BCC Innovation, Technology Center in Gastronomy, Basque Culinary Center, 20009 Donostia-San Sebastián, Spain.
Basque Culinary Center, Faculty of Gastronomic Sciences, Mondragon Unibertsitatea, 20009 Donostia-San Sebastián, Spain.
Sensors (Basel). 2022 Sep 8;22(18):6787. doi: 10.3390/s22186787.
Using implicit responses to determine consumers' response to different stimuli is becoming a popular approach, but research is still needed to understand the outputs of the different technologies used to collect data. During the present research, electroencephalography (EEG) responses and self-reported liking and emotions were collected on different stimuli (odor, taste, flavor samples) to better understand sweetness perception. Artificial intelligence analytics were used to classify the implicit responses, identifying decision trees to discriminate the stimuli by activated sensory system (odor/taste/flavor) and by nature of the stimuli ('sweet' vs. 'non-sweet' odors; 'sweet-taste', 'sweet-flavor', and 'non-sweet flavor'; and 'sweet stimuli' vs. 'non-sweet stimuli'). Significant differences were found among self-reported-liking of the stimuli and the emotions elicited by the stimuli, but no clear relationship was identified between explicit and implicit data. The present research sums interesting data for the EEG-linked research as well as for EEG data analysis, although much is still unknown about how to properly exploit implicit measurement technologies and their data.
使用内隐反应来确定消费者对不同刺激的反应正成为一种流行的方法，但仍需要研究来了解用于收集数据的不同技术的输出。在本研究中，收集了不同刺激（气味、味道、味道样本）的脑电图（EEG）反应和自我报告的喜好和情绪，以更好地理解甜味感知。人工智能分析用于对隐式反应进行分类，确定决策树，通过激活的感觉系统（气味/味道/味道）和刺激的性质（“甜”与“非甜”气味；“甜味”、“甜味”和“非甜味”；以及“甜刺激”与“非甜刺激”）来区分刺激。在自我报告的对刺激的喜爱程度和刺激引起的情绪之间发现了显著差异，但在显式和隐式数据之间没有明确的关系。本研究为与 EEG 相关的研究以及 EEG 数据分析汇总了有趣的数据，尽管对于如何正确利用隐式测量技术及其数据，还有很多未知之处。
