机器学习预测食品重金属检测中铜离子对汞离子荧光信号的干扰OA北大核心CSTPCD
Machine learning prediction of copper ion interference with mercury ion fluorescence signals in food heavy metal detection
目的:构建一个人工智能预测模型,在存在Cu2+干扰的复杂食品检测环境下预测荧光探针对Hg2+的选择性.方法:采用荧光探针技术结合7种先进经典的机器学习模型,预测分析存在Cu2+干扰时探针对Hg2+的选择性,并比较各模型的预测效果,选择最优模型.结果:基于分子二维描述符(molecular 2D descriptors,Mo12D)和极端梯度提升算法成功建立了在交叉验证和测试集中准确度为0.786和0.810的高效模型,在Cu2+干扰下准确预判Hg2+的探针选择性.结论:该模型通过选择性预判对Hg2+荧光分子探针的设计进行改进,使Hg2+荧光探针的设计更加高效可靠.
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.
宋方亮;梁盈;董界;王雪洁;钱洁
中南林业科技大学食品科学与工程学院,湖南长沙 410004||水稻及副产物深加工国家工程研究中心分子营养分中心,湖南长沙 410004中南大学湘雅药学院,湖南长沙 410013
汞离子检测荧光分子探针探针选择性机器学习化学信息学
mercury ion detectionfluorescent molecular probesprobe selectivitymachine learningcheminformatics
《食品与机械》 2024 (005)
62-66,153 / 6
国家自然科学基金(编号:32372349);国家重点研发计划(编号:2022YFF1100203);湖南省科技创新人才项目(编号:2022RC3056)
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