Food sensory evaluation employing artificial neural networks

Introduces a method of food sensory evaluation employing artificial neural networks. The process of food sensory evaluation can be viewed as a multi‐input and multi‐output (MIMO) system in which food composition serves as the input and human food evaluation as the output. It has proved to be very difficult to establish a mathematical model of this system; however, a series of samples have been obtained through experiments, each of which comprises input and output data. On the basis of these sample data, applies the back‐propagation algorithm (BP algorithm) to “train” a three‐layer feed‐forward network. The result is a neural network that can successfully imitate the food sensory evaluation of the evaluation panel. This method can also be applied in other fields such as food composition optimizing, new product development and market evaluation and investigation.