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使用机器学习方法,根据南极的新鲜度预测发酵的质量
Wenhui Qu1, Luying Tang1, Ruoshu Li2
1College of Food Science & Engineering, Ocean University of China, Qingdao, Shandong 266404, PR China.
Food chemistry
|January 7, 2026
概括
南极的新鲜度显著影响发酵汁的质量. 一个机器学习模型从鱼的新鲜度准确地预测汁质量,指导智能发酵和鱼利用.
科学领域:
- 食品科学 食品科学 食品科学
- 海洋生物学 海洋生物学
- 机器学习应用 机器学习应用
背景情况:
- 南极的质量对于发酵的鱼来说至关重要.
- 鱼新鲜度和汁质量之间的关系还不清楚.
- 对这种关系缺乏预测模型.
研究的目的:
- 为了评估不同新鲜度的样本及其相应的鱼.
- 开发一个高性能预测模型,将原材料 () 质量与产品 (鱼) 质量联系起来.
- 为的利用和智能发酵提供指导.
主要方法:
- 75个南极鱼样本及其发酵的评估.
- 在室温下24小时后对鱼的腐烂指标的分析.
- 机器学习的应用,包括特征选择的拉索回归,与预测的线性模型集成.
- 使用特征拟合和平均绝对百分比误差评估预测准确性.
主要成果:
- 鱼在24小时内严重腐烂,呈现着变黑的颜色和增加的腐烂化合物.
- 由于腐败,收益率和换率急剧下降.
- 开发的拉索回归集成线性模型实现了高预测准确度 (近80%的特征匹配,约70%与<5%的MAPE).
结论:
- 成功建立了一个强大的预测模型,将鱼的新鲜度与鱼质量联系起来.
- 这个模型为优化鱼利用和实现智能发酵过程提供了宝贵的见解.
- 这些发现支持在发酵汁生产中改善质量控制.
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