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

Abstract

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 classification 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.