In the present study, a portable system based on a microcontroller has been developed to classify different kinds of honeys. In order to do this classification, a Simplified Fuzzy ARTMAP network (SFA) implemented in a microcontroller has been used. Due to memory limits when working with microcontrollers, it is necessary to optimize the use of both program and data memory. Thus, a Graphical User Interface (GUI) for MATLAB® has been developed in order to optimize the necessary parameters to programme the SFA in a microcontroller. The measures have been carried out by potentiometric techniques using a multielectrode made of seven different metals. Next, the neural network has been trained on a PC by means of the GUI in Matlab using the data obtained in the experimental phase. The microcontroller has been programmed with the obtained parameters and then, new samples have been analysed using the portable system in order to test the model. Results are very promising, as an 87.5% recognition rate has been achieved in the training phase, which suggests that this kind of procedures can be successfully used not only for honey classification, but also for many other kinds of food.
a b s t r a c tA portable electronic tongue has been developed using an array of eighteen thick-film electrodes of different materials forming a multi-electrode array. A microcontroller is used to implement the pattern recognition. The classification of drinking waters is carried out by a Microchip PIC18F4550 microcontroller and is based on neural networks algorithms. These algorithm are initially trained with the multi-electrode array on a Personal Computer (PC) using several samples of waters (still, sparkling and tap) to obtain the optimum architecture of the networks. Once it is trained, the computed data are programmed into the microcontroller, which then gives the water classification directly for new unknown water samples. A comparative study between a Fuzzy ARTMAP, a Multi-Layer Feed-Forward network (MLFF) and a Linear Discriminant Analysis (LDA) has been done in order to obtain the best implementation on a microcontroller.
10This paper describes the determination of optimum values of the parameters of a
11Simplified Fuzzy ARTMAP neural network for monitoring dry-cured ham processing 12 with different salt formulations to be implemented in a microcontroller device. The 13 employed network must be set to the limited microcontroller memory but, at the same 14 time, should achieve optimal performance to classify the samples obtained from this 15 application.
16Hams salted with different salt formulations (100% NaCl; 50% NaCl+50% KCl and 17 55% NaCl + 25% KCl + 15% CaCl2+ 5% MgCl2) were checked at four processing 18 times, from post-salting to the end of their processing (2, 4, 8 and 12 months).
19Measurements were taken with a potentiometric electronic tongue system formed by
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