Biogenic amines(BAs)are important biomarkers for monitoring food quality and assisting in the diagnosis of disease.Facial,portable,accurate and high-throughput BAs detection is still challenging by the specific sensor...Biogenic amines(BAs)are important biomarkers for monitoring food quality and assisting in the diagnosis of disease.Facial,portable,accurate and high-throughput BAs detection is still challenging by the specific sensor compounds development or the complicated instrument operation.Deep learning(DL)algorithms are blooming for their superiority on the nonlinear and multidimensional data analysis,which endow the great advantage for the artificial intelligence assisted large sample analysis of the environmental or daily health monitoring.In this work,we developed a deep learning-assisted visualized fluorometric array-based sensing method.Two commercial fluorescent dyes were selected and combined into sensor arrays.Variation in the alkalinity of BAs causes significant and distinct fluorescence changes of the dyes.In conjunction with pattern recognition by the pretrained CNN models,the sensor array clearly differentiates seven BAs with 99.29%prediction accuracy and allows rapid single and multi-component quantification with a volume fraction range from 200 cm^(3)/m^(3)to 2500 cm^(3)/m^(3).This method also provides a new way for meat freshness monitoring.We envision that this novel analytical method for BAs can be used as an alternative and promising tool for the detection of a wider variety of analytes.展开更多
Underwater superoleophobic-oleophilic chips were developed to achieve oil extracting from aqueous solution and oil-droplet con-densing to preset microwell.The double-hierarchical(extracting-condensing)enrichment strat...Underwater superoleophobic-oleophilic chips were developed to achieve oil extracting from aqueous solution and oil-droplet con-densing to preset microwell.The double-hierarchical(extracting-condensing)enrichment strategy drastically improves the sensitivi-ty for organic contaminants or components,whose signal amplification approaches 459.7 times that of primary solution and 25.9 times that of single condensing enrichment strategy.Low to femtomolar limit of detection(2.6×10^(-15)mol/L)sensitivity and 6 vari-ous aflatoxins or mildewed foods identification demonstrate the significance and promotion for environment monitoring,water pu-rification,and so on.展开更多
基金This work is supported by the National Natural Science Foundation of China(Nos.21874056 and 52003103)the National Key R&D Program of China(No.2016YFC1100502)the Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Appications,Jinan University.
文摘Biogenic amines(BAs)are important biomarkers for monitoring food quality and assisting in the diagnosis of disease.Facial,portable,accurate and high-throughput BAs detection is still challenging by the specific sensor compounds development or the complicated instrument operation.Deep learning(DL)algorithms are blooming for their superiority on the nonlinear and multidimensional data analysis,which endow the great advantage for the artificial intelligence assisted large sample analysis of the environmental or daily health monitoring.In this work,we developed a deep learning-assisted visualized fluorometric array-based sensing method.Two commercial fluorescent dyes were selected and combined into sensor arrays.Variation in the alkalinity of BAs causes significant and distinct fluorescence changes of the dyes.In conjunction with pattern recognition by the pretrained CNN models,the sensor array clearly differentiates seven BAs with 99.29%prediction accuracy and allows rapid single and multi-component quantification with a volume fraction range from 200 cm^(3)/m^(3)to 2500 cm^(3)/m^(3).This method also provides a new way for meat freshness monitoring.We envision that this novel analytical method for BAs can be used as an alternative and promising tool for the detection of a wider variety of analytes.
基金the National Natural Science Foundation of China(Nos.22090050,22090052,22176180,21874121,51803194,41807201,21874056,52003103,21974128)the National Key R&D Program of China(Nos.2016YFC1100502,2018YFE0206900)+2 种基金the Characteristic Innovation Projects of Guangdong Province for University(No.2018GKTSCX004)the Key Projects in Basic and Applied Research of Jjiangmen(Grant No.[2019]256)Zhejiang Provincial Natural Science Foundation of China under Grant No.LY20B050002 andNo.LD21B050001.
文摘Underwater superoleophobic-oleophilic chips were developed to achieve oil extracting from aqueous solution and oil-droplet con-densing to preset microwell.The double-hierarchical(extracting-condensing)enrichment strategy drastically improves the sensitivi-ty for organic contaminants or components,whose signal amplification approaches 459.7 times that of primary solution and 25.9 times that of single condensing enrichment strategy.Low to femtomolar limit of detection(2.6×10^(-15)mol/L)sensitivity and 6 vari-ous aflatoxins or mildewed foods identification demonstrate the significance and promotion for environment monitoring,water pu-rification,and so on.
