###### Abstract  
The present work aims to test the potential of the application of Artificial Neural Networks (ANNs) for food authentication. For this purpose, honey was chosen as the working matrix. The samples were originated from two countries: Romania (50) and France (53), having as floral origins: acacia, linden, honeydew, colza, gallium verum, coriander, sunflower, thyme, raspberry, lvender and desthunt. The ANNs were built on the isotope and elemental content of the investigated honey samples. This approach conducted to the development of a prediction model for geographical recognition with an accuracy of 96%. Alongside this work, distinct models were developed and tested, with the aim of identifying the most suitable configurations for this application. In this regard, improvements have been continuously performed; the most important of them consisted in overcoming the unwanted phenomenon of over-fitting, observed for the training data set. This was achieved by identifying appropriate values for the number of iterations over the training data and for the size and number of the hidden layers and by introducing of a dropout layer in the configuration of the neural structure. As a conclusion, ANNs can be successfully applied in food authenticity control, but with a degree of caution with respect to the \"over optimization\" of the correct classification percentage for the training sample set, which can lead to an over-fitted model. ANNs; honey; geographical differentiation; recognition models; food authentication 202120202020202020202020202020202020202020202020202020202020202020202020202020202020202020202020202020202020202020202020202020202020202020202020202020202020202020202020202020202020202202020202020  
Food frauds become more and more subtle and difficult to identify despite the continuous development and implementation of sensitive analytical tools able to provide a high amount of information. Therefore, continuous improvements in terms of analytical detection as well as data processing need to be made. In this context, during the last decades, it was demonstrated that data processing tools, represented by advanced statistical methods, can be an important ally in the development of reliable models able to differentiate among distinct predefined classes of food matrices. Nowadays, a step forward in the improvement of the recognition model's development is given by Artificial Intelligence (AI). In particular, Artificial Neural Networks (ANNs) proved to be one of the most effective tools in this regard. An Artificial Neural Network can be seen as a structure of units, also called nodes or neurons, which are interconnected through some links. Each association has a numeric value that represents the weight between two units in the network. Each unit is designed to perform a computation based on the values received from its input links and to pass the obtained result to its output links. The computation is done locally and consists of two phases, one characterized by a linear function and one by a nonlinear function. In the first phase, the unit calculates the weighed sum of the inputs through the so-called input function. In the second one, an activation function processes the weighted sum into a final value that acts as the node's activation value and output. Applying distinct mathematical functions for the unit's activation function leads to distinct ANN models. Initially, the neural structure is characterized by some random weights that are then adjusted in order to make the network more accurate. This process is often split into epochs or iterations that imply updating all weights for all entities that contribute to the learning process of the network [11]. In an ANN, the accuracy of the predicted result depends on the chosen structure (i.e., the number of layers and units) and, more importantly, on the chosen activation function.