Pesticide decontamination using UV/ferrous-activated persulfate with the aid neuro-fuzzy modeling: A case study of Malathion

Vasseghian, Yasser; Moradi, Masoud; Pirsaheb, Meghdad; Khataee, Alireza; Rahimi, Shoeib; Badi, Mojtaba Yegane; Khaneghah, Amin Mousavi

doi:10.1016/j.foodres.2020.109557

Cited by 70 publications

(7 citation statements)

References 65 publications

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“…In another similar study in China, specifically on tea marketed in the Tehran market, their results revealed that consumed tea in Iran is free of pesticide residues or has residues that are lower than existing MRLs [4] , [45] . Natural factors such as temperature, rainfall, volatilization, photolysis, ventilation, biodegradation, pH, dew, and growth dilution, as well as the pre-harvest period between the last application and harvesting, can cause pesticide residues in tea bushes to degrade [39] , [32] , [44] , [40] . In South Africa, the demand for organic farming has increased as customers become more health conscious and the role of food in maintaining a healthy lifestyle, which has contributed to the low use of chemicals in agriculture [43] .…”

Section: Resultsmentioning

confidence: 99%

Determination of pesticide residues in rooibos (Aspalathus linearis) teas in South Africa

et al. 2022

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Section: Resultsmentioning

confidence: 99%

Determination of pesticide residues in rooibos (Aspalathus linearis) teas in South Africa

et al. 2022

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“…The type of plasma equipment and the process conditions are among the major effective parameters that should be considered in the plasma degradation of chlorpyrifos. It should also be noted that it should be the case for other technologies, such as Ultraviolet (UV), in spite of the promising results reported in the literature (Vasseghian et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

Emerging techniques in food science: the resistance of chlorpyrifos pesticide pollution against arc and dielectric barrier discharge plasma

Gavahian

Tsai

Khaneghah

2020

qas

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Many studies introduced cold plasma as a novel and effective processing technology for microbial decontamination of food and water as well as for the removal of environmental pollution such as pesticide. However, as there are several types of plasma designs, their efficacy in degrading major pesticide residues, such as chlorpyrifos (as a hazardous chemical), should be explored. This study was conducted to assess the decontamination efficacy of 8 min of arc and dielectric barrier discharge (DBD) plasma on chlorpyrifos pesticide-water samples at a con-centration of 2 mg·L-1. The plasma-treated samples were assessed by liquid chromatography-mass spectrometry (LC-MS) and compared with the control (untreated) sample. In addition, the effects of plasma processes on some physical properties of samples were studied. According to the results, plasma-treated samples showed similar physical characteristics (e.g., refractive index and color values) to those of the untreated samples. While the temperature of the samples remained steady during the DBD plasma treatment, arc plasma changed the temperature of the sample at a rate of about 3.75°C·min–1 and yielded a sample with a final temperature of 60°C. However, contrary to the general belief that plasma is an efficient technique for pesticide degradation, chemical analyses showed high resistance of chlorpyrifos against both arc and DBD plasma under the conditions used in the present study. Therefore, the possibility of high resistance of pesticide pollution to this emerging technology should be considered. Also, further studies on the efficiency of the selected plasma system for removing pesticide pollution (e.g., during water and wastewater treatment) at industrial scale is needed.

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“…The methodology proposed in this work focuses on architecture evolution, with this aspect being equivalent to finding the highest point on the surface [32]. Compared to constructive or destructive methods, the evolutionary algorithms are better suited because the surface is unlimited, not differentiable (changes in neurons or connections are discrete and have a dissentious effect on the ANN performance), complex and noisy, deceptive (similar ANN architectures can have different performance), and multimodal (distinct ANN architectures can have similar performance) [32,33]. Taking all of these aspects into account in the current work, DE evolves the architecture (number of layers and neurons in each hidden layer) and the resulted models are evaluated after being trained using the tools provided by the Keras library.…”

Section: Artificial Neural Network Modelingmentioning

confidence: 99%

Artificial Neural Networks for Predicting Hydrogen Production in Catalytic Dry Reforming: A Systematic Review

Drăgoi

Almomani

et al. 2021

Energies

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Dry reforming of hydrocarbons, alcohols, and biological compounds is one of the most promising and effective avenues to increase hydrogen (H2) production. Catalytic dry reforming is used to facilitate the reforming process. The most popular catalysts for dry reforming are Ni-based catalysts. Due to their inactivation at high temperatures, these catalysts need to use metal supports, which have received special attention from researchers in recent years. Due to the existence of a wide range of metal supports and the need for accurate detection of higher H2 production, in this study, a systematic review and meta-analysis using ANNs were conducted to assess the hydrogen production by various catalysts in the dry reforming process. The Scopus, Embase, and Web of Science databases were investigated to retrieve the related articles from 1 January 2000 until 20 January 2021. Forty-seven articles containing 100 studies were included. To determine optimal models for three target factors (hydrocarbon conversion, hydrogen yield, and stability test time), artificial neural networks (ANNs) combined with differential evolution (DE) were applied. The best models obtained had an average relative error for the testing data of 0.52% for conversion, 3.36% for stability, and 0.03% for yield. These small differences between experimental results and predictions indicate a good generalization capability.

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Pesticide decontamination using UV/ferrous-activated persulfate with the aid neuro-fuzzy modeling: A case study of Malathion

Cited by 70 publications

References 65 publications

Determination of pesticide residues in rooibos (Aspalathus linearis) teas in South Africa

Determination of pesticide residues in rooibos (Aspalathus linearis) teas in South Africa

Emerging techniques in food science: the resistance of chlorpyrifos pesticide pollution against arc and dielectric barrier discharge plasma

Artificial Neural Networks for Predicting Hydrogen Production in Catalytic Dry Reforming: A Systematic Review

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