Research process in application of fluorescent sensor for pesticide detection

The survival of human beings is inseparable from the development of agriculture.The use of pesticides is still an important means to ensure agricultural production,economic benefits and the national economy.With the abuse of pesticides,some pesticides are difficult to degrade after entering the natural world,which leads to many serious problems through the transmission and enrichment of food chains.Pesticide residue detection is a long-term challenge in the field of environment and food safety detection.In recent years,a large number of studies have been carried out on the detection of pesticide residues based on fluorescence sensing methods.This paper focuses on the application of fluorescence sensors in environmental samples or food,and analyzes the advantages and problems of existing fluorescence sensors in design,construction and data processing.Finally,the possible improvement directions in the future are discussed to promote its potential commercial value application research.

SERS devices with "hedgehog-like" nanosphere arrays for detection of trace pesticides

The development of surface-enhanced Raman scattering(SERS)devices for detection of trace pesticides has attracted more and more attention.In this work,a large-area self-assembly ap-proach assisted with reactive ion etching(RIE)is proposed for preparing SERS devices consisting of Ag-covered"hedgehog-like"nanosphere arrays(Ag/HLNAs).Such a SERS device has an enhancement factor of 2.79×107,a limit of detection(LOD)up to 10-12 M for Rhodamine 6G(R6G)analytes,and a relative standard deviation(RSD)smaller than 10%,demonstrating high uniformity.Besides,for pesticide detections,the device achieves an LOD of 10-s M for thiram molecules.It indicates that the proposed SERS device has a promising opportunity in detecting toxic organic pesticides.

A Device for Rapid Detection of Dithiocarbamate Pesticide Residues in Fruits

Based on the chemical properties of dithiocarbamate pesticides,a device for rapid detection was developed in the paper,and the experimental conditions were optimized. Dithiocarbamate residues in fruits were successfully detected using molecular absorption spectro-photometry,and the recovery rate was over 80%.The rapid detection method was simple to operate with low cost,and was conducive to application in basic level and enterprise laboratories.

Highly selective NIR fluorescent probe for acetylcholinesterase and its application in pesticide residues detection

A series of near-infrared(NIR)fluorescent substrates(NDRO-1∼8)derived from fluorophore NDRH with different volumes of ester bond as the recognition group were designed and synthesized for the detection of acetylcholinesterase(AChE),among which NDRO-1 with the smallest acetate group displayed the highest activity toward AChE.The detection limit of NDRO-1 for sensing AChE was 0.32μg/mL,and K_(m )was 6.40μmol/L,indicating ultra-sensitivity and good affinity of NDRO-1 toward AChE.NDRO-1 was used to detect the inhibitory of four kinds of pesticides including methamidophos,dichlorvos,and the detection limit was lower than 50μg/L,which was further used in pesticide residues detection.

Immobilization of acetylcholinesterase on functionalized SBA-15 mesoporous molecular sieve for detection of organophosphorus and carbamate pesticide

The immobilization of acetylcholinesterase （ACHE） on amino functionalized SBA-15 mesoporous sieves （NH2-SBA-15） was explored for detection of organophosphorus and carhamate pesticides. Several methods have been studied and the ＂adsorption-crosslinking＂ is found to be the best one. Firstly, AChE and bovine serum albumin （BSA） were adsorbed in turn on the surface of NH2-SBA-15 by electrostatic adsorption, and then glutaraldehyde was added for cross-linking the amino group of AChE. The NH2-SBA- 15 showed around 95% immobilization efficiency and the specific activity of immobilized AChE （ACHE- NH2-SBA-15） reached 130% relative to free AChE under optimal immobilization conditions. It was found AChE-NH2-SBA-15 retained 94.0% and 82.8% of its initial activity after 60days at 4℃ and 25℃, respectively. It remained 55.0% of its initial activity after 6 times recycling. During the detection of pesticide, the AChE-NH2-SBA-15 showed wider linear range and much lower limit of detection compared with free ACHE. The current method showed good recovery （93.8%-109.3%） and low RSD （〈5%） for both standard solution and real vegetable samples of Carbaryl and Trichlorfon. It was believed that AChE-NH2- SBA- 15 could be exploited as a fast, sensitive and low-cost biocatalyst towards the detection of pesticides residues which could be stored at room temperature for a long time.

Fusion of machine vision technology and AlexNet-CNNs deep learning network for the detection of postharvest apple pesticide residues

Pesticide residue is an important factor that affects food safety.In order to achieve effective detection of pesticide residues in apples,a machine-vision-based segmentation algorithm and hyperspectral techniques were used to segment the foreground and background regions of the apple image.By calculating the roundness value and extracting the region with the highest roundness value in the connected region,a region of interest(ROI)maskwas created for the apple.Four pesticides(chlorpyrifos,carbendazimand two mixed pesticides)and an inactive control were used at the same concentration of 100 ppm(except for the control group),and the hyperspectral region of the corresponding sample image was extracted by obtaining the different types of pesticide residues in the ROI masks.To increase the diversity of the samples and to expand the dataset,Gaussianwhite noise with a varying signal-to-noise ratio was added to each of the hyperspectral images of the apple.The number of samples was increased from four types of 12 samples to four types of 72 samples,giving 4608 hyperspectral data images in each category.The structure and parameters of a convolutional neural network(CNN)were determined using theoretical analysis and experimental verification.All the extracted hyperspectral images of apples were normalized to 227×227×3 pixels as the input of the CNN network for pesticide residue detection.There were 18,432 sample data of four types for 72 samples.Of these,12,288 images were selected using a bootstrap sampling method as the training set,and 6144 as the test set,with no overlap.The test results showthatwhen the number of training epochswas 10,the accuracy of the test set detectionwas 99.09%,and the detection accuracy of the single-band average imagewas 95.35%.A comparison with traditional k-nearest neighbor(KNN)and support vectormachine classification algorithms showed that the detection accuracy for KNNwas 43.75%and the average time was 0.7645 s.These results demonstrate that our method is a small-sample,noncontact,fast,effective and low-cost technique that can provide effective pesticide residue detection in postharvest apples.

Detection of chlorpyrifos in apples using gold nanoparticles based on surface enhanced Raman spectroscopy

In this study,gold nanoparticles(AuNPs)were synthesized for rapid and sensitive characterization and quantification of chlorpyrifos in apples.Min-max signal adaptive zooming and second derivative transformation method were adopted to pre-process Raman spectral signal.The min-max signal adaptive zooming method showed a higher correlation coefficient than derivative transformation when developing linear calibration curve between chlorpyrifos pesticide and Raman spectral peak intensity.The present method had a high reproducibility with the relative standard deviation less than 15%.Regression models showed a good linear relationship(R=0.962)between intensity of characteristic spectral peaks(at 677 cm-1)and chlorpyrifos concentration on whole apples ranging from 0.13 mg/kg to 7.59 mg/kg.The application of surface enhancement Raman spectroscopy(SERS)detected chlorpyrifos pesticide to the detection limit of 0.13 mg/kg,which can be applied further for lower concentration in the future.The method presented in this study can provide a way-out for detection of pesticide residue in whole apple to trace amount.

Electrochemical determination of paraquat using a glassy carbon electrode decorated with pillararene-coated nitrogen-doped carbon dots

An electrochemical sensor(carboxylatopillar[5]arene-coated nitrogen-doped carbon dots,namely CCDs)based on carboxylatopillar[5]arene(CP[5])functionalized nitrogen-doped carbon dots(N-CDs)has been developed in a facile and economic manner.To improve the performance of this electrochemical sensor in pesticide detection,the optimal solution pH(pH 7)and loading amount of CCDs on the electrode(0.50 mg/mL)have been determined.By virtue of the good conductivity of N-CDs and the molecular recognition property of CP[5],CCDs modified glassy carbon electrode,namely CCDs/GCE,shows excellent anti-interference capability,selectivity,stability,and reproducibility in the sensitive detection of paraquat.The peak currents are proportional to the paraquat concentration(from 0.1μmol/L to 10μmol/L)with a detection limit of 6.4 nmol/L(S/N=3),indicating a great potential in pesticide detection.In comparison with the electrochemical sensors that require expensive metal nanoparticles and complex preparation processes,CCDs/GCE exhibits excellent detection capability of paraquat with lower cost and simpler preparation processes.