Simultaneous qualitative and quantitative analysis of flavonols in Kaempferia galangal L. and honey by machine learning-based fluorescence sensor array

Multiple flavonols with similar structures or chemical properties are often present in complex samples, which challenges real sample detection. A two-element based array (Al3+-CD@Cu and Mg2+-CD@Cu) was constructed for simultaneous identification of multiple flavonols. Flavonols obtained significant fluorescence emission enhancement after complexation with metal ions (Al3+ and Mg2+) and wrapping by cyclodextrin (CD) on the CD@Cu surface. This sensor array successfully classified multiple flavonols in the samples using the SVM clas-sification algorithm. However, a critical problem of insufficient quantification capability exists in current sensor array research. The sensor array achieved the quantitative analysis of kaempferol, quercetin, and myricetin by leveraging machine learning regression algorithm.

Automated summary

A two-element based array (Al3+-CD@Cu and Mg2+-CD@Cu) was constructed for simultaneous identification of multiple flavonols by significant fluorescence emission enhancement. The sensor array successfully classified multiple flavonols in the samples using the SVM classification algorithm.