Abstract
[Purpose] The study on functional components such as dietary fiber, polyphenols, and carotenoids has been progressing. Foods containing these can be notified as foods with functional claims. When submitting a notification, it is necessary to confirm that the food contains an effective amount of these ingredients. In such cases, quantitative analysis is carried out using analytical instruments such as liquid chromatograph (HPLC). However, in the case of multiple components, it is difficult to separate and quantify each component, and it takes time to search for analytical conditions that can separate components from each other. In this study, the analytical conditions of HPLC were automatically optimized by AI algorithm. The validity of the analytical conditions was confirmed in real samples.
[Method] 15 mixtures of catechins, theaflavins, and gallic acid were selected as samples.The analysis condition was automatically optimized by the analysis software "LabSolutions MD" equipped with the AI algorithm. Green and black tea leaves were analyzed using the analytical conditions.
[Results] Initial analysis identified multiple poorly separated peaks. However, by automatic optimization of the analytical conditions by AI, it was possible to obtain the conditions under which 15 components could be separated. In addition, the analysis condition was applied to the analysis of tea leaves, and the scientific consideration for tea leaves could be carried out from the analysis result. This method does not depend on the knowledge and experience of the analyst and can easily determine the analysis conditions. In addition, it was suggested to contribute to the promotion of research on functional components.
