Deep Learning-Based Multicapturer SERS Platform on Plasmonic Nanocube Metasurfaces for Multiplex Detection of Organophosphorus Pesticides in Environmental Water

Non-linear responses via agglomeration and aggregation of gold nanoparticles for surface-enhanced Raman spectroscopy (SERS) coupled with chemometric analysis for chlorpyrifos detection

This study investigates the behaviour of gold nanoparticles (AuNPs) when exposed to chlorpyrifos, an agricultural pesticide, and its application in detecting the pesticide via surface-enhanced Raman spectroscopy (SERS). Under synergistic addition of NaCl, AuNPs undergo agglomeration at lower chlorpyrifos concentrations but aggregation at higher concentrations, resulting in a distinctive nonlinear SERS response. A linear relationship is obtained between 0.001 and 1 ppm with detection limit (LOD) of 0.009 ppm, while an inverse response is observed at higher concentrations (1-1000 ppm) with a LOD of 1 ppm. Combining the colorimetric response of AuNP solutions, their absorbance spectra, and principal component analysis can improve detection reliability. The assay, coupled with a simple recovery method using acetonitrile swabbing, achieves high reproducibility in detecting chlorpyrifos in cucumber, even at concentrations as low as 0.11 ppm. This approach can be tailored for various chlorpyrifos concentrations not only in cucumbers but also in different food matrices.

Smartphone-assisted colorimetric biosensor for the determination of organophosphorus pesticides on the peel of fruits

Nowadays, the widespread use of organophosphorus pesticides (OPs) in agricultural production leads to varying degrees of residues in crops, which pose a potential threat to human health. Conventional methods used in national standard for the detection of OPs in fruits and vegetables require expensive instruments or cumbersome sample pretreatment steps for the analysis. To address these challenges, in this work, we took advantage of the peroxidase-like activity of PtCu3 alloy nanocrystals (NCs) for a colorimetric and smartphone assisted sensitive detection of OPs. With the assist of a smartphone, the concentration of OPs on the peel of fruits could be obtained by comparing the B/RG value (the brightness value of blue divided by those of red and green) of a test strip with a calibration curve. This work not only provides a facile and cost-effective method to detect pesticides but also makes a positive contribution to food safety warning.

Integrating Wireless Remote Sensing and Sensors for Monitoring Pesticide Pollution in Surface and Groundwater

Water constitutes an indispensable resource crucial for the sustenance of humanity, as it plays an integral role in various sectors such as agriculture, industrial processes, and domestic consumption. Even though water covers 71% of the global land surface, governments have been grappling with the challenge of ensuring the provision of safe water for domestic use. A contributing factor to this situation is the persistent contamination of available water sources rendering them unfit for human consumption. A common contaminant, pesticides are not frequently tested for despite their serious effects on biodiversity. Pesticide determination in water quality assessment is a challenging task because the procedures involved in the extraction and detection are complex. This reduces their popularity in many monitoring campaigns despite their harmful effects. If the existing methods of pesticide analysis are adapted by leveraging new technologies, then information concerning their presence in water ecosystems can be exposed. Furthermore, beyond the advantages conferred by the integration of wireless sensor networks (WSNs), the Internet of Things (IoT), Machine Learning (ML), and big data analytics, a notable outcome is the attainment of a heightened degree of granularity in the information of water ecosystems. This paper discusses methods of pesticide detection in water, emphasizing the possible use of electrochemical sensors, biosensors, and paper-based sensors in wireless sensing. It also explores the application of WSNs in water, the IoT, computing models, ML, and big data analytics, and their potential for integration as technologies useful for pesticide monitoring in water.

Porous carbon film/WO3-x nanosheets based SERS substrate combined with deep learning technique for molecule detection

The Surface-enhanced Raman scattering (SERS) is an attractive optical detecting method with high sensitivity and detectivity, however challenges on large-area signal uniformity and complex spectra analysis methods always retards its wide application. Herein, a highly sensitive and uniform SERS detection strategy supported by porous carbon film/WO3-x nanosheets (PorC/WO3-x) based noble-metal-free SERS substrate and deep learning algorithm are reported. Experimentally, the PorC/WO3-x substrate was prepared by high-temperature annealing the PorC/WO3 films under the argon atmosphere. The defect density of the WO3 was controlled by tuning the reducing reaction time during the annealing process. The SERS performance was evaluated by using R6G as the Raman reporter, it showed that the SERS intensity obtained on the substrate with the optimal annealing time of 3 h was about 8 times as high as that obtained on the PorC/WO3 substrate without annealing treatment. And detection limit of 10-7 M and Raman enhancement factor of 106 could be achieved. Moreover, the above optimal SERS substrate was utilized to detect flavonoids of quercetin, 3-hydroxyflavone and flavone, and a deep learning algorithms was incorporated to identify the quercetin. It revealed that quercetin can be accurately detected within the above flavonoids, and lowest detectable concentration of 10-5 M can be achieved.

Breaking boundaries: Artificial intelligence for pesticide detection and eco-friendly degradation

Pesticides are extensively used agrochemicals across the world to control pest populations. However, irrational application of pesticides leads to contamination of various components of the environment, like air, soil, water, and vegetation, all of which build up significant levels of pesticide residues. Further, these environmental contaminants fuel objectionable human toxicity and impose a greater risk to the ecosystem. Therefore, search of methodologies having potential to detect and degrade pesticides in different environmental media is currently receiving profound global attention. Beyond the conventional approaches, Artificial Intelligence (AI) coupled with machine learning and artificial neural networks are rapidly growing branches of science that enable quick data analysis and precise detection of pesticides in various environmental components. Interestingly, nanoparticle (NP)-mediated detection and degradation of pesticides could be linked to AI algorithms to achieve superior performance. NP-based sensors stand out for their operational simplicity as well as their high sensitivity and low detection limits when compared to conventional, time-consuming spectrophotometric assays. NPs coated with fluorophores or conjugated with antibody or enzyme-anchored sensors can be used through Surface-Enhanced Raman Spectrometry, fluorescence, or chemiluminescence methodologies for selective and more precise detection of pesticides. Moreover, NPs assist in the photocatalytic breakdown of various organic and inorganic pesticides. Here, AI models are ideal means to identify, classify, characterize, and even predict the data of pesticides obtained through NP sensors. The present study aims to discuss the environmental contamination and negative impacts of pesticides on the ecosystem. The article also elaborates the AI and NP-assisted approaches for detecting and degrading a wide range of pesticide residues in various environmental and agrecultural sources including fruits and vegetables. Finally, the prevailing limitations and future goals of AI-NP-assisted techniques have also been dissected.

Sensitive fluorescent detection of phosmet and chlortetracycline in animal-derived food samples based on a water-stable Cd(II) chain-based zwitterionic metal-organic framework

The residues of pesticides and antibiotics have always been a major concern in agriculture and food safety. In order to provide a new method for the rapid detection of organophosphorus pesticides and antibiotics, a novel Cd(II) chain-based zwitterionic metal-organic framework MOF 1 with high sensitivity fluorescence sensing performance was successfully synthesized. A series of researches showed that the water- and pH-stable bifunctional MOF 1 has a great ability to detect phosmet (PSM) and chlortetracycline (CTC) in water through fluorescence quenching effect, with high detection sensitivity, low detection limits (0.0124 μM and 0.0131 μM), short response time (40 s) and reusability. Practical application results revealed that MOF 1 could detect PSM and CTC in milk, beef, chicken and egg samples, with satisfactory recoveries (95.2%-103.7%). As a novel fluorescence probe, MOF 1, is known the first case that can detect PSM in animal-derived samples, and the first dual-function material capable of detecting PSM and CTC. Mechanism studies displayed that competitive absorption and photoinduced electron transfer clearly authenticate the high quenching performance of the material.

Marker-Free Isoelectric Focusing Patterns for Identification of Meat Samples via Deep Learning

Isoelectric focusing (IEF) is a powerful tool for resolving complex protein samples, which generates IEF patterns consisting of multiplex analyte bands. However, the interpretation of IEF patterns requires the careful selection of isoelectric point (pI) markers for profiling the pH gradient and a trivial process of pI labeling, resulting in low IEF efficiency. Here, we for the first time proposed a marker-free IEF method for the efficient and accurate classification of IEF patterns by using a convolutional neural network (CNN) model. To verify our method, we identified 21 meat samples whose IEF patterns comprised different bands of meat hemoglobin, myoglobin, and their oxygen-binding variants but no pI marker. Thanks to the high throughput and short assay time of the microstrip IEF, we efficiently collected 1449 IEF patterns to construct the data set for model training. Despite the absence of pI markers, we experimentally introduced the severe pH gradient drift into 189 IEF patterns in the data set, thereby omitting the need for profiling the pH gradient. To enhance the model robustness, we further employed data augmentation during the model training to mimic pH gradient drift. With the advantages of simple preprocessing, a rapid inference of 50 ms, and a high accuracy of 97.1%, the CNN model outperformed the traditional algorithm for simultaneously identifying meat species and cuts of meat of 105 IEF patterns, suggesting its great potential of being combined with microstrip IEF for large-scale IEF analyses of complicated protein samples.

Multiplex Surface-Enhanced Raman Scattering: An Emerging Tool for Multicomponent Detection of Food Contaminants

For survival and quality of human life, the search for better ways to ensure food safety is constant. However, food contaminants still threaten human health throughout the food chain. In particular, food systems are often polluted with multiple contaminants simultaneously, which can cause synergistic effects and greatly increase food toxicity. Therefore, the establishment of multiple food contaminant detection methods is significant in food safety control. The surface-enhanced Raman scattering (SERS) technique has emerged as a potent candidate for the detection of multicomponents simultaneously. The current review focuses on the SERS-based strategies in multicomponent detection, including the combination of chromatography methods, chemometrics, and microfluidic engineering with the SERS technique. Furthermore, recent applications of SERS in the detection of multiple foodborne bacteria, pesticides, veterinary drugs, food adulterants, mycotoxins and polycyclic aromatic hydrocarbons are summarized. Finally, challenges and future prospects for the SERS-based detection of multiple food contaminants are discussed to provide research orientation for further.