title: Authentication of organically and conventionally grown basils by gas chromatography/mass spectrometry chemical profiles.
abstract: Basil plants cultivated by organic and conventional farming practices were accurately classified by pattern recognition of gas chromatography/mass spectrometry (GC/MS) data. A novel extraction procedure was devised to extract characteristic compounds from ground basil powders. Two in-house fuzzy classifiers, i.e., the fuzzy rule-building expert system (FuRES) and the fuzzy optimal associative memory (FOAM) for the first time, were used to build classification models. Two crisp classifiers, i.e., soft independent modeling by class analogy (SIMCA) and the partial least-squares discriminant analysis (PLS-DA), were used as control methods. Prior to data processing, baseline correction and retention time alignment were performed. Classifiers were built with the two-way data sets, the total ion chromatogram representation of data sets, and the total mass spectrum representation of data sets, separately. Bootstrapped Latin partition (BLP) was used as an unbiased evaluation of the classifiers. By using two-way data sets, average classification rates with FuRES, FOAM, SIMCA, and PLS-DA were 100 ± 0%, 94.4 ± 0.4%, 93.3 ± 0.4%, and 100 ± 0%, respectively, for 100 independent evaluations. The established classifiers were used to classify a new validation set collected 2.5 months later with no parametric changes except that the training set and validation set were individually mean-centered. For the new two-way validation set, classification rates with FuRES, FOAM, SIMCA, and PLS-DA were 100%, 93%, 97%, and 100%, respectively. Thereby, the GC/MS analysis was demonstrated as a viable approach for organic basil authentication. It is the first time that a FOAM has been applied to classification. A novel baseline correction method was used also for the first time. The FuRES and the FOAM are demonstrated as powerful tools for modeling and classifying GC/MS data of complex samples, and the data pretreatments are demonstrated to be useful to improve the performance of classifiers.
authors: Center for Intelligent Chemical Instrumentation, Clippinger Laboratories, Department of Chemistry and Biochemistry, Ohio University, Athens, Ohio 45701-2979, United States.
authors: Zhengfang
authors: Z
authors: Wang
authors: Pei
authors: P
authors: Chen
authors: Liangli
authors: L
authors: Yu
authors: Peter de B
authors: Pde B
authors: Harrington
mesh_terms: D008401:Gas Chromatography-Mass Spectrometry; D008962:Models, Theoretical; D018649:Ocimum basilicum / Q000737:chemistry* / Q000254:growth & development*; D058871:Organic Agriculture; D011786:Quality Control
references: Anal Bioanal Chem. 2009 Feb;393(3):809-33
references: 18931996
references: Anal Chem. 2012 Apr 17;84(8):3628-34
references: 22414002
references: Clin Chem. 2003 Jul;49(7):1041-4
references: 12816898
references: Anal Bioanal Chem. 2013 Nov;405(28):9219-34
references: 24085188
references: Anal Chim Acta. 2007 Sep 19;599(2):219-31
references: 17870284
references: Anal Chem. 2011 Oct 1;83(19):7464-71
references: 21823628
references: J Agric Food Chem. 2006 Mar 8;54(5):1822-8
references: 16506839
references: Parasitol Res. 2007 Jul;101(2):443-52
references: 17342533
references: Anal Chem. 2007 Feb 15;79(4):1485-91
references: 17297947
references: Forensic Sci Int. 2012 Jul 10;220(1-3):210-8
references: 22472078
references: Clin Exp Pharmacol Physiol. 2005 Oct;32(10):811-6
references: 16173941