in the second-column separations. In that analysis, the peak retention-time shifts were corrected while applying the two-step algorithm but the watershed algorithm was applied without shift correction. The results indicated that the watershed algorithm has a higher probability of erroneously splitting a single two-dimensional peak than the two-step approach. This paper reconsiders the analysis by comparing peak-detection performance for resolved peaks after correcting retention-time shifts for both the two-step and watershed algorithms. Simulations with wide-ranging conditions indicate that when shift correction is employed with both algorithms, the watershed algorithm detects resolved peaks with greater accuracy than the two-step method. © 2011 Elsevier B.V.


Reichenbach S.E.,University of Nebraska - Lincoln | Tian X.,University of Nebraska - Lincoln | Boateng A.A.,U.S. Department of Agriculture | Mullen C.A.,U.S. Department of Agriculture | And 2 more authors.
Analytical Chemistry | Year: 2013

Comprehensive two-dimensional chromatography is a powerful technology for analyzing the patterns of constituent compounds in complex samples, but matching chromatographic features for comparative analysis across large sample sets is difficult. Various methods have been described for pairwise peak matching between two chromatograms, but the peaks indicated by these pairwise matches commonly are incomplete or inconsistent across many chromatograms. This paper describes a new, automated method for postprocessing the results of pairwise peak matching to address incomplete and inconsistent peak matches and thereby select chromatographic peaks that reliably correspond across many chromatograms. Reliably corresponding peaks can be used both for directly comparing relative compositions across large numbers of samples and for aligning chromatographic data for comprehensive comparative analyses. To select reliable features for a set of chromatograms, the Consistent Cliques Method (CCM) represents all peaks from all chromatograms and all pairwise peak matches in a graph, finds the maximal cliques, and then combines cliques with shared peaks to extract reliable features. The parameters of CCM are the minimum number of chromatograms with complete pairwise peak matches and the desired number of reliable peaks. A particular threshold for the minimum number of chromatograms with complete pairwise matches ensures that there are no conflicts among the pairwise matches for reliable peaks. Experimental results with samples of complex bio-oils analyzed by comprehensive two-dimensional gas chromatography (GCxGC) coupled with mass spectrometry (GCxGC-MS) indicate that CCM provides a good foundation for comparative analysis of complex chemical mixtures. © 2013 American Chemical Society.


Grant
Agency: National Science Foundation | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 499.98K | Year: 2011

This Small Business Innovation Research Phase II project proposes to develop a system for automated classification of biological samples and discovery of biomarkers. The system will be designed to perform comprehensive pattern analysis of state-of-the-art biochemical separations generated by comprehensive two-dimensional chromatography (GCxGC) with high-resolution mass spectrometry (HRMS). The pairing of GCxGC and HRMS combines highly effective molecular separations with precise elemental analysis. A critical challenge for effective utilization of GCxGC-HRMS for biochemical sample classification and biomarker discovery is the difficulty of analyzing and interpreting the massive, complex data for metabolomic features. The quantity and complexity of the data, as well as the large dimensionality of the metabolome, and the possibility that significant chemical characteristics may be subtle and involve patterns of multiple constituents, necessitate investigation and development of new bioinformatics. The principal technical objective is an innovative framework for comprehensive feature matching and analysis across many samples. Specifically, the framework will incorporate advanced methods for multidimensional peak detection, peak pattern matching across large sample sets, data alignment, comprehensive feature matching, and multi-sample analyses (e.g., classification and biomarker discovery) with large sample sets. The anticipated result is a commercial system for automated multi-sample analysis. The broader impact/commercial potential of this project will be realized through improved informatics for biological classification and biomarker discovery. These tools will enable researchers to better understand biochemical processes and to discover metabolic biomarkers, which could lead to improved methods for disease diagnoses and treatments. These information technologies will foster utilization of advanced GCxGC-HRMS instrumentation, thereby contributing to the impetus for future instrument development. The informatics developed in this project also will be relevant for other classification problems involving multidimensional, multispectral data, including other applications (such as biofuels), other types of chemical analyses (such as multidimensional spectroscopy), and other fields (such as remote-sensing multispectral geospatial imagers). This project will contribute to national competitiveness in the global market for analytical technologies and will contribute to workforce development by involving students in research experiences through internships and student projects. Software developed in the project and an example dataset will be available to educational institutions to allow students to more easily explore biochemical complexity.


Grant
Agency: National Science Foundation | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 174.64K | Year: 2010

This Small Business Innovation Research (SBIR) Phase I project proposes to develop a system for automated classification of biological samples and discovery of biomarkers. The goal is a system to perform comprehensive pattern analysis of state-of-the-art biochemical separations generated by comprehensive two-dimensional gas chromatography (GCxGC) with high-resolution mass spectrometry (HRMS). A critical challenge for elective utilization of GCxGC-HRMS for biochemical classification and biomarker discovery is the diffculty of analyzing and interpreting the massive, complex data for metabolomic and proteomic features. The quantity and complexity of the data, as well as the large dimensionality of the biochemistry in which significant characteristics may be subtle and involve patterns of variations in multiple constituents, necessitate the investigation and development of new bioinformatics. The principal technical objective is an innovative framework for comprehensive feature matching and analysis across many samples. Feature matching is the basis for uniformly labeling structures so that similarities and differences can be documented. Specifically, the framework will incorporate advanced methods for multidimensional peak detection, peak pattern matching across large sample sets, data alignment, GCxGC-HRMS feature computations, and classification with large feature sets. The anticipated result is the technical foundation for a commercial system to classify biological samples and identify significant biomarkers. The broader impact/commercial potential of this project, if successful, will be a better understanding of biochemical processes and discovery of metabolomic and proteomic biomarkers, leading to improved methods for disease diagnoses and treatments. These innovative bioinformatics will contribute to economic competitiveness in the global market for analytical technologies and will foster utilization of advanced GCxGC-HRMS instrumentation. The informatics developed in this project also will be relevant for other classification problems involving multidimensional, multispectral data, including other applications (such as biofuels),other types of chemical analyses (such as multidimensional spectroscopy), and other fields (such as remote-sensing multispectral geospatial imagers). The project will contribute to workforce development, by involving student interns in research experiences through internships and project sponsorships, and to education, by providing software and example data to allow students to more easily explore biochemical complexity.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: | Award Amount: 174.64K | Year: 2010

This Small Business Innovation Research (SBIR) Phase I project proposes to develop a system for automated classification of biological samples and discovery of biomarkers. The goal is a system to perform comprehensive pattern analysis of state-of-the-art biochemical separations generated by comprehensive two-dimensional gas chromatography (GCxGC) with high-resolution mass spectrometry (HRMS). A critical challenge for elective utilization of GCxGC-HRMS for biochemical classification and biomarker discovery is the diffculty of analyzing and interpreting the massive, complex data for metabolomic and proteomic features. The quantity and complexity of the data, as well as the large dimensionality of the biochemistry in which significant characteristics may be subtle and involve patterns of variations in multiple constituents, necessitate the investigation and development of new bioinformatics. The principal technical objective is an innovative framework for comprehensive feature matching and analysis across many samples. Feature matching is the basis for uniformly labeling structures so that similarities and differences can be documented. Specifically, the framework will incorporate advanced methods for multidimensional peak detection, peak pattern matching across large sample sets, data alignment, GCxGC-HRMS feature computations, and classification with large feature sets. The anticipated result is the technical foundation for a commercial system to classify biological samples and identify significant biomarkers.

The broader impact/commercial potential of this project, if successful, will be a better understanding of biochemical processes and discovery of metabolomic and proteomic biomarkers, leading to improved methods for disease diagnoses and treatments. These innovative bioinformatics will contribute to economic competitiveness in the global market for analytical technologies and will foster utilization of advanced GCxGC-HRMS instrumentation. The informatics developed in this project also will be relevant for other classification problems involving multidimensional, multispectral data, including other applications (such as biofuels),other types of chemical analyses (such as multidimensional spectroscopy), and other fields (such as remote-sensing multispectral geospatial imagers). The project will contribute to workforce development, by involving student interns in research experiences through internships and project sponsorships, and to education, by providing software and example data to allow students to more easily explore biochemical complexity.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: SMALL BUSINESS PHASE II | Award Amount: 829.97K | Year: 2011

This Small Business Innovation Research Phase II project proposes to develop a system for automated classification of biological samples and discovery of biomarkers. The system will be designed to perform comprehensive pattern analysis of state-of-the-art biochemical separations generated by comprehensive two-dimensional chromatography (GCxGC) with high-resolution mass spectrometry (HRMS). The pairing of GCxGC and HRMS combines highly effective molecular separations with precise elemental analysis. A critical challenge for effective utilization of GCxGC-HRMS for biochemical sample classification and biomarker discovery is the difficulty of analyzing and interpreting the massive, complex data for metabolomic features. The quantity and complexity of the data, as well as the large dimensionality of the metabolome, and the possibility that significant chemical characteristics may be subtle and involve patterns of multiple constituents, necessitate investigation and development of new bioinformatics. The principal technical objective is an innovative framework for comprehensive feature matching and analysis across many samples. Specifically, the framework will incorporate advanced methods for multidimensional peak detection, peak pattern matching across large sample sets, data alignment, comprehensive feature matching, and multi-sample analyses (e.g., classification and biomarker discovery) with large sample sets. The anticipated result is a commercial system for automated multi-sample analysis.

The broader impact/commercial potential of this project will be realized through improved informatics for biological classification and biomarker discovery. These tools will enable researchers to better understand biochemical processes and to discover metabolic biomarkers, which could lead to improved methods for disease diagnoses and treatments. These information technologies will foster utilization of advanced GCxGC-HRMS instrumentation, thereby contributing to the impetus for future instrument development. The informatics developed in this project also will be relevant for other classification problems involving multidimensional, multispectral data, including other applications (such as biofuels), other types of chemical analyses (such as multidimensional spectroscopy), and other fields (such as remote-sensing multispectral geospatial imagers). This project will contribute to national competitiveness in the global market for analytical technologies and will contribute to workforce development by involving students in research experiences through internships and student projects. Software developed in the project and an example dataset will be available to educational institutions to allow students to more easily explore biochemical complexity.


Trademark
GC Image, LLC | Date: 2015-09-23

COMPUTER SOFTWARE FOR DATA IMAGE VISUALIZATION, IMAGE PROCESSING AND DATA IMAGE ANALYSIS, AND ISSUING REPORTS IN CONNECTION THEREWITH.

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