应用人工智能驱动的多维食品打印技术用于食品副产品的价值化
Yuchen Ma1,2, Ling Fu3, Fatao He4
1Department of Food Science & Technology, National University of Singapore, Science Drive 2, Singapore 117542, Singapore.
Journal of agricultural and food chemistry
|January 30, 2026
概括
本综述探讨了使用机器学习 (ML) 与多维食品打印 (FP) 来将食品副产品转化为有价值的资源. 机器学习的整合提高了印刷过程,使可持续的食品制造和减少浪费成为可能.
科学领域:
- 食品科学与技术 食品科学与技术
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
背景情况:
- 食品副产品富含营养,但往往被浪费,造成环境和资源挑战.
- 传统的增材制造 (AM) 面临着食品副产品的困难,原因是材料的变化和复杂的打印要求.
研究的目的:
- 审查机器学习 (ML) 与多维食品打印 (FP) 的整合,以提升食品副产品的价值.
- 探索ML在提高食品印刷技术能力方面的应用.
主要方法:
- 对食品印刷中的ML应用现有文献的审查.
- 分析ML在预测风病,优化配方和控制印刷过程中的作用.
- 探索 ML 驱动的适应式打印和变形预测,用于 4D 和 5D/6D 打印.
主要成果:
- ML显著增强了食品副产品的添加剂制造管道,改善了类风学预测和工艺控制.
- ML促进了各种副产品 (动物,植物,油) 在3D和4D打印应用中的功能转化.
- ML支持自适应式打印,实时调整,以及高级食品打印中复杂结构构建的潜力.
结论:
- 机器学习驱动的食品打印通过提升食品副产品的价值,为可持续生产和零废物制造提供了一条途径.
- 主要挑战包括数据限制和模型可转移性,需要进一步研究.
- 未来与物联网和云平台的整合可以导致自主和可扩展的食品制造系统.
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