Nuts have received increased attention from the public in recent years as important sources of some essential elements, and information on the levels of elements in edible nuts is useful to consumers. Determination of...Nuts have received increased attention from the public in recent years as important sources of some essential elements, and information on the levels of elements in edible nuts is useful to consumers. Determination of the elemental distributions in nuts is not only necessary in evaluating the total dietary intake of the essential elements, but also useful in detecting heavy metal contamination in food. The aim of this study was to determine the mineral contents in edible nuts, and to assess the food safety of nuts in the Beijing market. Levels of Li, Cr, Mn, Co, Cu, Zn, As, Se, Rb, Sr, Mo, Cd, Cs, Ba, Pb, Th, and U in 11 types of edible nuts and seeds （macadamia nuts, lotus nuts, pistachios, sunflower seeds, pine nuts, almonds, walnuts, chestnuts, hazelnuts, cashews, and ginkgo nuts） as well as raisins were determined by inductively coupled plasma mass spectrometry （ICP-MS）. The accuracy of the method was validated using standard reference materials GBWlO014 （cabbage） and GBWlO016 （tea）. Our results provide useful information for evaluating the levels of trace elements in edible nuts in the Beijing market, will be helpful for improving food safety, and will aid in better protecting consumer interests.展开更多
Boron/nitrogen-co-doped carbon(BCN)nanosheets decorated with Fe_(2)O_(3) nanocrystals(Fe_(2)O_(3)–BCN)were cast on a glassy carbon electrode(GCE)and applied as an electrochemical sensor to effectively detect paraquat...Boron/nitrogen-co-doped carbon(BCN)nanosheets decorated with Fe_(2)O_(3) nanocrystals(Fe_(2)O_(3)–BCN)were cast on a glassy carbon electrode(GCE)and applied as an electrochemical sensor to effectively detect paraquat(PQ),a toxic herbicide,in aqueous environments.A linear experiment performed using square wave voltammetry(SWV)under optimized experimental conditions produced a decent linear relationship and a low detection limit(LOD)of 2.74 nmol/L(S/N=3).Repeatability,reproducibility,stability,and interference experiments confirmed that the Fe_(2)O_(3)–BCN/GCE system exhibited decent electrochemical sensing performance for PQ molecules.Notably,the designed sensor showed high selectivity and a decent linear relationship with PQ concentration in natural water samples.To the best of our knowledge,this is the first study on the preparation of Fe_(2)O_(3)–BCN nanosheets for PQ detection.The proposed sensor can be employed as an effective alternative tool for distinguishing and processing PQ.展开更多
Contamination events in water distribution networks(WDNs)can have a huge impact on water supply and public health;increasingly,online water quality sensors are deployed for real-time detection of contamination events....Contamination events in water distribution networks(WDNs)can have a huge impact on water supply and public health;increasingly,online water quality sensors are deployed for real-time detection of contamination events.Machine learning has been used to integrate multivariate time series water quality data at multiple stations for contamination detection;however,accurate extraction of spatial features in water quality signals remains challenging.This study proposed a contamination detection method based on generative adversarial networks(GANs).The GAN model was constructed to simultaneously consider the spatial correlation between sensor locations and temporal information of water quality indicators.The model consists of two networksda generator and a discriminatordthe outputs of which are used to measure the degree of abnormality of water quality data at each time step,referred to as the anomaly score.Bayesian sequential analysis is used to update the likelihood of event occurrence based on the anomaly scores.Alarms are then generated from the fusion of single-site and multi-site models.The proposed method was tested on a WDN for various contamination events with different characteristics.Results showed high detection performance by the proposed GAN method compared with the minimum volume ellipsoid benchmark method for various contamination amplitudes.Additionally,the GAN method achieved high accuracy for various contamination events with different amplitudes and numbers of anomalous water quality parameters,and water quality data from different sensor stations,highlighting its robustness and potential for practical application to real-time contamination events.展开更多
Ensuring food safety is pivotal for public health and the stability of global food supply chains.Traditional detection methods,such as culturebased assays,polymerase chain reaction(PCR),and mass spectrometry,have demo...Ensuring food safety is pivotal for public health and the stability of global food supply chains.Traditional detection methods,such as culturebased assays,polymerase chain reaction(PCR),and mass spectrometry,have demonstrated high sensitivity and specificity but are hindered by their high cost,operational complexity,and dependence on laboratory environment,making them unsuitable for rapid,on-site detection.DNAzyme-based biosensors offer a novel solution through their unique molecular recognition and catalytic properties,enabling sensitive,cost-effective,and portable detection.For instance,DNAzyme sensors targeting Escherichia coli have achieved detection limits as low as 103 CFU/mL in food matrices like meat and juice,while colorimetric DNAzyme platforms for lead ion(Pb2+)detection have demonstrated sensitivity down to 1 nmol/L,making them suitable for on-site monitoring.This review systematically explores the applications of DNAzyme technology in detecting food contaminants,including pathogens,mycotoxins,heavy metals,antibiotics,and pesticide residues.This highlights the advantages of DNAzyme technology in terms of rapid detection,enhanced sensitivity,and strong anti-interference capability while critically analyzing the challenges encountered in real-world applications.Furthermore,this review proposes key directions for advancing DNAzyme commercialization and interdisciplinary integration,providing a foundation for future developments in food safety diagnostics.展开更多
基金supported by the Chinese Ministry of Science and Technology(Grant No.2013BAK03B00 and 2014FY211000)
文摘Nuts have received increased attention from the public in recent years as important sources of some essential elements, and information on the levels of elements in edible nuts is useful to consumers. Determination of the elemental distributions in nuts is not only necessary in evaluating the total dietary intake of the essential elements, but also useful in detecting heavy metal contamination in food. The aim of this study was to determine the mineral contents in edible nuts, and to assess the food safety of nuts in the Beijing market. Levels of Li, Cr, Mn, Co, Cu, Zn, As, Se, Rb, Sr, Mo, Cd, Cs, Ba, Pb, Th, and U in 11 types of edible nuts and seeds （macadamia nuts, lotus nuts, pistachios, sunflower seeds, pine nuts, almonds, walnuts, chestnuts, hazelnuts, cashews, and ginkgo nuts） as well as raisins were determined by inductively coupled plasma mass spectrometry （ICP-MS）. The accuracy of the method was validated using standard reference materials GBWlO014 （cabbage） and GBWlO016 （tea）. Our results provide useful information for evaluating the levels of trace elements in edible nuts in the Beijing market, will be helpful for improving food safety, and will aid in better protecting consumer interests.
基金funded by the National Key Research and Development Program(No.2019YFC1804400)the National Natural Science Foundation of China(Nos.21974124,22004109,22076174)+3 种基金Guangdong Province Higher Vocational Colleges&Schools Pearl River Scholar Funded Scheme(2016)Guangdong Third Generation Semiconductor Engineering Technology Development Center(No.2020GCZX007)the Yunnan Provincial Science and Technology Bureau and the Double Top Joint Fund of Yunnan University(No.2019FY003025)the Double First Class University Plan(No.C176220100042)。
文摘Boron/nitrogen-co-doped carbon(BCN)nanosheets decorated with Fe_(2)O_(3) nanocrystals(Fe_(2)O_(3)–BCN)were cast on a glassy carbon electrode(GCE)and applied as an electrochemical sensor to effectively detect paraquat(PQ),a toxic herbicide,in aqueous environments.A linear experiment performed using square wave voltammetry(SWV)under optimized experimental conditions produced a decent linear relationship and a low detection limit(LOD)of 2.74 nmol/L(S/N=3).Repeatability,reproducibility,stability,and interference experiments confirmed that the Fe_(2)O_(3)–BCN/GCE system exhibited decent electrochemical sensing performance for PQ molecules.Notably,the designed sensor showed high selectivity and a decent linear relationship with PQ concentration in natural water samples.To the best of our knowledge,this is the first study on the preparation of Fe_(2)O_(3)–BCN nanosheets for PQ detection.The proposed sensor can be employed as an effective alternative tool for distinguishing and processing PQ.
基金supported by the National Natural Science Foundation of China(52122901,52079016)Fundamental Research Funds for the Central Universities(DUT21GJ203+1 种基金the UK Royal Society(Ref:IF160108 and IEC\NSFC\170249)sponsored by the China Scholarship Council(202106060094).
文摘Contamination events in water distribution networks(WDNs)can have a huge impact on water supply and public health;increasingly,online water quality sensors are deployed for real-time detection of contamination events.Machine learning has been used to integrate multivariate time series water quality data at multiple stations for contamination detection;however,accurate extraction of spatial features in water quality signals remains challenging.This study proposed a contamination detection method based on generative adversarial networks(GANs).The GAN model was constructed to simultaneously consider the spatial correlation between sensor locations and temporal information of water quality indicators.The model consists of two networksda generator and a discriminatordthe outputs of which are used to measure the degree of abnormality of water quality data at each time step,referred to as the anomaly score.Bayesian sequential analysis is used to update the likelihood of event occurrence based on the anomaly scores.Alarms are then generated from the fusion of single-site and multi-site models.The proposed method was tested on a WDN for various contamination events with different characteristics.Results showed high detection performance by the proposed GAN method compared with the minimum volume ellipsoid benchmark method for various contamination amplitudes.Additionally,the GAN method achieved high accuracy for various contamination events with different amplitudes and numbers of anomalous water quality parameters,and water quality data from different sensor stations,highlighting its robustness and potential for practical application to real-time contamination events.
基金supported by a grant from the Ministry of Science and Higher Education of the Russian Federation for large scientific projects in priority areas of scientific and technological development(Project No.075-15-2024-483).
文摘Ensuring food safety is pivotal for public health and the stability of global food supply chains.Traditional detection methods,such as culturebased assays,polymerase chain reaction(PCR),and mass spectrometry,have demonstrated high sensitivity and specificity but are hindered by their high cost,operational complexity,and dependence on laboratory environment,making them unsuitable for rapid,on-site detection.DNAzyme-based biosensors offer a novel solution through their unique molecular recognition and catalytic properties,enabling sensitive,cost-effective,and portable detection.For instance,DNAzyme sensors targeting Escherichia coli have achieved detection limits as low as 103 CFU/mL in food matrices like meat and juice,while colorimetric DNAzyme platforms for lead ion(Pb2+)detection have demonstrated sensitivity down to 1 nmol/L,making them suitable for on-site monitoring.This review systematically explores the applications of DNAzyme technology in detecting food contaminants,including pathogens,mycotoxins,heavy metals,antibiotics,and pesticide residues.This highlights the advantages of DNAzyme technology in terms of rapid detection,enhanced sensitivity,and strong anti-interference capability while critically analyzing the challenges encountered in real-world applications.Furthermore,this review proposes key directions for advancing DNAzyme commercialization and interdisciplinary integration,providing a foundation for future developments in food safety diagnostics.
