Quantitative Study of Starch Swelling Capacity during Gelatinization with an Efficient Automatic Segmentation Methodology

Abstract A novel image segmentation methodology combined with optical microscopy observation was developed for qualifying starch swelling. Starch granules in the micrograph were successfully segmented based on high-precision edges extraction achieved by Canny edge detection together with mathematical morphology operation. Granules were automatically identified by computer vision and characterized by giving quantifiable area of these granules. The evolved swelling process could be generally divided into two phases. During the first phase, starch granules were only swollen up by 2.56%, which is hard to be identified by conventional naked eye. During the following narrow temperature interval (60 - 66 ℃), these starch granules were detected to swell up significantly by 9.08%. Through the granule area variable, swelling capacity was high-throughput characterized, which allows for the whole evaluation to be completed within a couple of minutes. The proposed methodology showed a high accuracy and potential as a novel technique for characterizing gelatinization.