数据驱动软传感用于原奶乙醇稳定性预测
Song Shen1, Xiaodong Song2, Haohan Ding1,3
1School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
一个新的软感应模型使用常规质量指标预测原牛奶乙醇的稳定性. 这种非破坏性方法提高了乳制品质量监测和热处理适度评估.
科学领域:
- 乳制品科学 乳制品科学
- 食品质量控制 食品质量控制
- 机器学习应用 机器学习应用
背景情况:
- 乙醇的稳定性对于生牛奶的质量和热处理适用性至关重要.
- 传统的测试方法具有破坏性,不适合工业规模的应用.
- 常规的组成分析 (蛋白质,脂肪,乳糖) 提供了非破坏性预测的潜力.
研究的目的:
- 开发一种非破坏性的软传感模型,用于预测生奶乙醇稳定性.
- 为此预测,利用通常监测的生牛奶摄入量指标.
- 提高乙醇稳定性测试在工业环境中的效率和适用性.
主要方法:
- 使用自动编码器进行特征相关性分析和变量选择.
- 使用TabNet构建乙醇稳定性分类模型.
- 实施基于TabDDPM的数据生成技术,以解决阶级不平衡问题.
主要成果:
- 开发的软传感模型在预测乙醇稳定性方面实现了92.57%的准确性.
- 该模型显示,用于识别乙醇不稳定的原始牛奶样本的召回率为90.26%.
- 该模型是通过三年工业原奶数据进行训练和验证的.
结论:
- 软传感模型显示了乳制品质量监测中的实际工程应用的巨大潜力.
- 这种非破坏性方法为传统的破坏性测试方法提供了可行的替代方案.
- 该研究强调了机器学习在预测关键原奶质量参数方面的有效性.
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