Analysing Informal Milk Supply Chains Data to Identify Seasonal Occurrences of Antibiotic Residues

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Statistics for Data Science and Policy Analysis

Abstract

Informal milk marketing chains provide the major milk supply link from smallholder dairy production systems to consumers in develo** countries. Prevalence risk of antibiotic residues was investigated in milk samples (n = 528) collected from different levels of informal supply chains in Pakistan from 2012–2013. After screening, all positive samples were further analyzed by High Performance Liquid Chromatography to quantify individual β-lactam residues. Fifteen percent of the total samples were found to be positive for at least one of the antibiotics. All positive samples (81/528) were positive for amoxicillin. Percentage of positive samples for ampicillin and penicillin was 12.1 and 6.4% respectively. Percentages of positive samples collected from farmers, small collectors, large collectors and retailers were 17.5, 15.1, 8.3 and 13.5 respectively. When relating to season of collection 11.3, 10.2, 19.1, 17.9 and 16.3% of samples in autumn, monsoon, spring, summer and winter were found positive. Concentrations of amoxicillin, ampicillin and penicillin in positive samples were 79.5 ± 12.15, 106.6 ± 12.49 and 13.7 ± 4.2μgkg−1 respectively. Occurrence of these high levels of drug residues demand mass awareness programs associated with the implementation of legislation and its enforcement designed to protect the consumer.

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Acknowledgement

Authors really acknowledge the help of Biomin® Singapore for its support by providing funds to purchase kits for ELISA analysis of milk samples. Moreover, we would like to thank Mr. Sajid and Mr. Faisal in Quality Operation Lab in UVAS Lahore for their support during analysis. Moreover, Charles Sturt University’s writing support for timely submission was exceptional.

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Aslam, N. et al. (2020). Analysing Informal Milk Supply Chains Data to Identify Seasonal Occurrences of Antibiotic Residues. In: Rahman, A. (eds) Statistics for Data Science and Policy Analysis. Springer, Singapore. https://doi.org/10.1007/978-981-15-1735-8_10

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