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Feature selection of gas chromatography/mass spectrometry chemical profiles of basil plants using a bootstrapped fuzzy rule-building expert system.
Anal Bioanal Chem. 2013 Nov; 405(28):9219-34.AB

Abstract

A bootstrapped fuzzy rule-building expert system (FuRES) and a bootstrapped t-statistical weight feature selection method were individually used to select informative features from gas chromatography/mass spectrometry (GC/MS) chemical profiles of basil plants cultivated by organic and conventional farming practices. Feature subsets were selected from two-way GC/MS data objects, total ion chromatograms, and total mass spectra, separately. Four economic classifiers based on the bootstrapped FuRES approach, i.e., fuzzy optimal associative memory (e-FOAM), e-FuRES, partial least-squares-discriminant analysis (e-PLS-DA), and soft independent modeling by class analogy (e-SIMCA), and four economic classifiers based on the bootstrapped t-weight approach, i.e., e-PLS-DA-t, e-FOAM-t, e-FuRES-t, and e-SIMCA-t, were constructed thereafter to be compared with full-size classifiers obtained from the entire GC/MS data objects (i.e., FOAM, FuRES, PLS-DA, and SIMCA). By using three features selected from two-way data objects, the average classification rates with e-FOAM, e-FuRES, e-PLS-DA, and e-SIMCA were 95.3 ± 0.5%, 100%, 100%, and 91.8 ± 0.2%, respectively. The established economic classifiers were used to classify a new validation set collected 2.5 months later with no parametric change to experimental procedure. Classification rates with e-FOAM, e-FuRES, e-PLS-DA, and e-SIMCA were 96.7%, 100%, 100%, and 96.7%, respectively. Characteristic components in basil extracts corresponding to highest-ranked useful features were putatively identified. The feature subset may prove valuable as a rapid approach for organic basil authentication.

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Publisher Full Text (DOI)

Authors+Show Affiliations

Wang Z
Center for Intelligent Chemical Instrumentation, Clippinger Laboratories, Department of Chemistry and Biochemistry, Ohio University, Athens, OH, 45701-2979, USA.
Harrington Pde B
No affiliation info available

MeSH

Food, OrganicGas Chromatography-Mass SpectrometryOcimum basilicumPlant ExtractsPlant Leaves

Pub Type(s)

Evaluation Study
Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

24085188

Citation

Wang, Zhengfang, and Peter de B. Harrington. "Feature Selection of Gas Chromatography/mass Spectrometry Chemical Profiles of Basil Plants Using a Bootstrapped Fuzzy Rule-building Expert System." Analytical and Bioanalytical Chemistry, vol. 405, no. 28, 2013, pp. 9219-34.
Wang Z, Harrington Pde B. Feature selection of gas chromatography/mass spectrometry chemical profiles of basil plants using a bootstrapped fuzzy rule-building expert system. Anal Bioanal Chem. 2013;405(28):9219-34.
Wang, Z., & Harrington, P. d. e. . B. (2013). Feature selection of gas chromatography/mass spectrometry chemical profiles of basil plants using a bootstrapped fuzzy rule-building expert system. Analytical and Bioanalytical Chemistry, 405(28), 9219-34. https://doi.org/10.1007/s00216-013-7327-x
Wang Z, Harrington Pde B. Feature Selection of Gas Chromatography/mass Spectrometry Chemical Profiles of Basil Plants Using a Bootstrapped Fuzzy Rule-building Expert System. Anal Bioanal Chem. 2013;405(28):9219-34. PubMed PMID: 24085188.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Feature selection of gas chromatography/mass spectrometry chemical profiles of basil plants using a bootstrapped fuzzy rule-building expert system. AU - Wang,Zhengfang, AU - Harrington,Peter de B, Y1 - 2013/10/02/ PY - 2013/07/24/received PY - 2013/08/28/accepted PY - 2013/10/3/entrez PY - 2013/10/3/pubmed PY - 2014/5/30/medline SP - 9219 EP - 34 JF - Analytical and bioanalytical chemistry JO - Anal Bioanal Chem VL - 405 IS - 28 N2 - A bootstrapped fuzzy rule-building expert system (FuRES) and a bootstrapped t-statistical weight feature selection method were individually used to select informative features from gas chromatography/mass spectrometry (GC/MS) chemical profiles of basil plants cultivated by organic and conventional farming practices. Feature subsets were selected from two-way GC/MS data objects, total ion chromatograms, and total mass spectra, separately. Four economic classifiers based on the bootstrapped FuRES approach, i.e., fuzzy optimal associative memory (e-FOAM), e-FuRES, partial least-squares-discriminant analysis (e-PLS-DA), and soft independent modeling by class analogy (e-SIMCA), and four economic classifiers based on the bootstrapped t-weight approach, i.e., e-PLS-DA-t, e-FOAM-t, e-FuRES-t, and e-SIMCA-t, were constructed thereafter to be compared with full-size classifiers obtained from the entire GC/MS data objects (i.e., FOAM, FuRES, PLS-DA, and SIMCA). By using three features selected from two-way data objects, the average classification rates with e-FOAM, e-FuRES, e-PLS-DA, and e-SIMCA were 95.3 ± 0.5%, 100%, 100%, and 91.8 ± 0.2%, respectively. The established economic classifiers were used to classify a new validation set collected 2.5 months later with no parametric change to experimental procedure. Classification rates with e-FOAM, e-FuRES, e-PLS-DA, and e-SIMCA were 96.7%, 100%, 100%, and 96.7%, respectively. Characteristic components in basil extracts corresponding to highest-ranked useful features were putatively identified. The feature subset may prove valuable as a rapid approach for organic basil authentication. SN - 1618-2650 UR - https://www.unboundmedicine.com/medline/citation/24085188/Feature_selection_of_gas_chromatography/mass_spectrometry_chemical_profiles_of_basil_plants_using_a_bootstrapped_fuzzy_rule_building_expert_system_ DB - PRIME DP - Unbound Medicine ER -