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    Determination of penicillin residues in different industrial effluents including wastewater and air samples is important to prevent exposure to residual amounts of penicillin and the development of antibiotic resistance. A green high performance liquid chromatography (HPLC) method coupled with diode array detection has been developed and validated for multiplex determination of nine penicillin antibiotics in the industrial air dust and wastewater environmental samples of penicillin facility in addition to the monitoring of facility surface cleaning. Separation was performed on C18 column with gradient elution of methanol and phosphate buffer (pH 4) at a flow rate of 1.5 mL min-1 and ultra violet (UV) detection at 220 nm. Low limits of detection were achieved (0.1-0.3 μg mL-1) indicating good sensitivity of the proposed. The method was applied for ensuring the efficiency of cleaning validation after worst-case selection. Recovery studies of the studied penicillins from fortified stainless steel and polycarbonate surfaces and swabs were between 91.91 and 100.22% with relative standard deviation 0.11-1.79%. The presence of any of the studied penicillins in wastewater samples from penicillin plant drainage was checked. Also, total air dust concentration (mg m-3) and % of penicillin active material residues in air dust were calculated from the area of the exposed group in suspension, tablet and vial production lines. The proposed method can be recommended for routine analysis of air and wastewater environmental samples for the detection of penicillin antibiotics at low levels as well as monitoring of facility surface cleaning with high accuracy and precision. © The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

    Citation

    Eman I El-Kimary, Mohammed A Korany, Ahmed E Issa, Mohamed G Basuny. Green Multiplex Chromatographic Determination of Nine Penicillin Antibiotics Residues in Industrial Air Dust and Wastewater Environmental Samples. Journal of chromatographic science. 2023 Aug 19;61(7):644-655

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    PMID: 35523719

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