味觉-EEG:基于脑电图和机器学习的乌玛米味觉评估的整体模型
Zhiyong Cui1, Ben Wu1, Imre Blank2
1Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai 200240, China.
Journal of agricultural and food chemistry
|August 28, 2023
概括
这项研究引入了电脑电图 (EEG) 方法来识别乌玛米物质,改进了主观的人类感官评估. 开发的TastePeptides-EEG模型实现了高准确度,为味觉感知分析提供了一种新的方法.
科学领域:
- 神经科学是一个神经科学.
- 食品科学 食品科学 食品科学
- 生物技术是生物技术.
背景情况:
- 传统的食物感官评估依赖于主观的人类评估,这可能是不一致的,容易产生偏见.
- 需要客观可靠的方法来分析味觉感知,特别是对于复杂的味道,如umami.
研究的目的:
- 开发一种基于脑电图 (EEG) 的方法,用于客观识别乌玛米物质.
- 建立一个机器学习模型来分析神经对乌玛米味道的反应.
- 为了研究乌玛米味觉的神经相关性.
主要方法:
- 从EEG数据中提取特征,使用百分比转换,标准化和显著性选.
- 选择性能最好的二进制分类算法作为子模型.
- 使用支持矢量机器 (SVM) 集成建模,以整合子模型输出,创建TastePeptides-EEG模型.
主要成果:
- 品味-EEG模型的准确性很高,在验证组中达到90.2%,在测试组中达到77.8%.
- 确定了与乌玛米味觉相关的α波的特定频率变化.
- 在乌玛米味觉刺激期间,在F/RT/C大脑区域发现了一种新的频率响应延迟现象.
结论:
- 基于EEG的分析提供了一个客观和有效的方法来识别umami物质,克服手动感官评估的局限性.
- 这项研究揭示了对umami味觉背后的神经机制的新见解.
- 公共可用的TastePeptides-EEG模型促进了对味道感知和用于味道分析的大脑与计算机接口的研究.
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