A novel characterization of starch gelatinization using microscopy observation with deep learning methodology. (August 2022) - Niu, Yahui; Zheng, Yong; Fu, Xiong; Zeng, Delu; Liu, Hongsheng
Abstract: Starch gelatinization under microscopy shows a continuous change of granule swelling and birefringence number whereas is yet inspected by eyes with inevitable subjective errors. In this study, a novel microscopy observation was developed for characterizing starch swelling capacity (SC) and its degree of gelatinization (DG) through a computer graphic analysis combined with deep learning techniques. SC and DG present a similar trend which was not seen a significant change before 60 °C followed a dramatical increase within 60–70 °C. The significant increase of DG was slower than SC, so that there were still high ungelatinized degrees at 68 °C when granules had disintegrated completely. In the case, the birefringence of small granules was efficiently identified and a high accuracy of 95% was achieved by using improved Starch-SSD. It may provide a fast and high throughput characterization of gelatinization merely based on a machine learning analysis of the photographical evidence. Highlights: An artificial intelligent deep learning strategy for visual characterization of gelatinization. Starch swelling capacity and gelatinization degree were charactered in detail. The artificial intelligent techniques showed high sensitivity and accuracy.