Automated Gradient Optimization based on AI Algorithm for LC Method Development ~Simultaneous Analysis for Functional Components in Foods~

User Benefits

－The AI algorithm of LabSolutions MD can automatically optimize gradient conditions to greatly reduce labor of LC method development.
－Anyone can optimize gradient conditions, regardless of their experience in chromatography.
－Comparison and evaluation of functional components, such as catechins and theaflavins, in tea leaves can be performed among different tea species.

Introduction

In the typical LC method development, the process begins with “preparation” which includes mobile phase preparation, column installation, and creation of analysis schedules, then analysis is started. After that, the acquired data is analyzed and “preparation” for the subsequent analysis is carried out, followed by starting the next analysis again. The method development progresses by repeating these processes, but in addition to the significant time required to repeatedly create analysis schedules, expertise in chromatography is necessary to explore optimal conditions based on data analysis. In other words, typical method development requires “human intervention”. Therefore, eliminating human involvement and automating such method development processes would be desirable to improve labor efficiency. This article employs a fifteen-standard mixed solution of catechins, theaflavins, and gallic acid, which are functional components in tea leaves. The AI algorithm (See Technical Report C190-E309 equipped with LabSolutions MD, a dedicated software for supporting method development, was utilized for the automatic optimization of gradient conditions. Furthermore, the optimized method was applied to several tea leaves and comparisons were made among different tea species.

June 25, 2024 GMT