School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, VIC 3010, Australia.
School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, VIC 3010, Australia.
Physiol Behav. 2019 Mar 1;200:139-147. doi: 10.1016/j.physbeh.2018.02.051. Epub 2018 Mar 5.
Traditional sensory tests rely on conscious and self-reported responses from participants. The integration of non-invasive biometric techniques, such as heart rate, body temperature, brainwaves and facial expressions can gather more information from consumers while tasting a product. The main objectives of this study were i) to assess significant differences between beers for all conscious and unconscious responses, ii) to find significant correlations among the different variables from the conscious and unconscious responses and iii) to develop a model to classify beers according to liking using only the unconscious responses. For this study, an integrated camera system with video and infrared thermal imagery (IRTI), coupled with a novel computer application was used. Videos and IRTI were automatically obtained while tasting nine beers to extract biometrics (heart rate, temperature and facial expressions) using computer vision analysis. Additionally, an EEG mobile headset was used to obtain brainwave signals during beer consumption. Consumers assessed foam, color, aroma, mouthfeel, taste, flavor and overall acceptability of beers using a 9-point hedonic scale with results showing a higher acceptability for beers with higher foamability and lower bitterness. i) There were non-significant differences among beers for the emotional and physiological responses, however, significant differences were found for the cognitive and self-reported responses. ii) Results from principal component analysis explained 65% of total data variability and, along with the covariance matrix (p < 0.05), showed that there are correlations between the sensory responses of participants and the biometric data obtained. There was a negative correlation between body temperature and liking of foam height and stability, and a positive correlation between theta signals and bitterness. iii) Artificial neural networks were used to develop three models with high accuracy to classify beers according to level of liking (low and high) of three sensory descriptors: carbonation mouthfeel (82%), flavor (82%) and overall liking (81%). The integration of both sensory and biometric responses for consumer acceptance tests showed to be a reliable tool to be applied to beer tasting to obtain more information from consumers physiology, behavior and cognitive responses.
传统的感官测试依赖于参与者有意识和自我报告的反应。将非侵入性生物识别技术（如心率、体温、脑电波和面部表情）与感官测试相结合，可以从消费者那里收集更多信息。本研究的主要目的是：i）评估所有有意识和无意识反应的啤酒之间的显著差异，ii）发现有意识和无意识反应的不同变量之间的显著相关性，iii）开发一种仅使用无意识反应来根据喜好对啤酒进行分类的模型。为此，本研究使用了集成的摄像机系统，该系统结合了视频和红外热成像技术（IRTI），并辅以新颖的计算机应用程序。在品尝九种啤酒时，自动获取视频和 IRTI，以使用计算机视觉分析提取生物特征（心率、温度和面部表情）。此外，还使用 EEG 移动耳机在饮用啤酒时获取脑电波信号。消费者使用 9 分愉悦量表评估啤酒的泡沫、颜色、香气、口感、味道、风味和整体可接受性，结果表明，泡沫丰富度和苦味较低的啤酒更受欢迎。i）在情绪和生理反应方面，啤酒之间没有显著差异，但在认知和自我报告反应方面存在显著差异。ii）主成分分析的结果解释了总数据变异性的 65%，加上协方差矩阵（p<0.05），表明参与者的感官反应与获得的生物识别数据之间存在相关性。体温与对泡沫高度和稳定性的喜好呈负相关，与θ信号与苦味呈正相关。iii）人工神经网络用于开发三个模型，以高精度根据三个感官描述符（碳酸口感、风味和整体喜好）的喜好程度（低和高）对啤酒进行分类：碳酸口感（82%）、风味（82%）和整体喜好（81%）。将感官和生物识别反应相结合用于消费者接受测试，被证明是一种可靠的工具，可以应用于啤酒品尝，从消费者的生理、行为和认知反应中获取更多信息。
