|WANG, ZHENGFANG - Ohio University|
|YU, LIANGLI - University Of Maryland|
|HARRINGTON, PETER DE - Ohio University|
Submitted to: Analytical Chemistry
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/11/2013
Publication Date: 2/17/2013
Citation: Wang, Z., Chen, P., Yu, L., Harrington, P.B. 2013. Authentication of organically and conventionally grown basils by gas chromatograpy/mass spectrometry chemical profiles. Analytical Chemistry. 85:2945-2953.
Interpretive Summary: Differentiation of organic and conventional grown foods are important both to the industries and consumers. This study described a new method that uses gas chromatography/mass spectrometry (GC/MS) and chemometric method to differentiate organic and conventional grown basils. The results clearly showed there were chemical differences between organic and conventional grown basils
Technical Abstract: Basil plants cultivated by organic and conventional farming practices were differentiated using gas chromatography/mass spectrometry (GC/MS) and chemometric methods. The two-way GC/MS data sets were baseline-corrected and retention time-aligned prior to data processing. Two self-devised fuzzy classifiers, i.e., the fuzzy rule-building expert system (FuRES) and the fuzzy optimal associative memory (FOAM), were used to build classification models. Two crisp classifiers, i.e., the 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 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 × 3 bootstrapped Latin partitions. 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%, 83%, 97%, and 100%, respectively. In addition, feature-based classification was evaluated. Six features important to differentiate organic and conventional basils were selected by the average FuRES discriminant. Economic classifiers e-FuRES, e-FOAM, e-SIMCA, and e-PLS-DA were these 6 data points. By using features extracted from initial two-way classifier-building data sets, average classification rates with e-FuRES, e-FOAM, e-SIMCA, and e-PLS-DA were 100 ± 0%, 95.3 ± 0.5%, 91.8 ± 0.2%, and 100 ± 0%, respectively, for 100 × 3 bootstrapped Latin partitions. For the long-term two-way validation set, classification rates with e-FuRES, e-FOAM, e-SIMCA, and e-PLS-DA were 100%, 93%, 93%, and 100%, respectively. Thereby, the GC/MS analysis is proven to be an effective approach for organic basil authentication, the FuRES and FOAM methods are demonstrated as powerful tools in processing two-way GC/MS data sets, and the data pretreatments are demonstrated to be useful to improve the performance of classifiers.