机器学习的应用用于使用图像处理对工业发酵进行定量分析.
1Department of Food Engineering, College of Bio-Convergence, Dankook University, 119, Dandae-ro, Dongnam-gu, Cheonan-si, Chungcheongnam-do 31116 Republic of Korea.
Food science and biotechnology
|February 13, 2025
概括
一个新的实时发酵量化传感器 (RFQS) 使用人工智能图像分析来监测发酵进展. 这项技术准确地测量发酵程度,为食品工业提供了宝贵的工具.
科学领域:
- 食品科学与技术 食品科学与技术
- 生物技术是生物技术.
- 人工智能在制造业中的应用
背景情况:
- 传统的发酵监测依赖于间接或耗时的方法.
- 精确,实时量化发酵对于过程优化和质量控制至关重要.
- 食品行业对自动化,非侵入性监测系统的需求正在增加.
研究的目的:
- 开发和验证实时发酵量化传感器 (RFQS) 用于定量发酵评估.
- 整合一个卷积神经网络 (CNN) 模型,用于发酵副产品的图像分析.
- 使用人工智能驱动的图像处理建立实时发酵度测量系统.
主要方法:
- 开发RFQS以检测和捕捉发酵气体产生的气气泡的图像.
- 集成基于CNN的发酵测量模型,用于分析泡图像.
- 用不同的酵母和葡萄糖度进行验证实验,以评估发酵动态.
- 在完成后使用实时传感器数据计算总发酵度.
主要成果:
- 通过分析气气泡图像,RFQS成功监测了发酵过程.
- 变化的酵母和葡萄糖量被证明会显著影响发酵的持续时间和程度.
- 集成的CNN模型准确地对泡图像进行了分类,以进行持续监控.
- 实时数据允许计算完成后的总发酵度.
结论:
- 基于人工智能的图像处理,通过RFQS,提供了一个有效的定量测量工具,用于发酵.
- 开发的系统可以持续实时监测发酵程度.
- 这项技术具有显著的潜力,可以提高发酵食品行业的工艺控制和效率.
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