Centro de Reconocimiento Molecular y Desarrollo Tecnológico, Unidad Mixta UPV-UV, Universitat Politècnica de València, València, Spain.
Sensors (Basel). 2013 Aug 13;13(8):10418-29. doi: 10.3390/s130810418.
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.
在本研究中，开发了一种基于单片机的便携式系统，用于对不同种类的蜂蜜进行分类。为了实现这种分类，使用了一种在单片机中实现的简化模糊 ARTMAP 网络 (SFA)。由于在使用单片机时内存有限，因此需要优化程序和数据内存的使用。因此，开发了一个用于 MATLAB®的图形用户界面 (GUI)，以便优化在单片机中编程 SFA 所需的参数。测量是通过使用由七种不同金属制成的多电极进行电位技术进行的。然后，通过使用实验阶段获得的数据，通过 Matlab 中的 GUI 在 PC 上对神经网络进行训练。使用获得的参数对单片机进行编程，然后使用便携式系统分析新样本，以测试模型。结果非常有希望，因为在训练阶段实现了 87.5%的识别率，这表明这种方法不仅可以成功用于蜂蜜分类，还可以用于许多其他种类的食品。
