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ARS Home » Southeast Area » Oxford, Mississippi » Natural Products Utilization Research » Research » Publications at this Location » Publication #395310

Research Project: Biobased Pesticide Discovery and Product Optimization and Enhancement from Medicinal and Aromatic Crops

Location: Natural Products Utilization Research

Title: Chemical authentication and speciation of Salvia botanicals: an investigation utilizing GC/Q-ToF and chemometrics

Author
item LEE, JOSEPH - University Of Mississippi
item Wang, Mei
item JIANPING, ZHAO - University Of Mississippi
item AVULA, BHARATHI - University Of Mississippi
item CHITTIBOYINAA, AMAR - University Of Mississippi
item KHAN, IKHLAS - University Of Mississippi

Submitted to: Foods
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/13/2022
Publication Date: 7/19/2022
Citation: Lee, J., Wang, M., Jianping, Z., Avula, B., Chittiboyinaa, A.G., Khan, I.A. 2022. Chemical authentication and speciation of Salvia botanicals: an investigation utilizing GC/Q-ToF and chemometrics. Foods. https://doi.org/10.3390/foods11142132.
DOI: https://doi.org/10.3390/foods11142132

Interpretive Summary: The Salvia genus contains many members which have been used as both a medicinal and culinary herb. Since different Salvia species are purported to possess various physiological effects due to their chemical composition, correct identification of botanical material is important. Raw plant material can be identified based upon morphological features; however, the identification of processed botanical material can present a challenge. In order to address this challenge, five commonly used Salvia species (S. apiana, S. divinorum, S. mellifera, S. miltiorrhiza, and S. officinalis) were analyzed using a GC/Q-ToF technique in order to establish a chemical “fingerprint” for each species. With this data, a principal component analysis was completed and a sample class prediction model was constructed. In addition, a personal compound database and library (PCDL) containing marker and characteristic compounds with the high-resolution mass spectra data from each species was constructed to aid in species identification. By combining these techniques, this high throughput method can be utilized for species identification of Salvia-based finished products. Overall, the implementation of this method has the potential to enhance the safety and quality of Salvia botanical products for consumers.

Technical Abstract: Members of the genus Salvia have been used as a culinary herb and prized for their purported medicinal attributes. Since physiological effects can vary widely between species of Salvia, it is of great importance to accurately identify botanical material to ensure safety for consumers. In the present study, an in-depth chemical investigation was performed utilizing GC/Q-ToF combined with chemometrics. Twenty-four authentic plant samples representing five commonly used Salvia species, viz. S. apiana, S. divinorum, S. mellifera, S. miltiorrhiza, and S. officinalis, were analyzed using a GC/Q-ToF technique. The high-resolution spectral data were employed to construct a sample class prediction (SCP) model based on stepwise reduction of data dimensionality followed by principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA). This model demonstrated 100% accuracy for both prediction and recognition abilities. In addition, marker compounds present in each species were identified. Further, to reduce the time required and increase the confidence level for compound identification and classification of different Salvia species, a personal compound database and library (PCDL) containing marker and characteristic compounds with the high-resolution mass spectra data from each species was constructed. By combining GC/Q-ToF, chemometrics, and PCDL, unambiguous identification of Salvia botanicals was achieved accurately and efficiently. This high throughput method can be utilized for species specificity and to probe the overall quality of various Salvia-based finished products.