This study explores the reduction of carbamates (CAs) and pyrethroids (PYs) − commonly used pesticides − in lettuce using various immersion solutions and ultrasonic processing. It also examines the role of machine learning and molecular docking in understanding the mechanisms of pesticide reduction. The results revealed that the highest reduction of both CAs and PYs exceeded 80 % on lettuce leaves. In most samples, the reduction increased with the power of ultrasonic processing and processing time. The results of machine learning models (XGBoost and SHAP) showed that during the immersion cleaning of CAs and PYs, as well as during both immersion cleaning and ultrasonic processing of CAs + PYs, the reduction was most influenced by the initial pesticide levels and immersion time. Gas Chromatography-Mass Spectrometry (GC–MS) analysis of lettuce’s wax layer identified 24 compounds, including fatty alcohols, fatty acids, fatty acid esters, and triterpenoids. Despite the absence of active sites, the lipophilic nature of long-chain aliphatic compounds aids in pesticide binding, while triterpenoids form strong hydrogen bonds with pesticides, indicating a robust adsorption on the lettuce surface. This study aims to offer insights into the efficient removal of chemical pesticide residues from fruits and vegetables, addressing critical concerns for food safety and human health.

中文翻译：

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通过超声波处理和各种浸泡溶液去除生菜 (Lactuca sativa L.) 中四种农药的因果推论和机制


本研究探讨了使用各种浸泡溶液和超声波处理来减少生菜中常用农药氨基甲酸酯 (CA) 和拟除虫菊酯 (PY) 的含量。它还研究了机器学习和分子对接在理解农药减少机制方面的作用。结果显示，生菜叶片上 CA 和 PY 的最高减少量均超过 80%。在大多数样品中，减少量随着超声波处理功率和处理时间的增加而增加。机器学习模型（XGBoost 和 SHAP）的结果表明，在 CA 和 PY 的浸泡清洗过程中，以及 CA + PY 的浸泡清洗和超声波处理过程中，初始农药水平和浸泡时间对减少量的影响最大。 。对生菜蜡层进行气相色谱-质谱 (GC-MS) 分析，鉴定出 24 种化合物，包括脂肪醇、脂肪酸、脂肪酸酯和三萜类化合物。尽管不存在活性位点，长链脂肪族化合物的亲脂性有助于农药的结合，而三萜类化合物与农药形成强氢键，表明在生菜表面有很强的吸附作用。这项研究旨在深入了解如何有效去除水果和蔬菜中的化学农药残留，解决食品安全和人类健康的关键问题。