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