*Article* **Sensitivity Analysis of Artificial Neural Networks Identifying JWH Synthetic Cannabinoids Built with Alternative Training Strategies and Methods**

**Catalina Mercedes Burlacu, Adrian Constantin Burlacu and Mirela Praisler \***

Department of Chemistry, Physics and Environment, Faculty of Science and Environment, "Dunarea de Jos" University of Galati, 47 Domneasca Street, 800008 Galati, Romania

**\*** Correspondence: mirela.praisler@ugal.ro

**Abstract:** This paper presents the alternative training strategies we tested for an Artificial Neural Network (ANN) designed to detect JWH synthetic cannabinoids. In order to increase the model performance in terms of output sensitivity, we used the *Neural Designer* data science and machine learning platform combined with the programming language *Python*. We performed a comparative analysis of several optimization algorithms, error parameters and regularization methods. Finally, we performed a new goodness-of-fit analysis between the testing samples in the data set and the corresponding ANN outputs in order to investigate their sensitivity. The effectiveness of the new methods combined with the optimization algorithms is discussed.

**Keywords:** JWH synthetic cannabinoids; artificial neural networks; optimization algorithms
