Determination of chemical classes from mass spectra of toxic organic compounds by SIMCA pattern recognition and information theory

Scott, Donald R.

doi:10.1021/ac00295a051

Cited by 34 publications

(9 citation statements)

References 46 publications

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“…The data generated by congener-specific analysis provide information that can be synthesized by principal components analysis (PCA), a statistical classification method that can be used to identify unique groups of samples based on chemical residue profiles (Scott, 1986;Vogt et al, 1987;Ciborowski and Corkum, 1988;Schwartz and Stalling, 1991;Kovats and Ciborowski, 1993;Steingraeber et al, 1994). To minimize the influence of total PCB concentrations and allow a representative comparison of compositional similarities among samples for PCA, we suggest that (1) congener concentrations less than the method limit of quantification or detection be assigned a value 0 and (2) congener concentrations for each sample be expressed as a percentage of the total PCB concentration (i.e.…”

Section: Sample Analysesmentioning

confidence: 99%

Bioassessment of contaminant transport and distribution in aquatic ecosystems by chemical analysis of burrowing mayflies (Hexagenia)

Steingraeber¹,

Wiener²

1995

Regul. Rivers: Res. Mgmt.

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Burrowing mayfly nymphs (Ephemeroptera) inhabit and ingest fine-grained sediments and detritus that may be enriched with metals and persistent organic compounds. The burrowing nymphs can externally adsorb and internally assimilate these contaminants, providing a link for the food chain transfer of potentially toxic substances from sediments to organisms in higher trophic levels. The emergent adults are short-lived and do not feed, thus their gut contents do not contribute greatly to their total contaminant burden. These characteristics make Hexagenia spp. and certain other burrowing mayflies useful for assessing ecosystem contamination. General protocols are presented for the collection, processing and analysis of emergent mayflies to assess the spatial distribution and bioaccumulation of sediment-associated contaminants in aquatic ecosystems. Two essential components of this bioassessment approach are a network of on-site volunteers with the materials and instructions needed to correctly collect and store samples and quality assurance procedures to estimate the accuracy of chemical analyses. The utility of this approach is demonstrated with an example of its application to the Upper Mississippi River (USA). Determination of cadmium, mercury and polychlorinated biphenyl congeners in emergent Hexagenia bilineata from a 1250 km reach of this river revealed (1) several source areas of contaminants and ( 2 ) distinct patterns in the bioaccumulation (and apparent sediment-associated transport) of each residue on both small and large spatial scales.

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Section: Sample Analysesmentioning

confidence: 99%

Bioassessment of contaminant transport and distribution in aquatic ecosystems by chemical analysis of burrowing mayflies (Hexagenia)

Steingraeber¹,

Wiener²

1995

Regul. Rivers: Res. Mgmt.

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“…Classification of an unknown is based on the goodness of fit of its response vector to each of the models. This approach differs from standard PCR where principal components are derived from the data matrix as a whole (Dunn, et al 1989;Vogt et al, 1987;Scott, 1986).…”

Section: Extended Disjoint Principal Components Regression (Edpcr)mentioning

confidence: 99%

Assessment of subsurface VOCs using a chemical microsensor array. Final report

Batterman¹,

Zellers

1993

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PREFACECun'cnfly available technology is not adequate to assess environmental contamination at Department of Energy (DOE) sites, take permanent remedial action, and eliminate or minimize the environmental impact of futm'eoperations. Technical resources to address these shortcomings exist within the DOE community and the private sector, but the involvement of the private sector ] in attaining permanent and cost=effective solutions has been limited. L During 1990, on bohalf of DOE's Office of Technology I_velopment, Argonne Nadonal Laboratory (ANL) conducted a competitive procurement of research and development projects addressing soil remediation, groundwater remediation, site characterization, and contaminant containment, Fifteen contracts were negotiated in these areas. This report documents work performed as part of the Private Sector Research and D_velopment Program sponsored by the DOE's Office of Technology Development within the Environmental Restoration and Waste Management Program. The research and development work described herein was conducted under contract to ANL.On behalf of DOE and ANL, I wish to thankthe performing contractor and especially the report authors for their cooperation and their contribution to development of new processes for characterization and remediation of DOE's environmental problems. We anticipate that the R&D investment described here will bc repaid many-fold in the application of better, faster, safer, and cheaper technologies.Details of the procurement process and status reports for all 15 of the contractors performing under this program can be found in "Applied Research and Development Private Sector Accomplishments = Interim Report" (Report No. DOE/CH-9216

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“…The second method is the Soft Independent Modeling of Class Analogy (SIMCA) [20] for the annotation of unknown peaks, and some techniques of SIMCA utilizing mass spectra have been developed, especially in toxic studies [21-25]. SIMCA is a supervised classification technique that is based on principal component analysis (PCA) [26], and it is useful for building multiple class models.…”

Section: Introductionmentioning

confidence: 99%

GC/MS based metabolomics: development of a data mining system for metabolite identification by using soft independent modeling of class analogy (SIMCA)

et al. 2011

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BackgroundThe goal of metabolomics analyses is a comprehensive and systematic understanding of all metabolites in biological samples. Many useful platforms have been developed to achieve this goal. Gas chromatography coupled to mass spectrometry (GC/MS) is a well-established analytical method in metabolomics study, and 200 to 500 peaks are routinely observed with one biological sample. However, only ~100 metabolites can be identified, and the remaining peaks are left as "unknowns".ResultWe present an algorithm that acquires more extensive metabolite information. Pearson's product-moment correlation coefficient and the Soft Independent Modeling of Class Analogy (SIMCA) method were combined to automatically identify and annotate unknown peaks, which tend to be missed in routine studies that employ manual processing.ConclusionsOur data mining system can offer a wealth of metabolite information quickly and easily, and it provides new insights, particularly into food quality evaluation and prediction.

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Determination of chemical classes from mass spectra of toxic organic compounds by SIMCA pattern recognition and information theory

Cited by 34 publications

References 46 publications

Bioassessment of contaminant transport and distribution in aquatic ecosystems by chemical analysis of burrowing mayflies (Hexagenia)

Bioassessment of contaminant transport and distribution in aquatic ecosystems by chemical analysis of burrowing mayflies (Hexagenia)

Assessment of subsurface VOCs using a chemical microsensor array. Final report

GC/MS based metabolomics: development of a data mining system for metabolite identification by using soft independent modeling of class analogy (SIMCA)

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