为了研究淀粉的糊化特性，创新性的将显微观察方法与基于人工神经网络（ANNs）的目标识别方法相结合，由机器学习算法实现对马铃薯淀粉糊化特性的在线检测。本文设计了一种基于深层神经网络的检测器（Starch-SSD），用于监测淀粉随温度升高的形态变化。首先进行对淀粉双折射特征的自动识别，然后计算糊化过程中每张显微图片中呈现双折射特征的淀粉数目，根据其数量变化确定淀粉的糊化温度和不同温度下的糊化程度（DG）。实验表明，与传统以人工视觉观察为主的方法相比，本文所提方法具有精度高、速度快的优点。与此同时，该方法克服了人工主观判断的不确定性，为利用显微镜对淀粉糊化特性进行判断提供了统一的标准，也为淀粉产品工业化生产过程中的在线监控提供了可能性。

A novel method of microscopy observation combined with target recognition based on Artificial Neural Network (ANNs) was developed to study the gelatinization characteristics of starch, and the on-line detection of the gelatinization characteristics of potato starch was realized by machine learning algorithm. In this paper, a detector based on deep neural network (starch-SSD) was designed to monitor the morphological changes of potato starch with the increasing of temperature. The starch birefringence characteristics were automatically identified first and then, the number of starches with birefringence characteristics in each micrograph during the process of gelatinization was calculated. The gelatinization temperature of starch and the degree of gelatinization (DG) at different temperatures were determined according to the change of birefringence. Results showed that compared with the traditional methods based on artificial vision observation, the proposed method had the advantages of high precision and fast speed. In addition, this method overcame the uncertainty of artificial subjective judgments and provided a uniform standard for judging the gelatinization characteristics of starch by microscopic. This study would provide the possibility of on-line monitoring during the industrialized production of starch products.

广东省自然科学基金项目（2017A030313019）；广东省高等职业院校珠江学者岗位计划资助项目（2016-95）