从机器学习到神经成像:对口味预测和识别技术的全面审查
Jingtao Wang1, Shan Wang2, Dongliang Li3
1Zhengzhou Tobacco Research Institute of CNTC, Fengyang Road 2, Zhengzhou, Henan 450001, PR China; College of Chemistry, Zhengzhou University, Kexue Avenue 100, Zhengzhou, Henan 450001, PR China.
Food research international (Ottawa, Ont.)
|December 6, 2025
概括
味道科学正在变得具有预测性,整合人工智能,生物传感器和神经成像. 这种"分子到大脑"的方法使客观的风味分析成为可能,并指导食品产品的创新.
科学领域:
- 味道科学 味道科学
- 感官科学是一种感官科学.
- 计算化学是一种计算化学.
背景情况:
- 传统的风味分析依赖于主观描述方法.
- 以前的评论往往侧重于孤立的技术进步.
- 正在发生一种转向预测性和机械化的风味科学的范式转变.
研究的目的:
- 为味道科学提供一个综合性的,多层次的研究框架.
- 综合从分子预测到大脑解释的进展.
- 为该领域提出一个统一的框架.
主要方法:
- 利用人工智能和计算模拟来进行in-silico风味化合物预测 (结构-活性和-受体建模).
- 使用具有功能性嗅觉/味觉受体的生物传感器,用于客观地识别风味.
- 应用神经成像技术 (EEG,fMRI) 来解码大脑对味道的反应.
主要成果:
- 人工智能和模拟彻底改变了香味化合物的in-silico预测.
- 生物传感器提供客观,仿生风味识别.
- 神经成像直接解码大脑对味道的综合感知反应.
结论:
- 一个集成的"分子到大脑"管道合成了各种技术进步.
- 这一框架作为未来风味科学研究的路线图.
- 该方法指导合理的食品产品设计,并推动行业创新.
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