Multi-class analysis of veterinary drugs in eggs using dispersive-solid phase extraction and ultra-high performance liquid chromatography-tandem mass spectrometry
FOOD CHEMISTRY
Authors: Wang, Chengfei; Li, Xiaowei; Yu, Fugen; Wang, Yingyu; Ye, Dongyang; Hu, Xue; Zhou, Lan; Du, Jingjing; Xia, Xi
Abstract
A multi-residue method has been developed for the identification and quantification of 78 compounds from seven different classes of veterinary drugs in eggs. This method was based on dispersive solid phase extraction where mixed-mode cation exchange sorbent was used to combine the isolation of compounds and sample purification. The analysis was performed using ultra-high performance liquid chromatography-tandem mass spectrometry, and the chromatographic run time of one injection was 9.5 min. The mean recovery ranged from 70.5% to 119.2% and inter-day relative standard deviation was less than 17.0%. The limit of quantification ranged between 0.1 and 1 mu g/kg, which was sufficient to support surveillance monitoring. Lastly, the method was successfully used to detect residues of veterinary drug in real samples. The dietary exposure risk was subsequently assessed using the results of the survey, indicating that the evaluated daily intake and percentage of acceptable daily intake were at toxicologically acceptable levels.
Running time analysis of the (1+1)-EA for robust linearoptimization
THEORETICAL COMPUTER SCIENCE
Authors: Bian, Chao; Qian, Chao; Tang, Ke; Yu, Yang
Abstract
Evolutionary algorithms (EAs) have found many successful real-world applications, where the optimization problems are often subject to a wide range of uncertainties. To understand the practical behaviors of EAs theoretically, there are a series of efforts devoted to analyzing the running time of EAs for optimization under uncertainties. Existing studies mainly focus on noisy and dynamic optimization, while another common type of uncertain optimization, i.e., robust optimization, has been rarely touched. In this paper, we analyze the expected running time of the (1+1)-EA solving robust linear optimization problems (i.e., linear problems under robust scenarios) with a cardinality constraint k. Two common robust scenarios, i.e., deletion-robust and worst-case, are considered. Particularly, we derive tight ranges of the robust parameter dor budget kallowing the (1+1)-EA to find an optimal solution in polynomial running time, which disclose the potential of EAs for robust optimization. (c) 2020 Elsevier B.V. All rights reserved.
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