Inspecting Ingredients of Starches in Starch-Noodle based on Image Processing and Pattern Recognition

Inspecting what sort of starch in commercial starch-noodles is important to international trade, food safety and protecting consumer benefit. At present, the inspection of components of starches in starch-noodle mainly relies on sensory perception, and which is fallibility or trustless. Because the microstructure pattern of starches in starch-noodles depends mainly on a kind or blend of starches from which the starch-noodle was made, this paper presents an approach to classify the starch-noodles by using computer system automatically based on recognizing the microstructure pattern of the starches and components in starch-noodle. The method consists of three steps: 1) take the micrograph of starch-noodles with scanning electron microscopy and preprocessing. 2) Extract features of fractal geometry and gray-level co-occurrence from micrograph. 3) Distinguish a sort of starch-noodles by using these combined features as input vector of artificial neural networks. The experiments has been conducted with starch-noodles of mungbean blending pachyrhizus, and the experimental results show that the method is practicable and effective