In the field of authenticating herbal plant materials, conventional methods mostly focus on monitoring specific compounds as markers. Recently, there has been a growing trend toward utilizing chemical fingerprinting of the whole system profile; this approach screens as many compounds as possible and minimizes the room for adulteration with synthetic or natural chemical compounds or substitution with other plant species.
One of the challenges in this area arises from the inherent complexity of natural productsâ€™ chemical fingerprints, which renders the use of fingerprint graphs for quality control purposes difficult and/or impossible, specifically in the case of large numbers of samples. One approach has been exploiting chemometric pattern recognition techniques, which are effective tools for reducing the complexity of such data sets.
Supra combines powerful analytical platforms such as GC-MS, ICP-MS with the state-of-the-art chemometric tools to extract the desired information and produce class predictive blueprints. These blueprints provide quality control sector with powerful tools to project new samples and identify the fingerprints with unexpected qualities as outliers in an unbiased fashion, which need further attention.