Quantitative structure-retention relationships model for retention time prediction of veterinary drugs in food matrixes

Abstract Quantitative structure-retention relationships (QSRR) is a technique used in the prediction of the retention time of compounds based on their structure and chromatographic behavior. In this study, an easy and usable QSRR model was established based on multiple linear regression (MLR) to predict three kinds of illegal additives in food matrixes. For this purpose, 95 drugs were chosen, including a training set of 62 drugs, a test set of 30 drugs, and a real sample set of 3 drugs. The molecular descriptors for each compound were obtained by free softwares of advanced chemistry development (ACD) and toxicity estimation software tool (TEST). After that, the MLR-based QSRR model was established, both internal and external validation was used for validation of this model. The result indicated that the following descriptors have great influence on the predicted retention time: ACDlogP, ALOGP, ALOGP2, Hy, Ui, ib, BEHp1, BEHp2, GATS1m, GATS2m. The correlation coefficient for fitting model revealed a strong correlation between the drug retention time and selected molecular descriptors (R 2  = 0.966). Moreover, the four validation methods (leave-one-out, k-fold cross-validation, test set, and real sample set) indicated the high reliability of this model. In conclusion, this method provided a more suitable and usable model for research work in several branches of analytical chemistry, especially in the field of food safety to improve the ability of retention time prediction for illegal additives.