Application of GC/Q-ToF Combined with Advanced Data Mining and Chemometric Tools in the Characterization and Quality Control of Bay Leaves.

Abstract

Correct identification of the true bay leaf (Laurus nobilis) and its substitutes is important not only for the quality control of the products, but also for the safety of the consumers. L.nobilis is often substituted or confused with other species, such as Cinnamomum tamala, Pimenta racemosa, Syzygium polyanthum, and Umbellularia californica. In the present study, the potential of gas chromatography combined with quadrupole time-of-flight mass spectrometry for the profiling of various bay leaf products was evaluated for the first time. Thirty-nine authenticated samples representing the true bay leaf and the four commonly substituted species were analyzed. An automatic feature extraction algorithm was applied for data mining and pretreatment in order to identify the most characteristic compounds representing different bay leaf groups. This set of data was employed to construct a sample class prediction model based on stepwise reduction of data dimensionality followed by principal component analysis and partial least squares discriminant analysis. The statistical model, with demonstrated excellent accuracies in recognition and prediction abilities, enabled the correct classification of commercial samples including complex mixtures and essential oils. In addition, in-house developed personal compound database and library with retention time locking offered the advantage of combining retention time matching with accurate mass matching, resulting in high confidence of compound identification for each bay leaf subgroup. At least three marker compounds were identified for each bay leaf species that could be used to discriminate among them.