Abstract: Cell parameters of steamed bread crumb were obtained by digital image analysis (DIA), with the objective to evaluate the possibility of using computer machine vision for steamed bread quality evaluation. Three wheat cultivars, Weaver with strong gluten, Ningchun 4 with medium gluten, and Jing 411 with weak gluten were used. Three water addition levels based on farinograph's water absorption were used, thus 9 samples were included in this experiment. K-means algorithm was used to segmentation of steamed bread image. The total cell area, mean cell area and the number of cell were obtained as characters of steamed bread image. Results show that those characters are able to characterize the effect of water addition and gluten strength on steamed bread quality, i.e., with increase of water addition or gluten strength, the cell total area increases. This is corresponded with the change of steamed bread volume. Eleven samples were used to compare the results of machine vision with panel evaluation, and result indicates that it is feasible to evaluate steamed bread quality by computer machine vision.

Key words: Image process, K-means algorithm, Steamed bread quality, Water addition, Wheat gluten strength