Optimization of an HPLC-MS/MS method to analyze chlordecone in bovine serum and correlations with levels in liver, muscle and fat.

ABSTRACT

Chlordecone is an organochlorine pesticide used from 1972 to 1993 in the French West Indies. Its extensive use and high persistence in soils induced massive contamination of the environment and of the food chain, especially in cattle through contaminated soil ingestion. To ensure suitability for consumption of bovine meat, monitoring plans are set up based on perirenal fat concentrations after slaughtering. In the present study, we have investigated an in-vivo monitoring approach by measuring chlordecone levels in serum samples. For this purpose, a sensitive high-performance liquid-chromatography-tandem mass spectrometry (HPLC-MS/MS) method following a QuEChERS extraction method was successfully optimized and validated, reaching a limit of quantification of 0.05 ng g-1 fresh weight. This method was applied to 121 serum samples collected from bovines originating from contaminated areas of Martinique and Guadeloupe. Chlordecone was detected in 88% of the samples, and quantified in 77% of the samples, with concentrations ranging from 0.05 to 22 ng g-1. Perirenal fat, liver, and muscle were also sampled on the same animals and the measured concentrations of chlordecone were statistically correlated to the levels determined in serum. Mean concentration ratios of 6.5 for fat/serum, 27.5 for liver/serum, and 3.3 for muscle/serum were calculated, meaning that chlordecone was not only distribute in fat (as expected), muscle and liver, but also in serum. Good correlations were found to allow prediction of chlordecone concentrations in muscle based on concentrations measured in serum. This study opens the door to possible pre-control of bovines before slaughter. In cases of probable non-compliance with maximum residue levels (MRLs), farm management could proceed to allow for depuration under controlled conditions. This would have a strong impact on both economic and food safety management measures.