Abstract
tThis paper describes the determination of optimum values of the parameters of a Simplified FuzzyARTMAP neural network for monitoring dry-cured ham processing with different salt formulations tobe implemented in a microcontroller device. The employed network must be set to the limited micro-controller memory but, at the same time, should achieve optimal performance to classify the samplesobtained from this application.Hams salted with different salt formulations (100% NaCl; 50% NaCl + 50% KCl and 55% NaCl + 25%KCl + 15% CaCl2+ 5% MgCl2) were checked at four processing times, from post-salting to the end of theirprocessing (2, 4, 8 and 12 months).Measurements were taken with a potentiometric electronic tongue system formed by metal electrodesof different materials that worked as nonspecific sensors. This study aimed to discriminate ham samplesaccording to two parameters: processing time and salt formulation.The results were analyzed with an artificial neural network of the Simplified Fuzzy ARTMAP (SFAM)type. During the training and validation process of the neural network, optimum values of the controlparameters of the neural network were determined for easy implementation in a microcontroller, and tosimultaneously achieve maximum sample discrimination. The test process was run in a PIC18F450 micro-controller, where the SFAM algorithm was implemented with the optimal parameters. A data analysiswith the optimized neural network was achieved, and samples were perfectly discriminated according toprocessing time (100%). It is more difficult to discriminate all samples according to salt formulation type,but it is easy to achieve salt type discrimination within each processing block time. Thus, we concludethat the processing time effect dominates salt formulation effects.
