丹贝固态发酵过程时间序列分析与预测

基于机器视觉替代物理化学方法和人工方法检测丹贝发酵过程。计算色调、饱和度、亮度彩色模型空间灰度共生矩阵来提取丹贝发酵图像纹理特征。通过和人工感官评定方法的对比，丹贝图像纹理特征曲线转折点被证明可以作为决策依据来区分丹贝发酵的4 个时期，并进一步细分为6 个阶段。纹理数据分析表明，少孢根霉菌丝发酵起点比人工方法提前3 h确定，丹贝发酵纹理特征值的极值点即为丹贝发酵过程的终点。通过移动观测窗来构建纹理特征时间序列，进而利用神经网络集成训练构建丹贝发酵过程的非线性时间序列模型，并最终通过此模型预测图像纹理特征曲线变化的极值点来实现对丹贝固态发酵过程和发酵终点的分析与预测。

Time Series Analysis and Prediction of the Solid-State Fermentation Process of Tempeh

A machine vision based method was developed as an alternative to the physical and chemical methods and the
manual method to detect the fermentation process of tempeh. The texture characteristics of the tempeh images taken during
fermentation were extracted by calculating the gray level co-occurrence matrix (GLCM) for HSI (hue, saturation, intensity)
color model. Compared with the manual sensory evaluation method, the turning point of texture feature curves of tempeh
images could be better used as decision basis to distinguish four fermentation periods and further divide them into six stages.
Texture data analysis showed that the starting point of tempeh fermentation by Rhizopus oligosporus was determined to be
3 h earlier than by using the manual method, and the extreme point of texture feature curves of tempeh images represented
the end point of tempeh fermentation. Texture feature time series was developed by sliding the observation window, and
then a nonlinear time series model for tempeh fermentation process was established by neural networks ensemble training.
Finally, the extreme points of texture feature curves were predicted using the model, achieving the analysis of the solid-state
fermentation process of tempeh and the prediction of its end point.