|WANG, MEI - University Of Mississippi|
|AVULA, BHARATHI - University Of Mississippi|
|WANG, YAN-HONG - University Of Mississippi|
|ZHAO, JIANPING - University Of Mississippi|
|AVONTO, CRISTINA - University Of Mississippi|
|PARCHER, JON - University Of Mississippi|
|RAMAN, VIJAYASANKAR - University Of Mississippi|
|ZWEIGENBAUM, JERRY - University Of Mississippi|
|WYLIE, PHILIP - University Of Mississippi|
|KHAN, IKHLAS - University Of Mississippi|
Submitted to: Journal of Food Chemistry
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/21/2013
Publication Date: 1/1/2014
Citation: Wang, M., Avula, B., Wang, Y., Zhao, J., Avonto, C., Parcher, J., Raman, V., Zweigenbaum, J., Wylie, P., Khan, I. 2014. An integrated approach utilizing chemometrics and GC/MS for classification of chamomile flowers, essential oils and commerical products. Journal of Food Chemistry. 152:391-398.
Interpretive Summary: This study involves an in-depth chemical investigation of various types of chamomile used in herbal teas, cosmetics and dietary supplements. A collection of authenticated plant samples were used to construct a sample class prediction (SCP) model with chemometric methods. This model was used to predict, classify and differentiate commercial products and essential oils purported to contain chamomile. The work reported in the ms. ascertained and addressed the problems of botanical classification and differentiation of chamomile used in commercial products and dietary supplements.
Technical Abstract: As part of an ongoing research program on authentication, safety and biological evaluation of phytochemicals and dietary supplements, an in-depth chemical investigation of different types of chamomile was performed. A collection of chamomile samples including authenticated plants, commercial products and essential oils was analyzed by GC/MS. Twenty-seven authenticated plant samples representing three types of chamomile, viz. German chamomile, Roman chamomile and Juhua were analyzed. The dataset was employed to construct a sample class prediction (SCP) model based on stepwise reduction of data dimensionality followed by principle component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). The model was cross-validated with samples including authenticated plants and commercial products. The model demonstrated 100.0% accuracy for both recognition and prediction abilities. In addition, 35 commercial products and 11 essential oils purported to contain chamomile were subsequently predicted by the validated PLS-DA model. Furthermore, tentative identification of the marker compounds correlated with different types of chamomile was explored.