As antibiotic residue becomes more and more serious all over the world, a rapid and effective detection method is needed to evaluate the antibiotic residue in feed matrices to ensure food safety for consumers. In this study, three different kinds of fluoroquinolones (norfloxacin, enrofloxacin, and ofloxacin) in feed matrices were analyzed using terahertz (THz) spectroscopy, respectively. Meanwhile, pure fluoroquinolones and pure feed matrices were also measured in the same way. Then, the absorption spectra of all of the samples were extracted in the transmission mode. Pure norfloxacin has two absorption peaks at 0.825 and 1.187 THz, and they could still be observed when mixing norfloxacin with feed matrices. Also, there was an obvious and strong absorption peak for ofloxacin at 1.044 THz. However, no obvious absorption peak for enrofloxacin was observed, and only a weak absorption peak was located at 0.8 THz. Then, the different models were established with different chemometrics to identify the fluoroquinolones in feed matrices and determined the fluoroquinolones content in the feed matrices. The least squares support vector machines, Naive Bayes, Mahalanobis distance, and back propagation neural network (BPNN) were used to build the identification model with a Savitzky-Golay filter and standardized normal variate pretreatments. The results show that the excellent classification model was acquired with the BPNN combined with no pretreatment. The optimal classification accuracy was 80.56% in the testing set. After that, multiple linear regression and stepwise regression were used to establish the quantitative detection model for different kinds of fluoroquinolones in feed matrices. The optimal correlation coefficients for norfloxacin, enrofloxacin, and ofloxacin in the prediction set were obtained with multiple linear regression that combined absorption peaks with wavelengths selected by stepwise regression, which were 0.867, 0.828, and 0.964, respectively. Overall, this research explored the potential of identifying the fluoroquinolones in feed matrices using THz spectroscopy without a complex pretreatment process and then quantitatively detecting the fluoroquinolones content in feed matrices. The results demonstrate that THz spectra could be used to identify fluoroquinolones in feed matrices and also detect their content quantitatively, which has great significance for the food safety industry. (C) 2018 Optical Society of America
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