BACKGROUND Pesticides are widely utilized worldwide to control undesirable life forms during the planting procedure in agriculture. But they can pollute the nature and jeopardize human wellbeing. Additionally, on account of high resistance and biological activity; pesticides are able to accumulate in living organs and lead to acute and long-term negative effects along with toxicity. Milk and dairy products constitute an important part of humans' diet since they contain fundamental supplements and nutrients, however they may also be the source of unhealthy components including pesticides. Therefore efficient, accurate and sensitive determination methods must be improved to quantify pesticide residues in these food samples. RESULTS Multivariate optimization strategy was employed to optimise an efficient and robust LC-MS/MS method for the determination of 156 pesticide residues in milk and dairy products. Three indepented variables considered and their levels in Box-Behnken Design were as follows: Initial percent of eluent A in mobile phase (30, 40, 50%), flow rate of the mobile phase (0.1, 0.2, 0.3 mL min-1 ), and ammonium formate concentration in mobile phase (0.0, 0.5, 1.0 mmol L-1 ). Under optimized conditions, average recoveries of target analytes were obtained in the range of 70.38% to 119.04%. Detection and quantification limits ranged from 0.06 to 2.70 μg kg-1 and from 0.22 to 8.10 μg kg-1 , respectively. CONCLUSION The validated method was successfully implemented to the analysis of twenty milk and dairy products including cream, cheese and yogurt. This method could be applied in many laboratories to reduce analysis time and cost. This article is protected by copyright. All rights reserved.

中文翻译：

---

一种测定牛奶和乳制品中 156 种农药残留的新 LC-MS/MS 方法的多变量优化

背景农药在世界范围内被广泛用于控制农业种植过程中不希望有的生命形式。但它们会污染自然并危害人类福祉。此外，由于高抗性和生物活性；杀虫剂能够在活体器官中积聚，并导致急性和长期的负面影响以及毒性。牛奶和乳制品是人类饮食的重要组成部分，因为它们含有基本的补充剂和营养素，但它们也可能是包括杀虫剂在内的不健康成分的来源。因此，必须改进高效、准确和灵敏的测定方法来量化这些食品样品中的农药残留。结果 采用多变量优化策略优化了一种高效、稳健的 LC-MS/MS 方法，用于测定牛奶和乳制品中的 156 种农药残留。Box-Behnken Design 中考虑的三个独立变量及其水平如下：流动相中洗脱液 A 的初始百分比（30、40、50%）、流动相的流速（0.1、0.2、0.3 mL min-1）和流动相中甲酸铵的浓度（0.0、0.5、1.0 mmol L-1）。在优化条件下，目标分析物的平均回收率为 70.38% 至 119.04%。检测限和定量限范围分别为 0.06 至 2.70 μg kg-1 和 0.22 至 8.10 μg kg-1。结论 该验证方法已成功应用于 20 种牛奶和乳制品（包括奶油、奶酪和酸奶）的分析。该方法可应用于许多实验室，以减少分析时间和成本。本文受版权保护。版权所有。