Digital Agriculture Food and Wine Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, Australia.
Department of Wine, Food and Molecular Biosciences, Faculty of Agriculture and Life Sciences, Lincoln University, Lincoln 7647, Canterbury, New Zealand.
Sensors (Basel). 2021 Nov 17;21(22):7641. doi: 10.3390/s21227641.
New and emerging non-invasive digital tools, such as eye-tracking, facial expression and physiological biometrics, have been implemented to extract more objective sensory responses by panelists from packaging and, specifically, labels. However, integrating these technologies from different company providers and software for data acquisition and analysis makes their practical application difficult for research and the industry. This study proposed a prototype integration between eye tracking and emotional biometrics using the BioSensory computer application for three sample labels: Stevia, Potato chips, and Spaghetti. Multivariate data analyses are presented, showing the integrative analysis approach of the proposed prototype system. Further studies can be conducted with this system and integrating other biometrics available, such as physiological response with heart rate, blood, pressure, and temperature changes analyzed while focusing on different label components or packaging features. By maximizing data extraction from various components of packaging and labels, smart predictive systems can also be implemented, such as machine learning to assess liking and other parameters of interest from the whole package and specific components.
新出现的非侵入式数字工具，如眼动追踪、面部表情和生理生物识别技术，已经被应用于从包装特别是标签中提取更客观的感官反应。然而，将这些来自不同公司供应商和软件的数据采集和分析技术整合到一起，对研究和行业来说是具有挑战性的。本研究提出了一个使用 BioSensory 计算机应用程序将眼动追踪和情感生物识别结合在一起的原型，用于三个样本标签：甜菊糖、薯片和意大利面条。呈现了多元数据分析，展示了所提出的原型系统的综合分析方法。可以对该系统进行进一步的研究，并整合其他生物识别技术，如与心率、血压和温度变化相关的生理反应，同时关注不同的标签组件或包装特征。通过最大化从包装和标签的各个组件中提取数据，也可以实现智能预测系统，例如使用机器学习来评估对整个包装和特定组件的喜好和其他感兴趣的参数。
