A novel characterization of starch gelatinization using microscopy observation with deep learning methodology. (August 2022)

• Record Type: Journal Article
• Authors: Niu, Yahui; Zheng, Yong; Fu, Xiong; Zeng, Delu; Liu, Hongsheng
• Is Part Of: Journal of food engineering. Volume 327(2022)
• Publication Date: 2022-08
• Subjects: Birefringence -- Computer vision -- Gelatinization -- Microscopy observation -- Starch granules; Food industry and trade -- Periodicals; Food -- Analysis -- Periodicals; Aliments -- Industrie et commerce -- Périodiques; Aliments -- Analyse -- Périodiques; Aliments -- Recherche -- Périodiques; 664.005
• DOI: 10.1016/j.jfoodeng.2022.111057
• Languages: English
• ISSNs: 0260-8774

Abstract

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.