Survey on chemical pollutants and harmful factors in food in Cangzhou City in 2023
Objective To comprehensively understand the pollution status of chemical pollutants and their harmful factors in food sold in Cangzhou City,determine the distribution characteristics of pollutants,and provide data support for food safety prevention and control work.Methods According to the"2023 National Food Pollutants and Harmful Factors Risk Monitoring Work Manual",samples were collected from different procurement links such as agricultural markets and supermarkets in 16 counties and districts of Cangzhou City in 2023,and carry out the detection of chemical pollutants and harmful factors,and the data were analyzed.Results A total of 109 positive samples were detected in 245 samples,the total detection rate was 44.1％.Two samples exceeded the standard,with an exceeding rate of 0.8％.There were 5 618 monitoring indicators,and 1 491 were detected,the detection rate was 26.5％.Two items exceeded the standard,the exceeding rate was 0.04％.The highest positive detection rate of aquatic products was 78.6％(22/28).The highest detection rate of pesticides was 83.5％(71/85).The highest positive rate was 52.7％in the farmer's market(39/72).The samples exceeding the standard were peanut[aflatoxin detoxification(AFB1)]and ginger(chlorpyrifos).The detection rates of samples in different procurement stages and monitoring items were compared,and the differences were statistically significant(all P＜0.05).The positive detection rates of different types of pollutants and their harmful factors in the samples,as well as the detection rates of monitoring items were compared,and the differences were statistically significant(all P＜0.05).Conclusion In 2023,the food sold in Cangzhou City have different degrees of pollution,mainly distributed in aquatic products,mainly in agricultural markets,with the highest detection of pesticides,but overall it is within a controllable range,and supervision should continue to be strengthened.
Chemical pollutantDetection rateExceeding the standardContaminate
NETL
NSTL
万方数据
