In this work, a multilayer perceptron (MLP) ANN is developed using the mass spectrum data from the 5 tested brands (see Error! Reference source not found.), comprising a single hidden layer of 100 nodes, and an output layer of 5 nodes (one for each brand/year). A network is trained until it has reached at least 95% accuracy or has performed 5000 training steps. A learning rate of 0.16, an annealing rate of 1000 and a learning momentum of 0.9 are used to train the model in good time. An L2 regularisation factor of 0.002 was employed to prevent over-fitting. returns after 100 training steps on average (see Figure ). It should be noted that as the model training takes advantage of an L2 regularisation factor, the loss function is not solely representative of network error, but of the network error and the sum of the square magnitudes of the layer weights.