Statistical models have been used to estimate the refractive index of 72 imidazolium-based ionic liquids using the electronic polarisability of their ions as the data for two different mathematical approaches: artificial neural networks, in the form of multi-layer perceptrons, and multiple linear regression models. Although the artificial neural networks and linear models have been able to accomplish this task, the multi-layer perceptron model has been shown to be a more accurate method, thanks to its ability of determining non-linear relationships between different dependent variables. Additionally, it is clear that the multiple linear regression presents a systematic deviation in the estimated refractive index values, which confirms that it is an inappropriate model for this system.

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Summary A computerised approach to vastly reduce the experimental information required (number of independent variables) to classify similar extra virgin olive oils (EVOOs) is presented. It is based on the application of a multilayer perceptron (MLP) and further analysis of the obtained results using differential calculations. To validate this new model, it has been applied for the classification of 147 EVOO samples into four similar families. The oil samples employed came from two types of protected denomination of origin (PDO) oils and two non‐PDO from the same Spanish province (Granada). This approach results in a new method that reduces the necessary size of the databases used, without an appreciable loss of information, by over 82%. The percentage of misclassifications using less data points is similar to the results achieved using the whole database (less than 0.90%).

A wide variety of olive oil samples from different origins and olive types has been chemically analyzed as well as evaluated by trained sensory panelists. Six chemical parameters have been obtained for each sample (free fatty acids, peroxide value, two UV absorption parameters (K232 and K268), 1,2-diacylglycerol content, and pyropheophytins) and linked to their quality using an artificial neural network-based model. Herein, the nonlinear algorithms were used to distinguish olive oil quality. Two different methods were defined to assess the statistical performance of the model (a K-fold cross-validation (K = 6) and three different blind tests), and both of them showed around a 95-96% correct classification rate. These results support that a relationship between the chemical and the sensory analyses exists and that the mathematical tool can potentially be implemented into a device that could be employed for various useful applications.

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