Multivariate optimization of a newLC-MS/MSmethod for the determination of 156 pesticide residues in milk and dairy products

Creative Commons License

Gorel-Manav O., Dinc-Zor S., Akyildiz E., Alpdoğan G.

JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE, vol.100, pp.4808-4817, 2020 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 100
  • Publication Date: 2020
  • Doi Number: 10.1002/jsfa.10540
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Agricultural & Environmental Science Database, Analytical Abstracts, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, CAB Abstracts, Chemical Abstracts Core, Communication Abstracts, EMBASE, Food Science & Technology Abstracts, INSPEC, MEDLINE, Metadex, Pollution Abstracts, Veterinary Science Database, DIALNET, Civil Engineering Abstracts
  • Page Numbers: pp.4808-4817
  • Yıldız Technical University Affiliated: Yes


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 a 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 optimize an efficient and robust liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) method for the determination of 156 pesticide residues in milk and dairy products. Three independent variables considered and their levels in the Box-Behnken design were as follows: initial percentage 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 mu g kg(-1)and from 0.22 to 8.10 mu g kg(-1), respectively. CONCLUSION The validated method was successfully implemented to the analysis of 20 milk and dairy products including cream, cheese and yogurt. This method could be applied in many laboratories to reduce analysis time and cost. (c) 2020 Society of Chemical Industry