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A new characterization methodology for starch gelatinization

Wu, Wei, Tao, Jinxuan, Zhu, Peitao, Liu, Hongsheng, Du, Qiliang, Xiao, Jie, Zhang, Wutong, Zhang, Shaobo
International journal of biological macromolecules 2019 v.125 pp. 1140-1146
computer vision, gelatinization, neural networks, process control, starch, temperature, uncertainty
A gelatinization degree control system, with a combination of Artificial Neural Networks (ANNs) and computer vision, was successfully developed. An intelligent measurement framework was purposely designed to achieve a precise investigation on phase transition and morphology change of starch in real time, as well as a process control during gelatinization. Base on a variation of birefringence number, the degree of gelatinization (DG) control system provided a direct and fast methodology without subjective uncertainty in studying starch gelatinization. In the course, the whole system was a cascade structure with the hot-stage temperature chosen as the inner-loop parameter, thus the granule morphology and birefringence at different DG could be easily observed and compared in real time, and the relative transition temperature was simultaneously calculated.