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Advance in Microfluidics Techniques for Rapid Detection of Pesticide Residues in Food

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06 July 2023

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07 July 2023

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Abstract
Food safety is a significant issue that affects people worldwide and is tied to their lives and health. The issue of pesticide residues in food is just one of many issues related to food safety, which leave residues in crops and are transferred through the food chain to human consumption. Foods contaminated with pesticide residues pose a serious risk to human health, including carcinogenicity, neurotoxicity and endocrine disruption. Although traditional methods including gas chromatography, high-performance liquid chromatography, chromatography and mass spectrometry can be used to achieve quantitative analysis of pesticide residues, the disadvantages of these techniques, such as being time-consuming and costly and requiring specialist staff, limit their application. Therefore, there is a need to develop rapid, effective and sensitive equipment for the quantitative analysis of pesticide residues in food. Microfluidics is rapidly emerging in a number of fields due to its outstanding strengths. This paper summarizes the application of microfluidic techniques to pyrethroid, carbamate, organochlorine and organophosphorus pesticides, as well as to commercial products. Meanwhile, the paper also outlines the development of microfluidics in combination with 3D printing technology and nanomaterials for the detection of pesticide residues in food.
Keywords: 
Subject: Engineering  -   Bioengineering

1. Introduction

Pesticides are crucial in contemporary agriculture because they prevent crop losses from pests, fungi, and viruses. It also protects crop growth and yields. The wide application of new pesticides has improved agricultural production, but the food safety problem caused by it has attracted more and more attention. Pollution caused by pesticides has gradually become a global public health problem [1,2,3,4]. Excessive intake of pesticides seriously harms human health [5,6,7]. Unscientific overuse, heavy reliance, and improper processing have left residues in crops and enriched them in the human food chain [8]. Humans ingest foods that contain excessive amounts of pesticides may result in cancer, neurological disease, and endocrine disorder [9,10,11]. The risks posed by pesticide residues are more acute for children and expectant women [12,13]. The entire food business faces a severe challenge due to this focus. The food business and producers are subject to more intense scrutiny and demand to assure the quality and safety of food due to greater regulatory enforcement and customer awareness. One of their most essential tasks is identifying pesticide residues in food for people's lives and health. However, conventional pesticide detection technologies have numerous shortcomings that cannot be used as rapid on-site detection technology [14,15,16].
The primary traditional methods for detecting pesticide residues are gas chromatography, high-performance liquid chromatography, and mass spectrometry [17,18,19,20]. These detection techniques have the advantages of accuracy and sensitivity. However, their sample processing and pretreatment procedure is complicated, time-consuming, expensive, and labor-intensive. As a result, traditional detection technology cannot meet the needs of consumers for rapid and convenient detection of pesticide residues. Therefore, developing a technology that can rapidly, conveniently, efficiently, and sensitively detect pesticide residues in food is essential. The demand for point-of-care testing for food safety is answered by microfluidics. Microfluidics provides a platform for rapidly detecting trace pesticide residues with a small sample. Combining microfluidic technology with pesticide residue detection devices effectively overcomes the shortages of traditional methods and realizes on-site detection [21,22,23,24,25].
Microfluidics integrates various functional units in submillimeter microchannels for a variety of analytical chemistry operations such as purification [26,27], reaction [28,29], separation [30,31], and detection [32,33]. Microfluidic sensors have the advantages of high throughput, miniaturization, portability, and small reagent consumption[34,35,36,37,38,39,40], which can rapidly obtain more accurate detection results. It is significant for food safety to develop on-site detection technologies and portable equipment [41,42,43]. Microfluidic sensors can identify specific analytes through biomolecules and enhance them into detectable signals [44,45,46]. However, to our knowledge, very few reviews currently expound on the application of microfluidics in detecting food pesticide residues.
This article reviews the recent research progress of microfluidics in rapidly detecting food pesticide residues, hoping to provide new ideas for the future microfluidics in pesticide detection. It focuses on several microfluidic devices to detect the most commonly used pesticides globally, including pyrethroids, carbamates, organochlorines, and organophosphorus pesticides. Different microfluidic tools like paper, arrays, centrifugation, and signal readouts like colorimetry, fluorescence, and electrochemical approaches are used for various food samples, as illustrated in Scheme 1. Future trends and commercial technologies for the on-site detection of pesticide residues are explored.

2. Microfluidic devices for pesticide detection

Due to the advantages of microfluidics in point-of-care testing, convenient, rapid, and efficient chemical reactions have become the first choice for detecting food pesticide residues[47,48,49,50,51]. Food pesticide residues, such as those from organophosphorus, carbamates, and pyrethroids, are detected by various microfluidic devices. Table 1 lists exemplary microfluidic tools for rapidly detecting pesticide residues in food.

2.1. Organophosphorus

Pesticides with an organophosphorus chemical as their primary component are known as organophosphorus pesticides [59,60]. These insecticides are commonly used in horticulture and agriculture to improve crop yield and quality while controlling various pests and illnesses. Organophosphorus pesticides primarily poison pests by acetylcholinesterase inhibition [61,62]. However, the nervous system of people might also be impacted by this [63]. Prolonged or excessive exposure to organophosphorus residues may cause neurological symptoms such as headache, dizziness, nausea, vomiting, muscle twitches, neurasthenia, and memory loss [64,65,66]. At present, there are numerous research on the detection of organophosphorus pesticides [67,68,69,70,71,72,73]. Shi and colleagues used phage and horseradish peroxidase to create an eco-friendly and safe electrochemical immunosensor [74].
The microfluidic device based on fluorescence intensity for quick pesticide residue detection in food has higher sensitivity compared to the conventional method [75,76,77,78,79,80,81], and Hu et al. (2019) developed a microfluidic array sensor based on QD-AchE aerogel that can detect organophosphorus pesticide residues quickly and with high sensitivity [23]. The principle of the microfluidic device to detect pesticide residues by fluorescence intensity is depicted in Figure 1. Quantum dots' fluorescence intensity gradually increases with an increase in organophosphorus concentration. Since acetylcholine catalyzes the production of thiochotine, organophosphorus inhibit its activity and restore the fluorescence intensity of acetylcholine-quenched quantum dots. With detection limits of less than 1.2 pM and a detection range of 10-5 M–10-12 M, the researchers evaluated four popular organophosphorus pesticides, including Paraoxon, parathion, dichlorvos, and deltamethrin. This further proved that the sensor has high sensitivity and broad detection range. Additionally, they evaluated that the recovery of organophosphorus insecticides achieved 98 % using apple samples. However, the instrument is currently based on monochromatic fluorescence to detect organophosphorus pesticides, which results in limited detection sensitivity due to the low contrast between red and background color. In the future, the contrast can be increased by adding a variety of colors to improve the detection sensitivity.
Also based on fluorescence detection, but compared with the array microfluidic sensor developed by Hu et al., Tong et al developed a threaded paper-based microfluidic device [52], as shown in Figure 2 below. Using 3D printing technology and fixed with cotton thread, threaded 3D μPAD (Figure 2C) included four 2D μPADs (Figure 2A). They created a ratio fluorescence system for organophosphorus detection using MnO2 nanosheets to oxidize o-phenylenediamine into 2,3-diamino phenazine with yellow-emission fluorescence and the internal filter effect to quench the fluorescence intensity of red emission carbon dots (RCDs). The fluorescence detection image is shown in Figure 2B. They also chose actual samples of spinach and tomatoes, with recovery rates ranging from 94.0 %~106.0 % and relative standard deviations (RSDs) under 8.6 %. The test results matched those from the HPLCMS test. This technique performs well and is appropriate for accurate field organophosphorus identification in real samples. The design diversity of 3D μPAD provides a simple and efficient detection platform for the detection of pesticide samples in complex agricultural samples.
Electrochemical technologies [82,83,84,85] are more straightforward and sensitive than fluorescence detection because they directly transform difficult-to-measure chemical parameters into simple-to-measure electrical ones. Common electrochemical identification techniques frequently demand intricate electrode production procedures and expensive detection costs. Yang et al. suggested a method for identifying pesticide residues based on multilayer paper-based microfluidic chips to address this issue [86]. After spraying pesticides on lettuce, the avermectin, phoxim, and dimethoate identification accuracy remained consistent at 93 %. A stopper microfluidics-based organophosphorus pesticide-detecting system was created by Wang et al. (2014) [53]. As depicted in Figure 3A, the device consists of a glass substrate measuring 11 mm × 33 mm, a Kühler-style inspection thin film three-electrode system, and a PDMS substrate with a flow channel structure. The instrument employs hydrogen peroxide to oxidize the micro-electrode array. Acetylcholinesterase activity changes following the addition of organo-phosphorus pesticides, and the charge change is determined by the Kuhler method. Figure 3B shows the procedure used to process plugs at the T-junction. Finally, the concentration of organophosphorus pesticides is measured. The charge that results and the organophosphorus concentration's logarithm has a linear connection. Malathion's lower detection limit (LOD) is 33 nM, while the LDLs for acetic acid, MEP, and diazinon are 90 nM. The Kuhler method, which relies on inhibiting acetylcholinesterase, can be carried out with tiny volume stoppers, requiring fewer expensive chemicals. The fast mixing of plugs makes it easier to repeat experiments and take accurate readings.

2.2. Carbamate

Carbamate pesticides are frequently employed in agriculture, forestry, and the sound harvesting business owing to the selective, little residual toxicity, and low toxicity to humans and animals [87,88,89,90]. However, carbamate pesticides with heavy usage in foods spread through the food chain and accumulate in human body through the digestive systems and the skin's mucous barrier [9,91,92,93]. In various studies, carbamate pesticides can easily produce nitroso compounds with nitrite in food (bread, yoghurt, cheese, soy sauce, vinegar), which can substantially harm human health [94,95]. They are also mutagenic, teratogenic, and carcinogenic under acidic circumstances in the stomach [96]. In that case, various methods of detecting carbamate were studied, including mass spectrometry, gas chromatography enzyme-linked immunosorbent assays and liquid-liquid extraction [97,98,99,100]. However, the mentioned methods are limited by complex procedure and expensive instruments [101]. To solve these problems, desirable methods like fluorescence, colorimetry and electrochemistry were proposed with high sensitivity and rapidity [102,103,104]. For example, Wu group discovered Cu2+/Cu+ conversion as the electrochemical signal for detection of ethyl carbamate. To achieve the visual detection of carbamate, Chen group proposed fluorescence paper-based sensor to detect carbamate in food [105]. To achieve the automation of detecting, Yan group designed a multi-signal readout platform for sensitive monitoring carbamate pesticide [106]. Currently, these desirable methods still suffer from long-distance transportation and complex environment.
Based on fluorescence, colorimetry and electrochemistry, microfluidic devices offer a viable solution to achieve the detection of carbamate and overcome the issues associated with complex procedures and transportation. Interestingly, the unitary and multiple signal readouts were both widely utilized in microfluidics [39,107,108,109,110,111]. For example, based on colorimetry, M.D. Fernández-Ramos (2020) suggests a bioactive microfluidic paper device for pesticide determination in water [54]. The proposed device contains three independent regions: a μPAD at the bottom for sampling, two microchannels separated by deposited acetylcholinesterase and AChCl solutions, and a top μPAD containing a pH indicator for detection. The paper device, working at room temperature, set the reducing reaction's rate as an analytical signal to quantified based on the color of μPAD. Figure 4A and B displays two diagrams in color to verify the existence of carbamate, where the purple one was deemed as the presence of carbamate, and the yellow one was regarded as the absence of carbamate. The design drawing for the whole device was exhibited in Figure 4C to guide the fabrication of microfluidic chip. The concentrations of chlorpyrifos and carbaryl were determined using their calibration curves, and they were found to be 0.24 μg L−1 for chlorpyrifos and 2.00 μg L−1 for carbaryl, respectively. The repeatability ranged between 4.2 and 5.5%, and the detection limits were 0.24 and 2.00 μg L−1, respectively. The researchers also conducted recovery trials with known concentrations of carbaryl and chlorpyrifos with average recovery rates of 97.7 and 102.3%. The device with the capillary holdersible to conduct many analytical processes such as sample buffering, sample filtration, etc.
Meanwhile, multiple readouts are successfully utilized to achieve the detection of carbamate, increasing the sensitivity and integration. Zhao et al. (2021) built a portable automatic double-readout detector integrated with a 3D-printed microfluidic nanosensor on the foundation of the colorimetric method [22]. As shown in Figure 5A and C, the chip, containing five chambers and several channel structures, was designed to control the flow and detection of chemical mixtures through centrifugal force [112,113,114]. The device, capturing chromatic aberration and fluorescence spectral images, successfully distinguished six urethane pesticides based on the cross-response mechanism and agglomeration effect of AuNPs (Figure 5B). It demonstrated high sensitivity and selectivity for urethane pesticides at the ppb level and good recognition ability at low concentrations of 50 ppb–800 ppb. The device with convenience and integration can also be adapted in environmental monitoring and home testing (Figure 5C).
The advantage of employing an electrochemical approach over a fluorescence method is that the analyte of interest does not need to be coupled to a fluorescent reporter or an im-aging setup. It simply requires a pair of electrodes, which are highly sensitive and versa-tile and are easily shrunk and integrated into a microfluidic platform. Based on electrochemical microfluidics, Flavio et al. suggested a method for quickly and accurately detecting phenyl carbamate herbicides in rivers, lakes, and irrigation water samples. This technique significantly enhances the C18-based OMIX microtip approach, which enriches the analyte by a factor of 10, lowers reagent waste, and increases the accuracy of detection results. It can quickly separate and sensitively detect carbamate pesticides in 6 minutes. Meanwhile, Gu et al. coupled concentration gradient creation and electrochemical detection to fabricate a straightforward and reliable droplet dose-reactive enzyme inhibition microfluidic device [56]. To introduce reagent and construct concentration gradients, a variety of slotted flasks and conical-tip capillaries were implicated in this device (Figure.6A). Polydimethylsiloxane (PDMS), which is integrated with microelectrodes, is used for droplet production and electrochemical detection. The method based on the enzyme inhibition principle depicted in Figure 6B, calculated the average semi-inhibitory concentration value of carbaryl with less than 5 μL of total reagent.

2.3. Other pesticides

Organochlorine pesticides are organic compounds containing chlorine in their chemical structure, which are fat-soluble and kill insects by interfering with the function of the nervous system [115,116,117,118,119]. It is widely used worldwide because of its low price, its broad spectrum of insecticidal efficiency, and ease of use. Excessive use of organochlorine pesticides will not only affect the environment, but also cause harm to the human body [120,121,122].Organochlorine pesticides mainly affect human health through food, respiration, and skin contact and can destroy certain hormones, enzymes, growth factors, and neurotransmitters in the body. Changes in relative homeostasis conditions within cells lead to oxidative stress and rapid cell death, leading to Parkinson's [123], cancer [124], and endocrine and reproductive diseases.
In order to detect organochlorine pesticide residues, many people have done the research. Malik et al. successfully determined organochlorine pesticide residues using an electron capture detector (GC-ECD) in 2011 [125], and Chowdhury et al. in 2013 using gas chromatography-tandem mass spectrometry (GC-MS) [126]. While these methods accurately measure organochlorine residues, they are often expensive and require specialized operators. In order to develop a simple, efficient, and stable method for the detection of organochlorine pesticide residues, Wang et al. developed a paper-based microfluidic device using fluorescence detection, as shown in Figure 7A [57], which consists of 3 three-port valves, six peristaltic pumps, and a 3D printing-based paper-based test platform (Figure 7B). The team proved the device's practical applicability and high sensitivity with good recovery and close-to-peak detection of dicofol content in tea by adding multiple interference terms.
Pyrethroid pesticides are synthesized by simulating the chemical structure of natural pyrethroids, also known as biomimetic synthetic pesticides. It has the effect of a wide insecticidal spectrum, high efficacy, sterilization, and mold inhibition [127]. Pyrethroids have effectively reduced the incidence of malaria in Africa and other places [128], but overuse has seriously affected people's health, causing cardiovascular diseases, reproductive diseases and so on [129,130,131,132,133]. Pyrethroid analysis is routinely used by gas chromatography-electron capture detector (GC-ECD), gas chromatography-mass spectrometry (GC-MS), liquid chromatography-ultraviolet (LC-UV), and liquid chromatography-mass spectrometry (LC-MS). The instrument technologies mentioned above have high accuracy and precision, good sensitivity, and very low detection limits, but they are expensive, complex to operate, and unsuitable for most environments.
In order to develop a low-cost and convenient pyrethroid detection method[134], Sumate et al. developed a layered paper microfluidic device using colorimetric detection to screen pyrethroids type II[58] , including cypermethrin, in environmental water samples, deltamethrin, cyhalothrin, fenvalerate, the detection principle is shown in Figure 8A and D, through cyanide ions and ninhydrin reaction color, on μPAD formed a color intensity corresponding to pyrethroid pesticide concentration, using red, green and blue color matching system for digital image analysis (Figure 8B, C). The detection limits for cypermethrin, deltamethrin, cyhalothrin, and fenvalerate were 2.50, 1.06, 3.20, and 5.73 μg/mL, respectively. Due to the paper-based layered structure, the device is easier to manufacture and use. It provides a detection platform for pesticide contamination in environmental surface water, with the advantages of portability, low reagent/sample consumption, and low-cost detection.

2.4. Commercialized products

In order to meet the demand for quick and precise studies, several microfluidic devices have been created in the fields of food safety and pesticide detection. The commercial items that are currently utilized for pesticide testing are listed in Table 2 below. These gadgets use diverse microfluidic technology and have unique benefits and drawbacks.
For the quick and precise detection of pesticides in food and environmental samples, a number of microfluidic devices have been developed. The portable, highly sensitive My-coLabTM AflaQuickTM by EnviroLogix Inc. can identify aflatoxins in just 10 minutes. EnviroLogix Inc.'s QuickTM has a high sensitivity and mobility level and can detect aflatoxins in under 10 minutes. Multiplexed pesticide detection is available with the Advanced Animal Diagnostics RaptorTM Integrated Analysis Platform, although it is more expensive and demands specialist training. Pesticide identification is possible with the Biosensing Instrument Inc. ToxiQuantTM Pesticide Microarray Kit. However, it requires refrigeration and has longer test times. The portable microfluidic sensor with SERS technology from GBC Scientific Equipment allows for label-free detection but calls for SERS equipment. The RapidChek® SELECTTM Salmonella from Romer Labs quickly identifies salmonella but has a low sensitivity. Although it needs specialist equipment, Detection's BioFlash Biological Identifier offers quick and sensitive findings. The mass spectrometry equipment is necessary for the ATHENA Integrated System, a lab-on-a-chip with quick results and customizable choices.
The microfluidic devices mentioned above have special features and capabilities to detect pesticides in food and environmental samples. While each technology has benefits like quick results, portability, and customizability choices, it also has drawbacks like con-strained detection targets, reduced sensitivity, need for specialized equipment, and a range of prices. These aspects are important when choosing the best microfluidic device to meet their unique pesticide detection and food safety application demands.

3. Conclusion and future perspectives

Pesticide residues in food have an impact on human life and health. As more people become aware of food safety issues, researchers have started looking into several rapid, easy, and effective ways to check food safety, of which microfluidic technology is one of the simplest and most effective methods. This paper reviews the latest developments in microfluidics for the detection of pesticide residues in food. Compared to traditional pesticide detection devices, microfluidic technology has the advantages of ease of use, low sample consumption, low reagent waste, high sensitivity and accuracy. In particular, an increasing number of microfluidic detection technologies for pesticide residues have started to be integrated with 3D-μPAD, allowing for a greater variety of assay device de-signs and providing a direct and effective platform for pesticide detection in complex agricultural samples. In addition, a growing number of microfluidic devices are opting to use this combination of nanomaterials and microfluidic technology, as it allows enrichment of the target analyte between 10 and 100 times, while using fewer reagents and obtaining better detection results and sensitivity.
Currently, there are three primary types of microfluidic technology detection methods for the detection of pesticide residues in food: colorimetric methods, fluorescence intensity methods, and electrochemical approaches. Each of these categories has its own benefits. Current research in fluorescence detection continues to focus on the monochromatic fluorescence-based detection of pesticide residues, which has limited detection sensitivity. In the future, the contrast can be improved by adding a variety of colors to increase the detection sensitivity. Microfluidics is just beginning to develop in pesticide detection, but as people's quality of life improves, they will become more concerned about food safety. With the development and exploration of 3D printing, nanomaterials and other technologies in the future, microfluidics will find more uses in food pesticide residue detection, providing simpler, more effective, fast, sensitive and affordable methods.

Author Contributions

Conceptualization, B.Y. and Z.J.; formal analysis, Y.Z. S.G. and A.S.; investigation, B.Y., Z.J., Y.Z. and A.S.; writing original draft preparation, A.J., Y.Z. S. G and A.S.; All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (No. 52075138), the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (No. 22KJB150050), the Jiangsu Agricultural Science and Technology Innovation Fund (No. CX(21)3162), the Market Supervision Administration Science and Technology Fund of Jiangsu Province (No. KJ2023076), the Postgraduate Research & Practice Innovation Program of Jiangsu Province (No. KYCX22_3479) and Science and Technology Planning Project of Yangzhou City (No. YZ2022180).

Data Availability Statement

The data presented in this study are available upon request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Weng, X.; Neethirajan, S. Ensuring food safety: Quality monitoring using microfluidics. Trends Food Sci. Technol. 2017, 65, 10–22. [CrossRef]
  2. Reeves, W.R.; McGuire, M.K.; Stokes, M.; Vicini, J.L. Assessing the Safety of Pesticides in Food: How Current Regulations Protect Human Health. Adv. Nutr. Int. Rev. J. 2019, 10, 80–88. [CrossRef]
  3. Lin, X.; Zhao, M.; Peng, T.; Zhang, P.; Shen, R.; Jia, Y. Detection and discrimination of pathogenic bacteria with nanomaterials-based optical biosensors: A review. Food Chem. 2023, 426, 136578. [CrossRef]
  4. Lin, X.; Ibarlucea, B.; Peng, T.; Shen, R.; Li, P.; Zhang, P. Two birds with one stone: A multifunctional nanoplatform for photothermal sensitive detection and real-time inactivation of Staphylococcus aureus with NIR responsive Cu2−XSe@Van NPs. Sensors Actuators B: Chem. 2023, 381, 133475. [CrossRef]
  5. Shrestha, S.; Parks, C.G.; Goldner, W.S.; Kamel, F.; Umbach, D.M.; Ward, M.H.; Lerro, C.C.; Koutros, S.; Hofmann, J.N.; Freeman, L.E.B.; et al. Pesticide Use and Incident Hypothyroidism in Pesticide Applicators in the Agricultural Health Study. Environ. Heal. Perspect. 2018, 126, 97008. [CrossRef]
  6. Kim, K.-H.; Kabir, E.; Jahan, S.A. Exposure to pesticides and the associated human health effects. Sci. Total Environ. 2017, 575, 525–535. [CrossRef]
  7. Raanan, R. Exposure to pesticides and chronic respiratory health effects: what is the link? Thorax 2021, 76, 748-749.
  8. Liu, Y.; Lin, X.; Ji, X.; Hao, Z.; Tao, Z. Smartphone-based enzyme-free fluorescence sensing of organophosphate DDVP. Microchim. Acta 2020, 187, 419. [CrossRef]
  9. Rani, L.; Thapa, K.; Kanojia, N.; Sharma, N.; Singh, S.; Grewal, A.S.; Srivastav, A.L.; Kaushal, J. An extensive review on the consequences of chemical pesticides on human health and environment. J. Clean. Prod. 2020, 283, 124657. [CrossRef]
  10. Parks, C.; Costenbader, K.; Long, S.; Hofmann, J.; Beane, F.L.; Sandler, D. Pesticide use and risk of systemic autoimmune diseases in the Agricultural Health Study. Environ. Res. 2022, 209, 112862–112862. [CrossRef]
  11. Bonner, M.R.; Freeman, L.E.B.; Hoppin, J.A.; Koutros, S.; Sandler, D.P.; Lynch, C.F.; Hines, C.J.; Thomas, K.; Blair, A.; Alavanja, M.C. Occupational Exposure to Pesticides and the Incidence of Lung Cancer in the Agricultural Health Study. Environ. Heal. Perspect. 2017, 125, 544–551. [CrossRef]
  12. Eskenazi, B.; Bradman, A.; Castorina, R. Exposures of children to organophosphate pesticides and their potential adverse health effects.. Environ. Heal. Perspect. 1999, 107, 409–419. [CrossRef]
  13. Lozowicka, B. Health risk for children and adults consuming apples with pesticide residue. Sci. Total. Environ. 2015, 502, 184–198. [CrossRef]
  14. Umapathi, R.; Park, B.; Sonwal, S.; Rani, G.M.; Cho, Y.; Huh, Y.S. Advances in optical-sensing strategies for the on-site detection of pesticides in agricultural foods. Trends Food Sci. Technol. 2021, 119, 69–89. [CrossRef]
  15. Pang, S.; Yang, T.; He, L. Review of surface enhanced Raman spectroscopic (SERS) detection of synthetic chemical pesticides. TrAC Trends Anal. Chem. 2016, 85, 73–82. [CrossRef]
  16. Umapathi, R.; Sonwal, S.; Lee, M.J.; Rani, G.M.; Lee, E.-S.; Jeon, T.-J.; Kang, S.-M.; Oh, M.-H.; Huh, Y.S. Colorimetric based on-site sensing strategies for the rapid detection of pesticides in agricultural foods: New horizons, perspectives, and challenges. Co-ord. Chem. Rev. 2021, 446, 214061. [CrossRef]
  17. Belmonte, I.d.S.; Pizzolato, T.M.; Gama, M.R. Quaternary ammonium pesticides: A review of chromatography and non-chromatography methods for determination of pesticide residues in water samples. Trends Environ. Anal. Chem. 2022, 35, e00171. [CrossRef]
  18. Varela-Martinez, D.A.; Gonzalez-Curbelo, M.A.; Gonzalez-Salamo, J.; Hernandez-Borges, J. Analysis of multiclass pesticides in dried fruits using QuEChERS-gas chromatography tandem mass spectrometry. Food Chem. 2019, 297, 124961.
  19. Pang, X.; Qiu, J.; Zhang, Z.; Li, P.; Xing, J.; Su, X.; Liu, G.; Yu, C.; Weng, R. Wide-Scope Multi-residue analysis of pesticides in beef by gas chromatography coupled with quadrupole Orbitrap mass spectrometry. Food Chem. 2023, 407, 135171. [CrossRef]
  20. Shao, Y.; Wang, M.; Cao, J.; She, Y.; Cao, Z.; Hao, Z.; Jin, F.; Wang, J.; El-Aty, A.A. A method for the rapid determination of pesticides coupling thin-layer chromatography and enzyme inhibition principles. Food Chem. 2023, 416, 135822. [CrossRef]
  21. Tahirbegi, I.B.; Ehgartner, J.; Sulzer, P.; Zieger, S.; Kasjanow, A.; Paradiso, M.; Strobl, M.; Bouwes, D.; Mayr, T. Fast pesticide detection inside microfluidic device with integrated optical pH, oxygen sensors and algal fluorescence. Biosens. Bioelectron. 2017, 88, 188–195. [CrossRef]
  22. Zhao, S.; Huang, J.; Lei, J.; Huo, D.; Huang, Q.; Tan, J.; Li, Y.; Hou, C.; Tian, F. A portable and automatic dual-readout detector integrated with 3D-printed microfluidic nanosensors for rapid carbamate pesticides detection. Sensors Actuators B: Chem. 2021, 346, 130454. [CrossRef]
  23. Hu, T.; Xu, J.; Ye, Y.; Han, Y.; Li, X.; Wang, Z.; Sun, D.; Zhou, Y.; Ni, Z. Visual detection of mixed organophosphorous pesticide using QD-AChE aerogel based microfluidic arrays sensor. Biosens. Bioelectron. 2019, 136, 112–117. [CrossRef]
  24. Tong, X.; Cai, G.H.; Xie, L.W.; Wang, T.T.; Zhu, Y.F.; Peng, Y.Q.; Tong, C.Y.; Shi, S.Y.; Guo, Y. Threaded 3D microfluidic paper analytical device-based ratiometric fluorescent sensor for background-free and visual detection of organophosphorus pesticides. Biosens. Bioelectron. 2023, 222, 7.
  25. Kung, C.-T.; Hou, C.-Y.; Wang, Y.-N.; Fu, L.-M. Microfluidic paper-based analytical devices for environmental analysis of soil, air, ecology and river water. Sensors Actuators B: Chem. 2019, 301, 126855. [CrossRef]
  26. Vicente, F.A.; Plazl, I.; Ventura, S.P.M.; Žnidaršič-Plazl, P. Separation and purification of biomacromolecules based on microfluidics. Green Chem. 2020, 22, 4391–4410. [CrossRef]
  27. Pan, B.; Karadaghi, L.R.; Brutchey, R.L.; Malmstadt, N. Purification of Ionic Liquid Solvents in a Self-Optimizing, Continuous Microfluidic Process via Extraction of Metal Ions and Phase Separation. ACS Sustain. Chem. Eng. 2023, 11, 228–237. [CrossRef]
  28. Salva, M.L.; Rocca, M.; Hu, Y.; Delamarche, E.; Niemeyer, C.M. Complex Nucleic Acid Hybridization Reactions inside Capillary-Driven Microfluidic Chips. Small 2020, 16, e2005476. [CrossRef]
  29. Wei, C.; Yu, C.; Li, S.; Meng, J.; Li, T.; Cheng, J.; Li, J. A droplet-based multivolume microfluidic device for digital polymerase chain reaction. Sensors Actuators B: Chem. 2022, 371, 132473. [CrossRef]
  30. Chen, T.; Huang, C.; Wang, Y.; Wu, J. Microfluidic methods for cell separation and subsequent analysis. Chin. Chem. Lett. 2022, 33, 1180–1192. [CrossRef]
  31. Jahromi, P.F.; Karimi-Sabet, J.; Amini, Y.; Fadaei, H. Pressure-driven liquid-liquid separation in Y-shaped microfluidic junctions. Chem. Eng. J. 2017, 328, 1075–1086. [CrossRef]
  32. Han, X.; Liu, Y.; Yin, J.; Yue, M.; Mu, Y. Microfluidic devices for multiplexed detection of foodborne pathogens. Food Res. Int. 2021, 143, 110246. [CrossRef]
  33. Wu, Y.-T.; Yang, C.-E.; Ko, C.-H.; Wang, Y.-N.; Liu, C.-C.; Fu, L.-M. Microfluidic detection platform with integrated micro-spectrometer system. Chem. Eng. J. 2020, 393, 124700. [CrossRef]
  34. Dong, R.; Liu, Y.; Mou, L.; Deng, J.; Jiang, X. Microfluidics-Based Biomaterials and Biodevices. Adv. Mater. 2019, 31, e1805033. [CrossRef]
  35. Yang, Y.; Chen, Y.; Tang, H.; Zong, N.; Jiang, X. Microfluidics for Biomedical Analysis. Small Methods 2020, 4, 1900451. [CrossRef]
  36. Shang, L.; Cheng, Y.; Zhao, Y. Emerging Droplet Microfluidics. Chem. Rev. 2017, 117, 7964–8040. [CrossRef]
  37. Mou, L.; Jiang, X. Materials for Microfluidic Immunoassays: A Review. Adv. Heal. Mater. 2017, 6, 1601403. [CrossRef]
  38. Yin, B.; Yue, W.; Sohan, A.; Wan, X.; Zhou, T.; Shi, L.; Qian, C.; Lin, X. Construction of a desirable hyperbolic microfluidic chip for ultrasensitive determination of PCT based on chemiluminescence. J. Mater. Chem. B 2023, 11, 1978–1986. [CrossRef]
  39. Lin, X.; Wu, H.; Zeng, S.; Peng, T.; Zhang, P.; Wan, X.; Lang, Y.; Zhang, B.; Jia, Y.; Shen, R.; et al. A self-designed device integrated with a Fermat spiral microfluidic chip for ratiometric and automated point-of-care testing of anthrax biomarker in real samples. Biosens. Bioelectron. 2023, 230, 115283. [CrossRef]
  40. Yin, B.-F.; Wan, X.-H.; Yang, M.-Z.; Qian, C.-C.; Sohan, A.S.M.M.F. Wave-shaped microfluidic chip assisted point-of-care testing for accurate and rapid diagnosis of infections. Mil. Med Res. 2022, 9, 1–13. [CrossRef]
  41. Nilghaz, A.; Mousavi, S.M.; Li, M.; Tian, J.; Cao, R.; Wang, X. Paper-based microfluidics for food safety and quality analysis. Trends Food Sci. Technol. 2021, 118, 273–284. [CrossRef]
  42. Wang, A.; Feng, X.; He, G.; Xiao, Y.; Zhong, T.; Yu, X. Recent advances in digital microfluidic chips for food safety analysis: Preparation, mechanism and application. Trends Food Sci. Technol. 2023, 134, 136–148. [CrossRef]
  43. Al-Mugahiry, B.; Al-Lawati, H.A.J. Recent analytical advancements in microfluidics using chemiluminescence detection systems for food analysis. TrAC Trends Anal. Chem. 2020, 124, 115802. [CrossRef]
  44. Fu, X.; Sun, J.; Liang, R.; Guo, H.; Wang, L.; Sun, X. Application progress of microfluidics-integrated biosensing platforms in the detection of foodborne pathogens. Trends Food Sci. Technol. 2021, 116, 115–129. [CrossRef]
  45. Xing, G.; Ai, J.; Wang, N.; Pu, Q. Recent progress of smartphone-assisted microfluidic sensors for point of care testing. TrAC Trends Anal. Chem. 2022, 157, 116792. [CrossRef]
  46. Tao, Y.; Shen, H.; Deng, K.; Zhang, H.; Yang, C. Microfluidic devices with simplified signal readout. Sensors Actuators B: Chem. 2021, 339, 129730. [CrossRef]
  47. Xie, Z.; Pu, H.; Sun, D. Computer simulation of submicron fluid flows in microfluidic chips and their applications in food analysis. Compr. Rev. Food Sci. Food Saf. 2021, 20, 3818–3837. [CrossRef]
  48. Sivakumar, R.; Lee, N.Y. Recent progress in smartphone-based techniques for food safety and the detection of heavy metal ions in environmental water. Chemosphere 2021, 275, 130096. [CrossRef]
  49. Kudr, J.; Zitka, O.; Klimanek, M.; Vrba, R.; Adam, V. Microfluidic electrochemical devices for pollution analysis–A review. Sensors Actuators B: Chem. 2017, 246, 578–590. [CrossRef]
  50. Yin, B.; Zheng, W.; Dong, M.; Yu, W.; Chen, Y.; Joo, S.W.; Jiang, X. An enzyme-mediated competitive colorimetric sensor based on Au@Ag bimetallic nanoparticles for highly sensitive detection of disease biomarkers. Anal. 2017, 142, 2954–2960. [CrossRef]
  51. Yin, B.; Wang, Y.; Dong, M.; Wu, J.; Ran, B.; Xie, M.; Joo, S.W.; Chen, Y. One-step multiplexed detection of foodborne pathogens: Combining a quantum dot-mediated reverse assaying strategy and magnetic separation. Biosens. Bioelectron. 2016, 86, 996–1002. [CrossRef]
  52. Tong, X.; Cai, G.H.; Xie, L.W.; Wang, T.T.; Zhu, Y.F.; Peng, Y.Q.; Tong, C.Y.; Shi, S.Y.; Guo, Y. Threaded 3D microfluidic paper analytical device-based ratiometric fluorescent sensor for background-free and visual detection of organophosphorus pesticides. Biosens. Bioelectron. 2023, 222, 114981.
  53. Wang, J.; Suzuki, H.; Satake, T. Coulometric microdevice for organophosphate pesticide detection. Sensors Actuators B: Chem. 2014, 204, 297–301. [CrossRef]
  54. Ramos, F.; Ogunneye, A.; Barbarinde, N.; Erenas, M.; Capitán-Vallvey, L. Bioactive microfluidic paper device for pesticide determination in waters. Talanta 2020, 218, 121108. [CrossRef]
  55. Della Pelle, F.; Del Carlo, M.; Sergi, M.; Compagnone, D.; Escarpa, A. Press-transferred carbon black nanoparticles on board of microfluidic chips for rapid and sensitive amperometric determination of phenyl carbamate pesticides in environmental samples. Microchim. Acta 2016, 183, 3143–3149. [CrossRef]
  56. Gu, S.; Lu, Y.; Ding, Y.; Li, L.; Zhang, F.; Wu, Q. Droplet-based microfluidics for dose–response assay of enzyme inhibitors by electrochemical method. Anal. Chim. Acta 2013, 796, 68–74. [CrossRef]
  57. Wang, F.; Zhang, X.; Huangfu, C.; Zhu, M.; Li, C.; Feng, L. The BODIPY-based chemosensor for the fluorometric determination of organochlorine pesticide dicofol. Food Chem. 2022, 370, 131033. [CrossRef]
  58. Pengpumkiat, S.; Nammoonnoy, J.; Wongsakoonkan, W.; Konthonbut, P.; Kongtip, P. A Microfluidic Paper-Based Analytical Device for Type-II Pyrethroid Targets in an Environmental Water Sample. Sensors 2020, 20, 4107. [CrossRef]
  59. Yu, Y.; You, J.; Sun, Z.; Li, G.; Ji, Z.; Zhang, S.; Zhou, X. Determination of residual organophosphorus thioester pesticides in agricultural products by chemical isotope-labelling liquid chromatography-tandem mass spectrometry coupled with in-syringe dispersive solid phase clean-up and in situ cleavage. Anal. Chim. Acta 2019, 1055, 44–55. [CrossRef]
  60. Balakrishnan, V.K.; Buncel, E.; Vanloon, G.W. Micellar Catalyzed Degradation of Fenitrothion, an Organophosphorus Pesticide, in Solution and Soils. Environ. Sci. Technol. 2005, 39, 5824–5830. [CrossRef]
  61. Fu, H.; Liu, H.; Ge, Y.; Chen, Y.; Tan, P.; Bai, J.; Dai, Z.; Yang, Y.; Wu, Z. Chitosan oligosaccharide alleviates and removes the toxicological effects of organophosphorus pesticide chlorpyrifos residues. J. Hazard. Mater. 2023, 446, 130669. [CrossRef]
  62. Pawar, K.S.; Bhoite, R.R.; Pillay, C.P.; Chavan, S.C.; Malshikare, D.S.; Garad, S.G. Continuous pralidoxime infusion versus repeated bolus injection to treat organophosphorus pesticide poisoning: a randomised controlled trial. Lancet 2006, 368, 2136–2141. [CrossRef]
  63. Engel, S.M.; Bradman, A.; Wolff, M.S.; Rauh, V.A.; Harley, K.G.; Yang, J.H.; Hoepner, L.A.; Barr, D.B.; Yolton, K.; Vedar, M.G.; et al. Prenatal Organophosphorus Pesticide Exposure and Child Neurodevelopment at 24 Months: An Analysis of Four Birth Cohorts. Environ. Heal. Perspect. 2016, 124, 822–830. [CrossRef]
  64. Wang, G.; Li, J.; Xue, N.; Al-Huqail, A.A.; Majdi, H.S.; Darvishmoghaddam, E.; Assilzadeh, H.; Khadimallah, M.A.; Ali, H.E. Risk assessment of organophosphorus pesticide residues in drinking water resources: Statistical and Monte-Carlo approach. Chemosphere 2022, 307, 135632. [CrossRef]
  65. Wu, Y.; Song, J.; Zhang, Q.; Yan, S.; Sun, X.; Yi, W.; Pan, R.; Cheng, J.; Xu, Z.; Su, H. Association between organophosphorus pesticide exposure and depression risk in adults: A cross-sectional study with NHANES data. Environ. Pollut. 2023, 316, 120445. [CrossRef]
  66. Liang, Y.; Zhan, J.; Liu, D.; Luo, M.; Han, J.; Liu, X.; Liu, C.; Cheng, Z.; Zhou, Z.; Wang, P. Organophosphorus pesticide chlorpyrifos intake promotes obesity and insulin resistance through impacting gut and gut microbiota. Microbiome 2019, 7, 1–15. [CrossRef]
  67. Kim, M.S.; Kim, G.W.; Park, T.J. A facile and sensitive detection of organophosphorus chemicals by rapid aggregation of gold nanoparticles using organic compounds. Biosens. Bioelectron. 2015, 67, 408–412. [CrossRef]
  68. Li, W.; Rong, Y.; Wang, J.; Li, T.; Wang, Z. MnO2 switch-bridged DNA walker for ultrasensitive sensing of cholinesterase activity and organophosphorus pesticides. Biosens. Bioelectron. 2020, 169, 112605. [CrossRef]
  69. Cancar, H.D.; Soylemez, S.; Akpinar, Y.; Kesik, M.; Göker, S.; Gunbas, G.; Volkan, M.; Toppare, L. A Novel Acetylcholinesterase Biosensor: Core–Shell Magnetic Nanoparticles Incorporating a Conjugated Polymer for the Detection of Organophosphorus Pesticides. ACS Appl. Mater. Interfaces 2016, 8, 8058–8067. [CrossRef]
  70. Song, D.; Tian, T.; Yang, X.; Wang, L.; Sun, Y.; Li, Y.; Huang, H. Smartphone-assisted sensor array constructed by copper-based laccase-like nanozymes for specific identification and discrimination of organophosphorus pesticides. Food Chem. 2023, 424, 136477. [CrossRef]
  71. Schütze, A.; Morales-Agudelo, P.; Vidal, M.; Calafat, A.M.; Ospina, M. Quantification of glyphosate and other organophosphorus compounds in human urine via ion chromatography isotope dilution tandem mass spectrometry. Chemosphere 2021, 274, 129427–129427. [CrossRef]
  72. Chang, J.; Yu, L.; Hou, T.; Hu, R.; Li, F. Direct and Specific Detection of Glyphosate Using a Phosphatase-like Nanozyme-Mediated Chemiluminescence Strategy. Anal. Chem. 2023, 95, 4479–4485. [CrossRef]
  73. Cheng, Z.; Dong, F.; Xu, J.; Liu, X.; Wu, X.; Chen, Z.; Pan, X.; Gan, J.; Zheng, Y. Simultaneous determination of organophosphorus pesticides in fruits and vegetables using atmospheric pressure gas chromatography quadrupole-time-of-flight mass spectrometry. Food Chem. 2017, 231, 365–373. [CrossRef]
  74. Shi, R.; Zou, W.; Zhao, Z.; Wang, G.; Guo, M.; Ai, S.; Zhou, Q.; Zhao, F.; Yang, Z. Development of a sensitive phage-mimotope and horseradish peroxidase based electrochemical immunosensor for detection of O,O-dimethyl organophosphorus pesticides. Biosens. Bioelectron. 2022, 218, 127419. [CrossRef]
  75. Fu, R.; Wang, Y.; Liu, Y.; Liu, H.; Zhao, Q.; Zhang, Y.; Wang, C.; Li, Z.; Jiao, B.; He, Y. CRISPR-Cas12a based fluorescence assay for organophosphorus pesticides in agricultural products. Food Chem. 2022, 387, 132919. [CrossRef]
  76. Shen, Y.; Gao, X.; Chen, H.; Wei, Y.; Yang, H.; Gu, Y. Ultrathin C3N4 nanosheets-based oxidase-like 2D fluorescence nanozyme for dual-mode detection of organophosphorus pesticides. J. Hazard. Mater. 2023, 451, 131171. [CrossRef]
  77. Long, Q.; Li, H.; Zhang, Y.; Yao, S. Upconversion nanoparticle-based fluorescence resonance energy transfer assay for organophosphorus pesticides. Biosens. Bioelectron. 2015, 68, 168–174. [CrossRef]
  78. Yuan, L.; Tian, X.Y.; Fan, Y.S.; Sun, Z.B.; Zheng, K.Y.; Zou, X.B.; Zhang, W. TPB-DMTP@S-CDs/MnO2 Fluorescence Composite on a Dual- Emission-Capture Sensor Module for Fingerprint Recognition of Organophosphorus Pesticides. Anal. Chem. 2023, 95, 2741–2749.
  79. Zhang, C.; Wang, L.; Tu, Z.; Sun, X.; He, Q.; Lei, Z.; Xu, C.; Liu, Y.; Zhang, X.; Yang, J.; et al. Organophosphorus pesticides detection using broad-specific single-stranded DNA based fluorescence polarization aptamer assay. Biosens. Bioelectron. 2014, 55, 216–219. [CrossRef]
  80. Meng, X.; Wei, J.; Ren, X.; Ren, J.; Tang, F. A simple and sensitive fluorescence biosensor for detection of organophosphorus pesticides using H2O2-sensitive quantum dots/bi-enzyme. Biosens. Bioelectron. 2013, 47, 402–407. [CrossRef]
  81. Jiang, W.; Yang, Z.; Tong, F.; Zhang, S.; Zhu, L.; Wang, L.; Huang, L.; Liu, K.; Zheng, M.; Zhou, Y.; et al. Two birds with one stone: An enzyme-regulated ratiometric fluorescent and photothermal dual-mode probe for organophosphorus pesticide detection. Biosens. Bioelectron. 2023, 224, 115074. [CrossRef]
  82. Velusamy, V.; Palanisamy, S.; Chen, S.-W.; Balu, S.; Yang, T.C.; Banks, C.E. Novel electrochemical synthesis of cellulose microfiber entrapped reduced graphene oxide: A sensitive electrochemical assay for detection of fenitrothion organophosphorus pesticide. Talanta 2019, 192, 471–477. [CrossRef]
  83. Zheng, Q.; Chen, Y.; Fan, K.; Wu, J.; Ying, Y. Exploring pralidoxime chloride as a universal electrochemical probe for organophosphorus pesticides detection. Anal. Chim. Acta 2017, 982, 78–83. [CrossRef]
  84. Gopi, P.K.; Ngo, D.B.; Chen, S.-M.; Ravikumar, C.H.; Surareungchai, W. High-performance electrochemical sensing of hazardous pesticide Paraoxon using BiVO4 nano dendrites equipped catalytic strips. Chemosphere 2022, 288, 132511. [CrossRef]
  85. Liu, X.; Cheng, H.; Zhao, Y.; Wang, Y.; Li, F. Portable electrochemical biosensor based on laser-induced graphene and MnO2 switch-bridged DNA signal amplification for sensitive detection of pesticide. Biosens. Bioelectron. 2022, 199, 113906. [CrossRef]
  86. Yang, N.; Zhou, X.; Yu, D.; Jiao, S.; Han, X.; Zhang, S.; Yin, H.; Mao, H. Pesticide residues identification by impedance time-sequence spectrum of enzyme inhibition on multilayer paper-based microfluidic chip. J. Food Process. Eng. 2020, 43, 127419. [CrossRef]
  87. Liu, T.; Su, H.; Qu, X.; Ju, P.; Cui, L.; Ai, S. Acetylcholinesterase biosensor based on 3-carboxyphenylboronic acid/reduced graphene oxide–gold nanocomposites modified electrode for amperometric detection of organophosphorus and carbamate pesticides. Sensors Actuators B: Chem. 2011, 160, 1255–1261. [CrossRef]
  88. Sahin, A.; Dooley, K.; Cropek, D.M.; West, A.C.; Banta, S. A dual enzyme electrochemical assay for the detection of organophosphorus compounds using organophosphorus hydrolase and horseradish peroxidase. Sensors Actuators B: Chem. 2011, 158, 353–360. [CrossRef]
  89. Wang, C.; Wu, Q.; Wu, C.; Wang, Z. Determination of some organophosphorus pesticides in water and watermelon samples by microextraction prior to high-performance liquid chromatography. J. Sep. Sci. 2011, 34, 3231–3239. [CrossRef]
  90. Zhang, X.; Wang, H.; Yang, C.; Du, D.; Lin, Y. Preparation, characterization of Fe3O4 at TiO2 magnetic nanoparticles and their application for immunoassay of biomarker of exposure to organophosphorus pesticides. Biosens. Bioelectron. 2013, 41, 669–674. [CrossRef]
  91. Jarrard, H.; Delaney, K.; Kennedy, C. Impacts of carbamate pesticides on olfactory neurophysiology and cholinesterase activity in coho salmon (Oncorhynchus kisutch). Aquat. Toxicol. 2004, 69, 133–148. [CrossRef]
  92. Pulgarín, J.A.M.; Bermejo, L.F.G.; Durán, A.C. Determination of carbamates in soils by liquid chromatography coupled with on-line postcolumn UV irradiation and chemiluminescence detection. Arab. J. Chem. 2018, 13, 2778–2784. [CrossRef]
  93. Yu, R.; Liu, Q.; Liu, J.; Wang, Q.; Wang, Y. Concentrations of organophosphorus pesticides in fresh vegetables and related human health risk assessment in Changchun, Northeast China. Food Control. 2016, 60, 353–360. [CrossRef]
  94. Beland, F.A.; Benson, R.W.; Mellick, P.W.; Kovatch, R.M.; Roberts, D.W.; Fang, J.-L.; Doerge, D.R. Effect of ethanol on the tumorigenicity of urethane (ethyl carbamate) in B6C3F1 mice. Food Chem. Toxicol. 2005, 43, 1–19. [CrossRef]
  95. Tu, Q.; Qi, W.; Zhao, J.; Zhang, L.; Guo, Y. Quantification ethyl carbamate in wines using reaction-assisted-extraction with 9-xanthydrol and detection by heart-cutting multidimensional gas chromatography-mass spectrometry. Anal. Chim. Acta 2018, 1001, 86–92. [CrossRef]
  96. Mishra, S.; Zhang, W.; Lin, Z.; Pang, S.; Huang, Y.; Bhatt, P.; Chen, S. Carbofuran toxicity and its microbial degradation in contaminated environments. Chemosphere 2020, 259, 127419. [CrossRef]
  97. Chang, N.-B.; Mani, S.; Parvathinathan, G.; Kanth, R.S. Pesticide Impact Assessment via Using Enzyme-linked Immunosorbent Assay (ELISA) Technique in the Lower Rio Grande River Basin, Texas. Water Qual. Expo. Heal. 2009, 1, 145–158. [CrossRef]
  98. Kim, J.H.; Park, J.-M.; Choi, G.-H.; Kim, Y.-W. Development of easy and efficient methods for quantitative analysis of ethyl carbamate using GC-MS in various fermented foods. Food Sci. Biotechnol. 2013, 22, 599–603. [CrossRef]
  99. Silva, .A.S.; Lopez-Avila, V.; Pawliszyn, J. Fast and robust direct immersion solid phase microextraction coupled with gas chromatography–time-of-flight mass spectrometry method employing a matrix compatible fiber for determination of triazole fungicides in fruits. J. Chromatogr. A 2013, 1313, 139–146. [CrossRef]
  100. Valente, I.M.; Ramos, R.M.; Gonçalves, L.M.; Rodrigues, J.A. Determination of ethyl carbamate in spirits using salting-out assisted liquid–liquid extraction and high performance liquid chromatography with fluorimetric detection. Anal. Methods 2014, 6, 9136–9141. [CrossRef]
  101. Zhu, Y.; Wang, M.; Zhang, X.; Cao, J.; She, Y.; Cao, Z.; Wang, J.; El-Aty, A.M.A. Acetylcholinesterase Immobilized on Magnetic Mesoporous Silica Nanoparticles Coupled with Fluorescence Analysis for Rapid Detection of Carbamate Pesticides. ACS Appl. Nano Mater. 2022, 5, 1327–1338. [CrossRef]
  102. Zeng, S.; Sun, X.; Wan, X.; Qian, C.; Yue, W.; Sohan, A.S.M.M.F.; Lin, X.; Yin, B. A cascade Fermat spiral microfluidic mixer chip for accurate detection and logic discrimination of cancer cells. Anal. 2022, 147, 3424–3433. [CrossRef]
  103. Yin, B.F.; Wan, X.H.; Sohan, A.; Lin, X.D. Microfluidics-Based POCT for SARS-CoV-2 Diagnostics. Micromachines 2022, 13, 1238.
  104. Yin, B.; Qian, C.; Wan, X.; Sohan, A.M.F.; Lin, X. Tape integrated self-designed microfluidic chip for point-of-care immunoassays simultaneous detection of disease biomarkers with tunable detection range. Biosens. Bioelectron. 2022, 212, 114429. [CrossRef]
  105. Chen, H.; Hu, O.; Fan, Y.; Xu, L.; Zhang, L.; Lan, W.; Hu, Y.; Xie, X.; Ma, L.; She, Y.; et al. Fluorescence paper-based sensor for visual detection of carbamate pesticides in food based on CdTe quantum dot and nano ZnTPyP. Food Chem. 2020, 327, 127075. [CrossRef]
  106. Yan, X.; Kong, D.; Jin, R.; Zhao, X.; Li, H.; Liu, F.; Lin, Y.; Lu, G. Fluorometric and colorimetric analysis of carbamate pesticide via enzyme-triggered decomposition of Gold nanoclusters-anchored MnO2 nanocomposite. Sensors Actuators B: Chem. 2019, 290, 640–647. [CrossRef]
  107. Bacchin, P.; Snisarenko, D.; Stamatialis, D.; Aimar, P.; Causserand, C. Combining fluorescence and permeability measurements in a membrane microfluidic device to study protein sorption mechanisms. J. Membr. Sci. 2020, 614, 118485. [CrossRef]
  108. Cao, Y.; Ye, C.; Zhang, C.; Zhang, G.; Hu, H.; Zhang, Z.; Fang, H.; Zheng, J.; Liu, H. Simultaneous detection of multiple foodborne bacteria by loop-mediated isothermal amplification on a microfluidic chip through colorimetric and fluorescent assay. Food Control. 2021, 134, 118485. [CrossRef]
  109. Habibey, R. Incubator-independent perfusion system integrated with microfluidic device for continuous electrophysiology and microscopy readouts. Biofabrication 2023, 15, 118485. [CrossRef]
  110. Muluneh, M.; Kim, B.; Buchsbaum, G.; Issadore, D. Miniaturized, multiplexed readout of droplet-based microfluidic assays using time-domain modulation. Lab a Chip 2014, 14, 4638–4646. [CrossRef]
  111. Peric, O.; Hannebelle, M.; Adams, J.D.; Fantner, G.E. Microfluidic bacterial traps for simultaneous fluorescence and atomic force microscopy. Nano Res. 2017, 10, 3896–3908. [CrossRef]
  112. Yin, B.; Yue, W.; Sohan, A.S.M.M.F.; Zhou, T.; Qian, C.; Wan, X. Micromixer with Fine-Tuned Mathematical Spiral Structures. ACS Omega 2021, 6, 30779–30789. [CrossRef]
  113. Yin, B.; Wan, X.; Qian, C.; Sohan, A.S.M.M.F.; Zhou, T.; Yue, W. Enzyme Method-Based Microfluidic Chip for the Rapid Detection of Copper Ions. Micromachines 2021, 12, 1380. [CrossRef]
  114. Yin, B.; Wan, X.; Qian, C.; Sohan, A.S.M.M.F.; Wang, S.; Zhou, T. Point-of-Care Testing for Multiple Cardiac Markers Based on a Snail-Shaped Microfluidic Chip. Front. Chem. 2021, 9, 741058. [CrossRef]
  115. Huang, D.; Gao, L.; Zhu, S.; Qiao, L.; Liu, Y.; Ai, Q.; Xu, C.; Wang, W.; Lu, M.; Zheng, M. Target and non-target analysis of organochlorine pesticides and their transformation products in an agrochemical-contaminated area. Chemosphere 2023, 324, 138314. [CrossRef]
  116. Potapowicz, J.; Lambropoulou, D.; Nannou, C.; Kozioł, K.; Polkowska, Z. Occurrences, sources, and transport of organochlorine pesticides in the aquatic environment of Antarctica. Sci. Total. Environ. 2020, 735, 139475. [CrossRef]
  117. Kafaei, R.; Arfaeinia, H.; Savari, A.; Mahmoodi, M.; Rezaei, M.; Rayani, M.; Sorial, G.A.; Fattahi, N.; Ramavandi, B. Organochlorine pesticides contamination in agricultural soils of southern Iran. Chemosphere 2020, 240, 124983. [CrossRef]
  118. Tsygankov, V.Y. Organochlorine pesticides in marine ecosystems of the Far Eastern Seas of Russia (2000–2017). Water Res. 2019, 161, 43–53. [CrossRef]
  119. Taiwo, A.M. A review of environmental and health effects of organochlorine pesticide residues in Africa. Chemosphere 2019, 220, 1126–1140. [CrossRef]
  120. Xu, T.; Miao, J.; Chen, Y.; Yin, D.; Hu, S.; Sheng, G.D. The long-term environmental risks from the aging of organochlorine pesticide lindane. Environ. Int. 2020, 141, 105778. [CrossRef]
  121. Fang, J.; Liu, H.; Zhao, H.; Wong, M.; Xu, S.; Cai, Z. Association of prenatal exposure to organochlorine pesticides and birth size. Sci. Total. Environ. 2019, 654, 678–683. [CrossRef]
  122. Thomas, A.; Toms, L.-M.L.; Harden, F.A.; Hobson, P.; White, N.M.; Mengersen, K.L.; Mueller, J.F. Concentrations of organochlorine pesticides in pooled human serum by age and gender. Environ. Res. 2017, 154, 10–18. [CrossRef]
  123. Heys, K.A.; Shore, R.F.; Pereira, M.G.; Martin, F.L. Levels of Organochlorine Pesticides Are Associated with Amyloid Aggregation in Apex Avian Brains. Environ. Sci. Technol. 2017, 51, 8672–8681. [CrossRef]
  124. Rodríguez, .G.P.; López, M.I.R.; Casillas, T..D.; León, J.A.A.; Mahjoub, O.; Prusty, A.K. Monitoring of organochlorine pesticides in blood of women with uterine cervix cancer. Environ. Pollut. 2017, 220, 853–862. [CrossRef]
  125. Malik, R.N.; Rauf, S.; Mohammad, A.; Eqani, S.-A.S.; Ahad, K. Organochlorine residual concentrations in cattle egret from the Punjab Province, Pakistan. Environ. Monit. Assess. 2011, 173, 325–341. [CrossRef]
  126. Chowdhury, M.A.Z.; Fakhruddin, A.; Islam, N.; Moniruzzaman, M.; Gan, S.H.; Alam, K. Detection of the residues of nineteen pesticides in fresh vegetable samples using gas chromatography–mass spectrometry. Food Control. 2013, 34, 457–465. [CrossRef]
  127. Tang, W.; Wang, D.; Wang, J.; Wu, Z.; Li, L.; Huang, M.; Xu, S.; Yan, D. Pyrethroid pesticide residues in the global environment: An overview. Chemosphere 2018, 191, 990–1007. [CrossRef]
  128. Bajunirwe, F. Pyrethroid resistance in sub-Saharan Africa. Lancet 2020, 395, 1236–1237. [CrossRef]
  129. Stellman, S.D.; Stellman, J.M. Pyrethroid Insecticides—Time for a Closer Look. JAMA Intern. Med. 2020, 180, 374-375. [CrossRef]
  130. Larsen, D.A. Exposure to Pyrethroids and Health Risk. JAMA Intern. Med. 2020, 180, 1027-1028. [CrossRef]
  131. (Halcomb), S.E.D.; Cole, M.; Sheets, L. Exposure to Pyrethroids and Health Risk. JAMA Intern. Med. 2020, 180, 1028. [CrossRef]
  132. Ullah, S.; Li, Z.; Zuberi, A.; Arifeen, M.Z.U.; Baig, M.M.F.A. Biomarkers of pyrethroid toxicity in fish. Environ. Chem. Lett. 2019, 17, 945–973. [CrossRef]
  133. Yang, Y.; Wu, N.; Wang, C. Toxicity of the pyrethroid bifenthrin insecticide. Environ. Chem. Lett. 2018, 16, 1377–1391. [CrossRef]
  134. Yin, B.; Qian, C.; Wang, S.; Wan, X.; Zhou, T. A Microfluidic Chip-Based MRS Immunosensor for Biomarker Detection via Enzyme-Mediated Nanoparticle Assembly. Front. Chem. 2021, 9, 688442. [CrossRef]
  135. MycoLab™ Afla-Quick™. Available online: https://www.envirologix.com/mycolab-afla-quick (accessed on 30th June).
  136. Raptor™ Integrated Analysis Platform. Available online: https://advancedanimaldiagnostics.com/products/raptor-platform (accessed on 25th June).
  137. Página no encontrada - Certest Biotec - Raw Materials | Diagnostics | Pharma. Available online: https://www.certest.es/en/products/pesticide-detect (accessed on 30th June).
  138. ToxiQuant™ Pesticide Microarray Kit. Available online: https://www.biosensingusa.com/products/pesticide-microarray (accessed on 27th June).
  139. SERS-based Portable Microfluidic Sensor. Available online: https://www.gbctechtraining.com/product_list/sensors.aspx (accessed on 27th June).
  140. iTube. Available online: https://ayanda-biosystems.com/itube-technology/ (accessed on 30th June).
  141. RapidChek® SELECT™ Salmonella. Available online: https://www.romerlabs.com/foodsafety/rapidchek-select-salmonella/ (accessed on 26th June).
  142. BioFlash Biological Identifier - Mobile, high sensitivity biothreat detector | Smiths Detection. Available online: https://www.smithsdetection.com/products/bioflash-biological-identifier/ (accessed on 30th June).
  143. MELISA-45 System. Available online: https://www.ibistech.net/products/melisa-45/ (accessed on 25th June).
  144. ATHENA Integrated System. Available online: https://www.cabn.manchester.ac.uk/research/focus-areas/nanotechnology/ (accessed on 27th June).
Preprints 78760 sch001
Scheme 1. Microfluidic technology-based schematic diagram for the detection of food pesticide residues.
Scheme 1. Microfluidic technology-based schematic diagram for the detection of food pesticide residues.
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Figure 1. Schematic diagram of organophosphorus fluorescence detection based on enzyme-inhibited recovery reaction. (Reprinted/adapted with permission from Ref.[23].Copyright 2019 Biosensors & Bioelectronics).
Figure 1. Schematic diagram of organophosphorus fluorescence detection based on enzyme-inhibited recovery reaction. (Reprinted/adapted with permission from Ref.[23].Copyright 2019 Biosensors & Bioelectronics).
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Figure 2. (A) Schematic diagram of 2D μPAD. (B) Fluorescence detection image. (C) Schematic diagram of 3D μPAD. (Reprinted/adapted with permission from Ref.[52]. Copyright 2023 Biosensors & Bioelectronics.).
Figure 2. (A) Schematic diagram of 2D μPAD. (B) Fluorescence detection image. (C) Schematic diagram of 3D μPAD. (Reprinted/adapted with permission from Ref.[52]. Copyright 2023 Biosensors & Bioelectronics.).
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Figure 3. (A) Schematic diagram of a microfluidic device. (B) Solution mixing procedure diagram. (a) The first step is to introduce the reaction solution into the mainstream channel. (b-d) Measure the volume using the auxiliary runner and discard the main runner section. (e) The two solutions merge in the main channel. (f) Transport the new plug to the sensing area. (Reprinted/adapted with permission from Ref.[53]. Copyright 2014 Sensors and Actuators B-Chemical).
Figure 3. (A) Schematic diagram of a microfluidic device. (B) Solution mixing procedure diagram. (a) The first step is to introduce the reaction solution into the mainstream channel. (b-d) Measure the volume using the auxiliary runner and discard the main runner section. (e) The two solutions merge in the main channel. (f) Transport the new plug to the sensing area. (Reprinted/adapted with permission from Ref.[53]. Copyright 2014 Sensors and Actuators B-Chemical).
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Figure 4. (A) Schematic diagram of μPAD when pesticides are added. (B) Schematic diagram of μPAD in the absence of pesticides. (C) Design drawing of μPAD. (Reprinted/adapted with permission from Ref.[54]. Copyright 2020 Talanta).
Figure 4. (A) Schematic diagram of μPAD when pesticides are added. (B) Schematic diagram of μPAD in the absence of pesticides. (C) Design drawing of μPAD. (Reprinted/adapted with permission from Ref.[54]. Copyright 2020 Talanta).
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Figure 5. (A) Internal structure diagram of microfluidic chip. (B) Schematic diagram of the detection principle. (C) Schematic diagram of a 3D printed four-layer microfluidic chip (D) Schematic diagram of the device used to inspect the chip. (Reprinted/adapted with permission from Ref.[22]. Copyright 2021 Sensors and Actuators B-Chemical).
Figure 5. (A) Internal structure diagram of microfluidic chip. (B) Schematic diagram of the detection principle. (C) Schematic diagram of a 3D printed four-layer microfluidic chip (D) Schematic diagram of the device used to inspect the chip. (Reprinted/adapted with permission from Ref.[22]. Copyright 2021 Sensors and Actuators B-Chemical).
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Figure 6. (A) Schematic diagram of the detection principle of acetylcholinesterase inhibition based on electrochemical method. (B) Schematic diagram of pesticide testing system. (Reprinted/adapted with permission from Ref.[56]. Copyright 2013 Analytica Chimica Acta).
Figure 6. (A) Schematic diagram of the detection principle of acetylcholinesterase inhibition based on electrochemical method. (B) Schematic diagram of pesticide testing system. (Reprinted/adapted with permission from Ref.[56]. Copyright 2013 Analytica Chimica Acta).
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Figure 7. (A)Schematic diagram of the equipment. (B) Schematic diagram of fluorescence detection. (Reprinted/adapted with permission from Ref.[57]. Copyright 2022 Food Chemistry).
Figure 7. (A)Schematic diagram of the equipment. (B) Schematic diagram of fluorescence detection. (Reprinted/adapted with permission from Ref.[57]. Copyright 2022 Food Chemistry).
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Figure 8. (A) Schematic diagram of pyrethroid hydrolysis. (B) Schematic diagram of the detection of paper-based microfluidic device (C) Schematic diagram of color reaction with pyrethroid. (D) Design and dimensioning of the device. (Reprinted/adapted with permission from Ref.[58]. Copyright 2020 Sensors).
Figure 8. (A) Schematic diagram of pyrethroid hydrolysis. (B) Schematic diagram of the detection of paper-based microfluidic device (C) Schematic diagram of color reaction with pyrethroid. (D) Design and dimensioning of the device. (Reprinted/adapted with permission from Ref.[58]. Copyright 2020 Sensors).
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Table 1. Representative Microfluidics device for rapid detection of pesticide residues in food.
Table 1. Representative Microfluidics device for rapid detection of pesticide residues in food.
Pesticide type Detected
pesticides		 Characteristic of the microflfluidic device
and the analysis		 Real sample Ref.
Organophosphorus Paraoxon LOD: 0.38 pM
Assay time: 5 min
QD-AChEaerogel based microfluidic arrays sensor
Linear range: 10-12-10-5 M		 Apple [23]
DDVP LOD: 1.0 μg L-1
Assay time: 30 min
Threaded 3D microfluidic paper analytical device-based sensor
Linear range:2.5-120 μg L-1 		Spinach
Tomato		 [52]
Malathion LOD:33 nM
Assay time:7 min
A coulometric microdevice based on plug-based microfluidics
Linear range: 10−10-10−2 M		 Waters [53]
Carbamate Chlorpyrifos LOD: 2.0 μg L-1
Assay time:35 min
Bioactive microfluidic paper device
Linear range:2.0-45 μg L-1 		Waters [54]
Carbendazim LOD: 3.102 ppb
Assay time:12 min
3D-printed microfluidic nanosensors
Linear range: 0.01-10 ppm		 Cabbage [22]
Carbofuran LOD: 0.9 μM
Assay time:6min
Electrochemical microfluidics based on carbon black nanoparticles
Linear range: 25-125 μM		 Waters [55]
Carbaryl LOD: 15.6 μM
Assay time:6 min
A simple but robust droplet-based microfluidic system
Linear range: 15.6-21.8 μM		 Waters [56]
Organochlorine Dicofol LOD: 200 pbb
Assay time:20 min
A paper based microfluidic device modified by PTES
Linear range: 0-10 ppm		 Tea [57]
Pyrethroid Cypermethrin LOD:2.5 µg/mL
Assay time:6min
A Microfluidic Paper-Based Analytical Device
Linear range: 2-15 µg/mL		 Waters [58]
Table 2. Commercialized products for pesticide testing.
Table 2. Commercialized products for pesticide testing.
Product Name Manufacturer Detected pesticides Types of Microfluidics technology Time Target Sensitivity Storage and Stability Ref.
MycoLab™ Afla-Quick™ EnviroLogix Inc Aflatoxins Lateral Flow Immunoassay (LFIA) 10 min Aflatoxins in various food samples Detects aflatoxin B1, B2, G1, G2 at low ppb levels Stable at room temperature, shelf life of 12 months [135]
Raptor™ Integrated Analysis Platform Advanced Animal Diagnostics (AAD) Various pesticides (customizable) Microfluidic Immunoassay 30 min Pesticides in food and feed samples Customizable to different pesticide targets Stable at room temperature, shelf life of 18 months [136]
Pesticide Detect™ CerTest Biotec Various pesticides (customizable) Microfluidic Immunochromatography 10 min Pesticides in food and water samples Customizable to different pesticide targets Stable at room temperature, shelf life of 12 months [137]
ToxiQuant™ Pesticide Microarray Kit Biosensing Instrument Inc. Various pesticides (customizable) Microarray-based detection 2-3 hours Pesticides in food and environmental samples Customizable to different pesticide targets Stable at refrigeration temperature, shelf life of 6 months [138]
SERS-based Portable Microfluidic Sensor GBC Scientific Equipment Various pesticides (customizable) Surface-Enhanced Raman Spectroscopy (SERS) minutes Pesticides in food and water samples Customizable to different pesticide targets Stable at room temperature, dependent on the instrument [139]
iTube Ayanda Biosystems Various pesticides (customizable) Smartphone-based colorimetric assay minutes Pesticides in food samples Customizable to different pesticide targets Stable at room temperature, dependent on the smartphone [140]
RapidChek® SELECT™ Salmonella Romer Labs Various pesticides (customizable) Lateral Flow Immunoassay (LFIA) 15 min Pesticides in food samples Customizable to different pesticide targets Stable at room temperature, shelf life of 12 months [141]
BioFlash Biological Identifier Smiths Detection Various pesticides (customizable) Immunomagnetic Separation (IMS) minutes Pesticides in food and environmental samples Customizable to different pesticide targets Stable at room temperature, dependent on the instrument [142]
MELISA-45 System IBIS Technologies Various pesticides (customizable) Mass Spectrometry minutes Pesticides in food and environmental samples Customizable to different pesticide targets Stable at room temperature, dependent on the instrument [143]
ATHENA Integrated System Centre for Advanced BioNano Systems (Australia) Various pesticides (customizable) Lab-on-a-chip minutes Pesticides in food samples Customizable to different pesticide targets Stable at room temperature, dependent on the instrument [144]
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