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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We study the influence of grain boundaries (GBs) on radiation-induced vacancies, as well as on the hydrogen (H) behavior in tungsten (W) samples with different grain sizes in the temperature range from 300 K to 573 K, both experimentally and by computer simulations. For this purpose, coarse-grained and nanostructured W samples were sequentially irradiated with carbon (C) and H ions at energies of 665 keV and 170 keV, respectively. A first set of the implanted samples was annealed at 473 K and a second set at 573 K. Object kinetic Monte Carlo simulations were performed to account for experimental outcomes. Results show that the number of vacancies for nanostructured W is always larger than for monocrystalline W samples in the whole studied temperature range and that the number of vacancies is only reduced in samples with a large density of grain boundaries and at temperatures high enough to activate the vacancy motion (around 573 K). Results also indicate that the migration of H along vacancy free grain boundaries is more effective than along the bulk, and that the retained H is trapped in vacancies located within the grains. These results are used to explain the experimental outcomes.

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.