Non-linear models applied to experimental spectroscopical quantitative analysis of aqueous ternary mixtures of imidazolium and pyridinium-based ionic liquids

Spectroscopic determination of the photodegradation of monovarietal extra virgin olive oils and their binary mixtures through intelligent systems

“…This approach enables the elimination of redundant and/or non-informative variables (absorbance-wavelength pairs). In the case considered, the calculation of the areas under the curve (AUCs) of the different absorption bands observed was the procedure selected to both reduce the number of independent variables and extract the useful spectroscopic information from the bands, as a combination of AUCs might contain more information than absorption peaks at discrete wavelengths [19].…”

“…In order to be able to appropriately operate, ANNs require the optimization of their weights [19,20,23]. This process comprises two distinct stages: the first one is known as the training phase, and the second one, the verification phase.…”

“…To correctly fulfill these two steps, the database must be previously randomly divided: 85% of the data is used in the training stage (training dataset), while the remaining 15% is employed in the verification stage (verification dataset). This is one of the most typical training-verification data distributions to train MLPs [19,20]. The training phase takes place firstly, where the training dataset is processed by the model.…”

“…The MLP performs the estimation and calculates the error attained (comparing the estimated response with the actual target values). In order to decrease this error value, the weights are modified towards obtaining the lowest possible error [16,19,20]. If this process keeps going on, the error would potentially reach a value of zero, as the weights are strictly improved to achieve a perfect fit to the training data available.…”

“…The quantity of these neurons is extremely important for the final performance of the model: if the HNN employed is too high, it can produce an over-fit network; but if the number is too low, it would lead to a decrease in the learning capability of the model [16,19]. The procedure chosen for the optimization of the HNN was a trial-and-error or heuristic method, considering that the number of weights must be the lowest possible, and always smaller than the number of data points available.…”