School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China; Bionic Sensing and Pattern Recognition Research Team, Northeast Electric Power University, Jilin 132012, China.
School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China; Bionic Sensing and Pattern Recognition Research Team, Northeast Electric Power University, Jilin 132012, China.
Food Chem. 2024 Oct 15;455:139816. doi: 10.1016/j.foodchem.2024.139816. Epub 2024 May 25.
As the need for food authenticity verification increases, sensory evaluation of food odors has become widely recognized. This study presents a theory based on electroencephalography (EEG) to create an Olfactory Perception Dimensional Space (EEG-OPDS), using feature engineering and ensemble learning to establish material and emotional spaces based on odor perception and pleasure. The study examines the intrinsic connection between these two spaces and explores the mechanisms of integration and differentiation in constructing the OPDS. This method effectively visualizes various types of food odors while identifying their perceptual intensity and pleasantness. The average classification accuracy for odor recognition in an eight-category experiment is 96.1%. Conversely, the average classification accuracy for sensory pleasantness recognition in a two-category experiment is 98.8%. The theoretical approach proposed in this study, based on olfactory EEG signals to construct an OPDS, captures the subtle perceptual differences and individualized pleasantness responses to food odors.
随着对食品真实性验证需求的增加，食品气味的感官评价已得到广泛认可。本研究提出了一种基于脑电图（EEG）的理论，通过特征工程和集成学习，基于气味感知和愉悦度构建材料和情感空间，创建嗅觉感知维度空间（EEG-OPDS）。本研究探讨了这两个空间之间的内在联系，并研究了在构建 OPDS 时的整合和分化机制。该方法可以有效地可视化各种类型的食物气味，同时识别它们的感知强度和愉悦度。在一个八类实验中，气味识别的平均分类准确率为 96.1%。相反，在一个两类实验中，感官愉悦度识别的平均分类准确率为 98.8%。本研究提出的基于嗅觉 EEG 信号构建 OPDS 的理论方法，捕捉到了对食物气味的微妙感知差异和个性化愉悦反应。
