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基于多维数据融合和可解释的机器学习的baijiu发酵阶段的智能识别

Lin Du¹, Junchao Zhou², Yongqing Tang³

¹Liquor Making Biotechnology and Intelligent Manufacturing of Key Laboratory of China National Light Industry, Sichuan University of Science and Engineering, Yibin 644005, People's Republic of China; School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin 644005, People's Republic of China.

Food research international (Ottawa, Ont.)

|February 28, 2026

概括

本研究介绍了一种使用传感器数据和机器学习的智能方法,以识别Baijiu发酵阶段. 多层感知器模型实现了99.48%的准确性,使标准化智能生产成为可能.

科学领域:

* 食品科学与技术 * 食品科学与技术
* 生物技术 * 生物技术
*人工智能在制造业中的应用

背景情况:

*传统的Baijiu生产依赖于人类的专业知识,在固态发酵过程中缺乏实时客观控制.
*目前的方法涉及回顾性离线分析,阻碍了流程优化和标准化.
* 需要智能系统来动态监测和控制发酵阶段.

研究的目的:

* 通过使用多维传感器数据和可解释的机器学习,开发 Baijiu 发酵阶段的智能识别方法.
* 客观地将发酵分成不同的阶段,并使用各种分析技术验证细分.
* 评估机器学习算法,以准确和可解释的发酵阶段识别.

主要方法:

*无监督学习,客观地将发酵分成三个阶段.
*使用生物化学动力学,部分最小平方差分分析 (PLS-DA) 和风味化合物分析进行验证.
*对六种机器学习算法的评估,重点关注多层感知器 (MLP) 和Shapley增量扩展 (SHAP) 的解释性.

主要成果:

* 发酵过程客观地分为三个不同的阶段,在多个维度中得到验证.
*多层感知器 (MLP) 模型在独立测试组上确定发酵阶段的准确率达到99.48%.
* SHAP分析确定了二氧化碳度,pH值,温度和湿度作为驱动分类的关键特征,突出了它们的非线性相互作用.

结论:

*开发的智能方法为实时发酵监测提供了可扩展,具有成本效益的解决方案.
*这种方法有助于从基于经验的Baijiu造转向标准化,智能化的生产.
* 提供强大的技术基础,通过人工智能集成推进传统发酵食品行业.

关键词:

州发酵公司的发酵机器学习是机器学习.多维数据融合多维数据融合过程分析;发酵阶段的识别.固态发酵发酵的固态发酵方式

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