Combined Approach for Determining Diuron in Sugarcane and Soil: Ultrasound‐Assisted Extraction, Carbon Nanotube‐Mediated Purification, and Gas Chromatography–Electron Capture Detection

Abstract

Diuron is a urea herbicide that is frequently detected in surface water, groundwater, and marine waters. However, there are few methods or guidelines reported on ensuring the quality of sugarcane and soil. In this study, a method was developed for detecting diuron to ensure the quality and safety of food and sugar. Mass spectrometry was used to identify 3,4-dichloroaniline as a marker for the thermal decomposition of diuron, and thus, as a representative component for quantitative diuron analysis. This approach can be used to rapidly detect trace amounts of diuron. In addition, ultrasound-assisted extraction (UAE) and carbon nanotube column purification were used in conjunction with gas chromatography-electron capture detection to detect diuron. The method was then evaluated for its accuracy, detection limit, and viability. The effects of extraction solvent, ultrasound time, and ultrasound power on the extraction efficiency of the analyte from sugarcane and soil were also investigated. The efficiency and optimum conditions of UAE were examined through single-factor experiments and Box-Behnken design (BBD). The optimal extraction conditions were identified as follows: acetonitrile as the extraction solvent, extraction temperature of 27°C, extraction time of 3.4min, and ultrasound power of 70 W. Under these conditions, high linearity was achieved for diuron concentrations of 0.01 to 5.0 mg/L, and the purification correlation coefficient was consistently greater than 0.998. Hence, gas chromatography, combined with UAE and BBD, offers superior efficiency extraction, which is sufficiently accurate and precise for pesticide residue analysis. PRACTICAL APPLICATION: We developed an accurate and cost-effective method for detecting diuron (a commonly used herbicide) in soil and sugar samples. We performed experiments to determine the optimum detection conditions for our method. This method can be used for online monitoring of sugar manufacturing processes to ensure food safety and quality.