使用机器学习量化来自多种作物的成分的技术功能性质
Anouk Lie-Piang1, Jos Hageman2, Iris Vreenegoor1
1Food Process Engineering, Wageningen University, P.O. Box 17, 6700 AA, Wageningen, the Netherlands.
Current research in food science
|October 12, 2023
概括
配制食品需要了解技术功能性质,而不仅仅是成分. 这项研究表明,模型可以预测来自多种作物 (如黄豆和狼) 的成分混合物的这些特性,尽管有一些准确性权衡.
科学领域:
- 食品科学与技术 食品科学与技术
- 植物性成分 植物性成分
- 配料功能 配料的功能
背景情况:
- 最少加工的食品成分含有复杂的成分,需要基于技术功能性质的配方.
- 了解成分功能对于开发来自各种植物来源的新型食品至关重要.
研究的目的:
- 评估来自多种作物的成分混合物的技术功能性质 (凝,粘度,乳液稳定性,发泡能力) 的量化可行性.
- 基于单一作物与多作物混合物的成分功能预测模型进行比较.
- 探索先进的建模技术来预测成分的行为.
主要方法:
- 对黄豆和狼成分的凝,粘度,乳液稳定性和发泡能力的量化.
- 应用线条回归,随机森林和神经网络模型来预测技术功能性质.
- 根据预测的准确性和物理可行性选择最佳模型.
主要成果:
- 开发了一个统一的模型来预测黄色豆和狼混合物的每个技术功能性质.
- 使用多作物数据的模型显示,与单作物特定模型相比,预测误差更高.
- 分析表明,在没有显著信息丢失的情况下,可以减少某些属性的数据集大小.
结论:
- 配料混合物的技术功能性质可以在不同的作物中建模,为食品配方提供一种通用的方法.
- 虽然单个模型可以实现多种作物,但它涉及到预测准确性的妥协.
- 进一步的研究可以优化数据要求,以便对植物性成分功能进行强大的预测建模.
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