Shimadzu Review Vol.81[3・4](2024)
Realizing Health for People and the Planet through Food and Sustainable Food and Agriculture Industries

SPECIALLY COLLECTED PAPERS

Automatic Optimization of Gradient Conditions by AI Algorithm
-Application to LC Method for the Simultaneous Analysis of Functional Components in Foods-

by Shinichi FujisakiHidetoshi TeradaManami Kobayashi

Shimadzu Review 81[3・4] (2024)

Abstract

Optimizing gradient conditions for LC method development involves a cyclical process of preparing and implement-ing an extensive analysis schedule to screen separation conditions and then analyzing and interpreting the resulting  data. Preparing this analysis schedule multiple times is a very time-consuming task, and optimizing the separation con-ditions based on the results of these analyses requires knowledge and expertise in chromatography. Because gradient optimization requires human labor, there is a demand for tools that can reduce the labor-intensiveness of this work by automating the optimization workflow. We have developed LabSolutions MD, a software that assists analysis meth-od development and is equipped with an AI algorithm that automates gradient condition optimization. This article describes an example case study in which LabSolutions MD was used to automatically optimize gradient conditions to separate functional components of foods. These optimized conditions were then used to analyze and compare varieties and brands of tea leaves.


Solutions Center of Excellence, Analytical & Measuring Instruments Division, Shimadzu Corporation, Kyoto, Japan

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