Abstract
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.
Highlights
This study aimed to propose the integration of eye-tracking information and emotional response of sensory panelists to assess specific areas of interest (AOI) of labels, such as images, logos, and nutrition information, among others, and self-reported liking of the overall label
Theon analyses presented in thisand paper are an vision example of how the data may be handled; The analyses presented in this paper are an example of how data maytheir be handled; each user of the proposed method would be free the to analyze own data each user of the proposed method would be free to analyze their own ac- as according to their needs
ANOVAs may be conducted to assess differences per as prepresented in this paper, and per sample and the interaction of AOIs and samples; this sented in this paper, and per sample and the interaction of AOIs and samples; this will depend on the aim of the specific study
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Packaging and labels are the first points of contact between food and beverage products with consumers. Around 95% of food and beverage products that do not have consumer preference assessments for packaging will probably fail in the market [1]. The implementation of new and emerging digital technologies for sensory analysis of food, beverage, and packaging products, such as video acquisition for physiological [2,3,4,5,6], emotional [7,8,9], and eye-tracking data [10,11,12], requires multiple devices from different companies and respective software packages for data acquisition, handling, and analysis [13]
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