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Automated pipeline for classifying Aroclors in soil by gas chromatography/mass spectrometry using modulo compressed two-way data objects.
Talanta. 2013 Dec 15; 117:483-91.T

Abstract

Seven polychlorinated biphenyls (PCBs) commercial mixtures, Aroclor 1016, 1221, 1232, 1242, 1248, 1254, and 1260, were analyzed by gas chromatography/mass spectrometry (GC/MS) combined with solid phase microextraction (SPME). Three pattern recognition methods: a fuzzy rule-building expert system (FuRES), partial least-squares discriminant analysis (PLS-DA), and a fuzzy optimal associative memory (FOAM) were used to build classification models. Modulo compression was introduced for data preprocessing to extract the characteristic features and compress the data size. Baseline correction and data normalization were also applied prior to data processing. Four GC/MS data set configurations were constructed and used to evaluate the classifiers and data pretreatments including two-way modulo compressed, two-way data, one-way total ion current and one-way total mass spectrum. The results indicate that modulo compression and baseline correction methods significantly improved the performance of the classifiers which resulted in improved classification rates for FuRES, PLS-DA, and FOAM classifiers. By using two-way modulo compressed data sets, the average classification rates with FuRES, PLS-DA, and FOAM were 100±0%, 94.6±0.7%, and 96.1±0.6% for 100 bootstrapped Latin partitions of the Aroclor standards. The classifiers were validated by application to Aroclor samples extracted from soil with no parametric changes except that the calibration set of standards and validation set of soil samples were individually mean centered. The classification rates for the GC/MS modulo 35 compressed data obtained from the Aroclor soil samples with FOAM, FuRES, and PLS-DA were 100%, 96.4%, and 78.6%, respectively. Therefore, a chemometric pipeline for SPME-GC/MS data coupled with chemometric analysis was devised as a fast authentication method for different Aroclors in soil.

Authors+Show Affiliations

Center for Intelligent Chemical Instrumentation, Clippinger Laboratories, Department of Chemistry and Biochemistry, Ohio University, Athens, OH 45701-2979, United States.No affiliation info available

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

Language

eng

PubMed ID

24209371

Citation

Zhang, Mengliang, and Peter de B. Harrington. "Automated Pipeline for Classifying Aroclors in Soil By Gas Chromatography/mass Spectrometry Using Modulo Compressed Two-way Data Objects." Talanta, vol. 117, 2013, pp. 483-91.
Zhang M, Harrington Pde B. Automated pipeline for classifying Aroclors in soil by gas chromatography/mass spectrometry using modulo compressed two-way data objects. Talanta. 2013;117:483-91.
Zhang, M., & Harrington, P. d. e. . B. (2013). Automated pipeline for classifying Aroclors in soil by gas chromatography/mass spectrometry using modulo compressed two-way data objects. Talanta, 117, 483-91. https://doi.org/10.1016/j.talanta.2013.09.050
Zhang M, Harrington Pde B. Automated Pipeline for Classifying Aroclors in Soil By Gas Chromatography/mass Spectrometry Using Modulo Compressed Two-way Data Objects. Talanta. 2013 Dec 15;117:483-91. PubMed PMID: 24209371.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Automated pipeline for classifying Aroclors in soil by gas chromatography/mass spectrometry using modulo compressed two-way data objects. AU - Zhang,Mengliang, AU - Harrington,Peter de B, Y1 - 2013/10/07/ PY - 2013/08/14/received PY - 2013/09/26/revised PY - 2013/09/27/accepted PY - 2013/11/12/entrez PY - 2013/11/12/pubmed PY - 2014/6/16/medline KW - FOAM KW - FuRES KW - Fuzzy optimal associative memory KW - Fuzzy rule-building expert system KW - GC/ECD KW - GC/MS KW - K-nearest neighbor KW - KNN KW - Modulo compression KW - PCA KW - PCBs KW - PDMS KW - PDR KW - PLS-DA KW - PNN KW - Partial least-squares discriminant analysis KW - Polychlorinated biphenyl KW - RRFs KW - SIMCA KW - SPME KW - SVD KW - Solid phase microextraction KW - TIC KW - TMS KW - compressed mass spectra using a modulo divisor of 35 KW - fuzzy optimal associative memory KW - fuzzy rule-building expert system KW - gas chromatography with electron capture detectors KW - gas chromatography/mass spectrometry KW - mod-35 KW - partial least-squares discriminant analysis KW - polychlorinated biphenyls KW - polydimethylsiloxane KW - principal component analysis. KW - probabilistic neural network KW - projected difference resolution KW - relative response factors KW - singular value decomposition KW - soft independent modeling by class analogy KW - solid phase microextraction KW - total ion current KW - total mass spectrum SP - 483 EP - 91 JF - Talanta JO - Talanta VL - 117 N2 - Seven polychlorinated biphenyls (PCBs) commercial mixtures, Aroclor 1016, 1221, 1232, 1242, 1248, 1254, and 1260, were analyzed by gas chromatography/mass spectrometry (GC/MS) combined with solid phase microextraction (SPME). Three pattern recognition methods: a fuzzy rule-building expert system (FuRES), partial least-squares discriminant analysis (PLS-DA), and a fuzzy optimal associative memory (FOAM) were used to build classification models. Modulo compression was introduced for data preprocessing to extract the characteristic features and compress the data size. Baseline correction and data normalization were also applied prior to data processing. Four GC/MS data set configurations were constructed and used to evaluate the classifiers and data pretreatments including two-way modulo compressed, two-way data, one-way total ion current and one-way total mass spectrum. The results indicate that modulo compression and baseline correction methods significantly improved the performance of the classifiers which resulted in improved classification rates for FuRES, PLS-DA, and FOAM classifiers. By using two-way modulo compressed data sets, the average classification rates with FuRES, PLS-DA, and FOAM were 100±0%, 94.6±0.7%, and 96.1±0.6% for 100 bootstrapped Latin partitions of the Aroclor standards. The classifiers were validated by application to Aroclor samples extracted from soil with no parametric changes except that the calibration set of standards and validation set of soil samples were individually mean centered. The classification rates for the GC/MS modulo 35 compressed data obtained from the Aroclor soil samples with FOAM, FuRES, and PLS-DA were 100%, 96.4%, and 78.6%, respectively. Therefore, a chemometric pipeline for SPME-GC/MS data coupled with chemometric analysis was devised as a fast authentication method for different Aroclors in soil. SN - 1873-3573 UR - https://www.unboundmedicine.com/medline/citation/24209371/Automated_pipeline_for_classifying_Aroclors_in_soil_by_gas_chromatography/mass_spectrometry_using_modulo_compressed_two_way_data_objects_ DB - PRIME DP - Unbound Medicine ER -