Artificial neural networks (Fuzzy ARTMAP) analysis of the dataobtained with an electronic tongue applied to a ham-curing processwith different salt formulations

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.This work was financially supported by the Spanish Government and European FEDER funds (MAT2012-38429-C04-04).Gil Sánchez, L.; Garrigues Baixauli, J.; Garcia-Breijo, E.; Grau Meló, R.; Aliño Alfaro, M.; Baigts Allende, DK.; Barat Baviera, JM. (2015). Artificial neural networks (Fuzzy ARTMAP) analysis of the dataobtained with an electronic tongue applied to a ham-curing processwith different salt formulations. Applied Soft Computing. (30):421-429. https://doi.org/10.1016/j.asoc.2014.12.0374214293

Kinetics studies during NaCl and KCl pork meat brining

[EN] We describe and quantify salt transfer processes during food processing using mathematical models. Our approach incorporates as novel elements: (i) a more rigorous determination of the salt diffusion kinetics by using the salt concentration of the liquid phase instead of the salt concentration of the overall samples, (ii) the use of novel, more accurate equilibrium conditions, (iii) the consideration of mass transfer coefficients in the boundary and (iv) the consideration of diffusion and salt transfer coefficients as functions of time. The methodology is used to determine the NaCl and KCl diffusion and transport coefficients during pork meat salting. Our results, combining experimental studies with a numerical study of a mathematical optimization problem point out the limitations of purely diffusive models and the need for more sophisticated models accounting for more physical and chemical processes taking place in meat during salting. © 2011 Elsevier Ltd. All rights reserved.This work was partially supported by Ministerio de Ciencia y Tecnologia under grants AGL2004-05064-C02, MTM2009-13832, CSD2006-32, and the European Union (FEDER programme).Barat Baviera, JM.; Baigts Allende, DK.; Aliño Alfaro, M.; Fernández Fernández, FJ.; Perez García, V. (2011). Kinetics studies during NaCl and KCl pork meat brining. Journal of Food Engineering. 106(1):102-110. doi:10.1016/j.jfoodeng.2011.04.022S102110106