Machine learning prediction of copper ion interference with mercury ion fluorescence signals in food heavy metal detection

SONG Fangliang;LIANG Ying;DONG Jie;WANG Xuejie;QIAN Jie;College of Food Science and Engineering,Central South University of Forestry and Technology;Molecular Nutrition Branch,National Engineering Research Center of Rice and By-Product Deep Processing;Xiangya School of Pharmaceutical Sciences,Central South University;

mercury ion detection;;fluorescent molecular probes;;probe selectivity;;machine learning;;cheminformatics

Objective: To construct an artificial intelligence prediction model to predict the selectivity of fluorescent probes for Hg~(2+) in a complex food testing environment in the presence of Cu~(2+) interference. Methods: Fluorescent probe technology combined with seven advanced classical machine learning models was used to predict and analyze the selectivity of the probe for Hg~(2+) in the presence of Cu~(2+) interference, and to compare the prediction effect of each model and select the optimal model. Results: Efficient models with accuracies of 0.786 and 0.810 in the cross-validation and test sets were successfully established based on Molecular 2D Descriptors(Mol2D) and extreme gradient boosting algorithms to accurately predict the probe selectivity of Hg~(2+) under Cu~(2+) interference. Conclusion: The model is improved for the design of Hg~(2+) fluorescent molecular probes by selective prediction, which makes the design of Hg~(2+) fluorescent probes more efficient and reliable.