A new method for endocrine disruptor assessment in chicken fat tissues by LC-MS/MS analysis

Abstract

Food contamination by phthalate esters (PAEs) is an emerging issue due to their ubiquitous behavior. They are present in foods as a result of migration from food packing. They are known as endocrine disruptors and present an environmental health risk. These characteristics pose analytical challenges in their assessment due to contamination during their analysis. In this work, we present a novel, highly functional analytical method for the analysis of six phthalates, namely dimethyl phthalate (DMP), diethyl phthalate (DEP), di-n-butylphthalate (DBP), butyl benzyl phthalate (BBP), bis(2-ethylhexyl) phthalate (DEHP) and di-n-octyl phthalate (DNOP), in chicken fat tissues. For this purpose, we developed a simple but powerful extraction method without one of the major sources of analysis contamination, the clean-up with solid phase extraction cartridges. The innovation relies on the reduced steps of sample preparation, the minimal sample quantities and the minimal extraction solvent quantities by liquid–liquid extraction with methanol and detection by liquid chromatography with tandem mass spectrometry. The limits of quantification results for each target analyte in the chicken fat tissues were 0.25 μg kg⁻¹ for DMP, 0.5 μg kg⁻¹ for DEP, 1.25 μg kg⁻¹ for DBP, 1.25 μg kg⁻¹ for BBP, 2.5 μg kg⁻¹ for DEHP and 2 μg kg⁻¹ for DNOP, respectively. The calculated measurement uncertainty ranges from 7.2% to 16%. The present validated method was found to be precise, sensitive, and rapid to determine 6 PAEs in chicken fat tissues. This method also shows potential for application in other matrices such as water and food. This approach reduces solvent consumption, minimizes contamination risk, and aligns with the principles of Green Analytical Chemistry. Additionally, this method makes use of only 10 mg of sample, demonstrating that accurate quantification of trace contaminants is achievable with minimal sample amounts, as supported by recent miniaturized methodologies for fat-rich matrices.