A detection method of two carbamate pesticides residues on tomatoes utilizing excitation-emission matrix fluorescence technique

Application and Progress of Machine Learning in Pesticide Hazard and Risk Assessment

Long-term exposure to pesticides is associated with the incidence of cancer. With the exponential increase in the number of new pesticides being synthesized, it becomes more and more important to evaluate the toxicity of pesticides by means of simulated calculations. Based on existing data, machine learning methods can train and model the predictions of the effects of novel pesticides, which have limited available data. Combined with other technologies, this can aid the synthesis of new pesticides with specific active structures, detect pesticide residues, and identify their tolerable exposure levels. This article mainly discusses support vector machines, linear discriminant analysis, decision trees, partial least squares, and algorithms based on feedforward neural networks in machine learning. It is envisaged that this article will provide scientists and users with a better understanding of machine learning and its application prospects in pesticide toxicity assessment.

Colorimetric and ratiometric supramolecular AIE fluorescent probe for the on-site monitoring of fipronil

The overuse of fipronil (FPN, a broad-spectrum insecticide) in agriculture has brought great concerns for environmental pollution and food safety. The development of a rapid, reliable, and portable analytical method for the on-site monitoring of FPN is therefore of great significance but is full of challenge. Herein, a novel supramolecular probe using human serum albumin (HSA) as the host and an aggregation-induced emission-active fluorescence probe LIQ-TPA-TZ as the guest was developed for the colorimetric and ratiometric detection of FPN, displaying fast response (30 s), high sensitivity (LOD ∼ 0.05 μM), and good selectivity and anti-interference performance. Moreover, portable paper-based test strips could be facilely obtained and utilized for the determination of FPN, showing colorimetric changes from yellow to orange. This supramolecular probe also demonstrated great potential in real applications for choosing the best cleaning method to reduce the residue rate of FPN on apples. This study provides a versatile tool for the fast and real-time analysis of FPN, which greatly benefits the on-site determination of pesticides with the use of simple testing apparatus.

Prediction of microseismic events in rock burst mines based on MEA-BP neural network

Microseismic monitoring is an important tool for predicting and preventing rock burst incidents in mines, as it provides precursor information on rock burst. To improve the prediction accuracy of microseismic events in rock burst mines, the working face of the Hegang Junde coal mine is selected as the research object, and the research data will consist of the microseismic monitoring data from this working face over the past 4 years, adopts expert system and temporal energy data mining method to fuse and analyze the mine pressure manifestation regularity and microseismic data, and the "noise reduction" data model is established. By comparing the MEA-BP and traditional BP neural network models, the results of the study show that the prediction accuracy of the MEA-BP neural network model was higher than that of the BP neural network. The absolute and relative errors of the MEA-BP neural network were reduced by 247.24 J and 46.6%, respectively. Combined with the online monitoring data of the KJ550 rock burst, the MEA-BP neural network proved to be more effective in microseismic energy prediction and improved the accuracy of microseismic event prediction in rock burst mines.

Current electroanalytical approaches in the carbamates and dithiocarbamates determination

Pesticides are a suitable tool for controlling plagues and disease vectors. However, their inappropriate use allows for contamination of the environment, soil, water, and foods. Carbamates and dithiocarbamates pesticides present accumulative effects in the human body resulting in hormonal, neurological and reproductive disorders, and some are still suspected or proven to give carcinogenic or mutagenic effects. This review provides a current electroanalytical approach in the carbamates and dithiocarbamates determination, showing the use of voltammetric techniques such as amperometry, cyclic and linear scan, differential pulse, and square wave voltammetry, indicating their advantages, disadvantages, and perspectives in electroanalytical detection of carbamates and dithiocarbamates in natural water and foods. Also are reported the different materials used in the preparation of working electrodes since their choice has an important impact on the success of the analytical applications, resulting in suitable sensitivity, selectivity, stability, and robustness.

Nondestructive testing methods for pesticide residue in food commodities: A review

Pesticides play an important role in increasing the overall yield and productivity of agricultural foods by controlling pests, insects, and numerous plant-related diseases. However, the overuse of pesticides has resulted in pesticide contamination of food products and water bodies, as well as disruption of ecological and environmental systems. Global health authorities have set limits for pesticide residues in individual food products to ensure the availability of safe foods in the supply system and to assist farmers in developing the best agronomic practices for crop production. Therefore, the use of nondestructive testing (NDT) methods for pesticide residue detection is gaining interest in the food supply chain. The NDT techniques have several advantages, such as simultaneous measurement of chemical and physical characteristics of food without destroying the product. Although numerous studies have been conducted on NDT for pesticide residue in agro-food products, there are still challenges in real-time implementation. Further study on NDT methods is needed to establish their potential for supplementing existing methods, identifying mixed pesticides, and performing volumetric quantification (not surface accumulation alone).

Detection of captan residues in apple juice using fluorescence spectroscopy combined with a genetic algorithm and support vector machines

The captan residues in apple juice were detected by fluorescence spectrometry, and the content level of captan was predicted based on a genetic algorithm and support vector machines (GA-SVMs). According to the captan concentration in apple juice, the experimental samples were divided into four levels, including no excess, slight excess, moderate excess, and severe excess. A GA was used to select the characteristic wavelength and optimize SVM parameters, and SVM was applied to train the classification model. 50 characteristic wavelength points were selected from the original fluorescence spectra, which contained 401 wavelength points, and the classification accuracy of the training set and test set is 99.02% and 100%, respectively, which is higher than the traditional PLS method. The results show that a GA can effectively select the feature wavelengths, and an SVM model can accurately predict the content level of captan residues. A fast and non-destructive analysis method, combined with a GA and SVM based on fluorescence spectroscopy, was realized.