Firuzeh Hosseini - Department of Chemistry, Faculty of Science, Farhangian University, Tehran, Iran
Ezzat Naddaf - Department of Applied Chemistry, Mashhad Branch, Islamic Azad University, Mashhad, Iran
Javd Fadaee Kakhki - Technology Management Department, Police Sciences and Social Studies Institute, Tehran, Iran.
Mahmoud Ebrahimi - Department of Chemistry, Mashhad Branch, Islamic Azad University Mashhad, Iran

In the present work, a very sensitive, reliable, and simple spectrofluorometric procedure was improved for concurrent determination of ofloxacin, enrofloxacin, and levofloxacin in the absence of separation treads. Despite a spectral cover, a part of fluoroquinolones have been concurrently resolved by chemometric come near to involving principal component analysis artificial neural network and partial least squares. Artificial varieties mixtures of fluoroquinolones were evaluated and the results acquired by the implementations of these chemometric approaches were evaluated and compared. It was found that the principal component, artificial neural network method provided relatively better accuracy than that of PLS method. This method was applied satisfactorily for determining mixtures of fluoroquinolones in tilapia, chicken samples, and synthetic samples (with concentration ranging over ۰.۰۵-۱.۱ µg/mL for ofloxacin as well as ۰.۰۶-۰.۶ µg/mL and ۰.۰۱-۰.۲۳ µg/mL for enrofloxacin and levofloxacin), respectively. The suggested method enables detection limits of ۰.۰۴, ۰.۰۱, and ۰.۰۰۹ µg/mL for ofloxacin, enrofloxacin, and levofloxacin, correspondingly. The recoveries in the tilapia and chicken matrices ranged from ۱۱۴% to ۹۲%. All experiments that needed to be repeated were repeated ۴ times.

Fluoroquinolnes, Principal component, Artificial Neural Network, Partial Least Squares