Machine learning prediction of copper ion interference with mercury ion fluorescence signals in food heavy metal detection
Objective:To construct an artificial intelligence prediction model to predict the selectivity of fluorescent probes for Hg2+in a complex food testing environment in the presence of Cu2+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 Hg2+in the presence of Cu2+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 Hg2+under Cu2+interference.Conclusion:The model is improved for the design of Hg2+fluorescent molecular probes by selective prediction,which makes the design of Hg2+fluorescent probes more efficient and reliable.
mercury ion detectionfluorescent molecular probesprobe selectivitymachine learningcheminformatics
国家科技期刊平台
NSTL
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