A new methodology combining microscopy observation with Artificial Neural Networks for the study of starch gelatinization. (January 2018) - Tao, Jinxuan; Huang, Jiabin; Yu, Long; Li, Zikang; Liu, Hongsheng; Yuan, Bo; Zeng, Delu
Abstract: A novel methodology combining microscopy observation with Artificial Neural Networks (ANNs) and realized by machine learning algorithms for the study of starch gelatinization was developed. As the most critical part during object detection, an improved starch single shot multi-box detector (starch-SSD) originated from ANNs was purposely designed and applied in monitoring the morphological changes of starch with increasing temperature. In the case, the birefringences were automatically identified by computer vision and then the relative birefringence number of the image was calculated. Basing on such number change, the temperature of phase transition was detected and consequently the degree of gelatinization (DG) at specific temperature was quickly calculated. Compared with traditional methods that mainly performed by manual operation, experimental results confirmed that the proposed method has competitive accuracy and is much faster. It also provides a unified standard for microscopy observation without subjective uncertainty. Graphical abstract: Highlights: A methodology using Artificial Neural Networks was developed to study starch gelatinization. An improved detector was designed and applied in monitoring the birefringence change. The method has competitive accuracy and is faster, and provides a unified standard.