The application of neural networks in smell analyzing system of grain quality

Moldy grain have mildew which are harmful to people and animals. To provide a simple and objective solution to identify whether grain are moldy, a smell analyzing system of grain quality is developed. The system consists of gas sensor array, gas pipe system, signal adjusting circuit, data collecting system and pattern recognition system. The software is developed on the basis of Visual Basic 6.0 and Matlab 6.5. In order to improve veracity of identification, three eigenvalues in the collecting data are used, one is the maximal response point, the other two are near the maximal response point. The sample eigenvalues are trained with the triplex-optimize BP NN. By testing, both training samples and test samples can be identified correctly, which indicates that the application of neural networks is valuable in the smell analyzing system of grain quality.