Exploring the hedonic and incentive properties in preferences for bitter foods via self-reports, facial expressions and instrumental behaviours

Analysis of Pungency Sensation Effects from an Oral Processing, Sensorial and Emotions Detection Perspective—Case Study with Grilled Pork Meat

Pungency is an interesting sensory stimulus analyzed from different perspectives, in particular the underpinning mechanisms of its sensation and perception. In this study, grilled pork meat coated with three types of hot sauces were investigated regarding its main food oral processing characteristics and evaluated using time-intensity and temporal dominance of pungency sensations methods analyzing the pungency descriptors and intensities. Besides these methods, facial expressions obtained from video capturing were subject to emotion detection. Mastication parameters showed a slight, but not statistically significant, trend of an increased number of chews and consumption time associated with pungency intensity, while saliva incorporation indicated an increasing trend depending on the pungency intensity, especially after 25 strokes and before swallowing. Both time intensity and temporal dominance of pungency sensations showed that the complexity of understanding these sensations is in relation to intensity and type. Finally, the use of emotion detection software in analyzing the faces of panelists during mastication confirmed the increase in non-neutral emotions associated with the increase in pungency intensity.

The State of Automated Facial Expression Analysis (AFEA) in Evaluating Consumer Packaged Beverages

In the late 1970s, analysis of facial expressions to unveil emotional states began to grow and flourish along with new technologies and software advances. Researchers have always been able to document what consumers do, but understanding how consumers feel at a specific moment in time is an important part of the product development puzzle. Because of this, biometric testing methods have been used in numerous studies, as researchers have worked to develop a more comprehensive understanding of consumers. Despite the many articles on automated facial expression analysis (AFEA), literature is limited in regard to food and beverage studies. There are no standards to guide researchers in setting up materials, processing data, or conducting a study, and there are few, if any, compilations of the studies that have been performed to determine whether any methodologies work better than others or what trends have been found. Through a systematic Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) review, 38 articles were found that were relevant to the research goals. The authors identified AFEA study methods that have worked and those that have not been as successful and noted any trends of particular importance. Key takeaways include a listing of commercial AFEA software, experimental methods used within the PRISMA analysis, and a comprehensive explanation of the critical methods and practices of the studies analyzed. Key information was analyzed and compared to determine effects on the study outcomes. Through analyzing the various studies, suggestions and guidance for conducting and analyzing data from AFEA experiments are discussed.

Emerging Technologies Based on Artificial Intelligence to Assess the Quality and Consumer Preference of Beverages

Beverages is a broad and important category within the food industry, which is comprised of a wide range of sub-categories and types of drinks with different levels of complexity for their manufacturing and quality assessment. Traditional methods to evaluate the quality traits of beverages consist of tedious, time-consuming, and costly techniques, which do not allow researchers to procure results in real-time. Therefore, there is a need to test and implement emerging technologies in order to automate and facilitate those analyses within this industry. This paper aimed to present the most recent publications and trends regarding the use of low-cost, reliable, and accurate, remote or non-contact techniques using robotics, machine learning, computer vision, biometrics and the application of artificial intelligence, as well as to identify the research gaps within the beverage industry. It was found that there is a wide opportunity in the development and use of robotics and biometrics for all types of beverages, but especially for hot and non-alcoholic drinks. Furthermore, there is a lack of knowledge and clarity within the industry, and research about the concepts of artificial intelligence and machine learning, as well as that concerning the correct design and interpretation of modeling related to the lack of inclusion of relevant data, additional to presenting over- or under-fitted models.

Consumers' Perceptions and Preferences for Bitterness in Vegetable Foods: The Case of Extra-Virgin Olive Oil and Brassicaceae-A Narrative Review

The presence of some healthy phytochemicals in food can be paired with high bitterness, and consumers have a widespread avoidance toward bitter-tasting food. This causes a gap between preferences and healthy needs of consumers. Therefore, this review collected insights from literature belonging to different discipline domains in order to have a broad view of the current state-of-the-art about biochemical aspects and consumers’ perceptions and preferences toward foods with an enhanced bitter taste. In detail, we focused on two core products of the Mediterranean diet: Extra-virgin olive oil (EVOO) and Brassicaceae, both characterized by specific phytochemicals having strong healthy properties and bitter-pungent taste. Results suggested that, although bitter taste is a general driver of dislike, some exceptions can be represented by: niches of consumers (e.g., innovators and organic buyers), foods consumed with specific purposes (e.g., coffee, chocolate, and alcoholic beverages). The level of bitterness perceived by the consumers can be modulated through exposure, information on benefits, and elements within the environment (e.g., music). Thus, these insights can be used to develop specific campaigns aimed at promoting bitter (healthy) food, considering also the key role that could be played by food pairings.

Are (All) Consumers Averse to Bitter Taste?

The current study combined hedonic liking with non-hypothetical experimental auctions to measure consumer preferences for bitter tasting food and identify individual socio-demographic and psychographic characteristics that influence bitter aversion. Furthermore, the research analyzed whether consumer preferences for bitter food were influenced by sensory and health-related information. Findings reveal that respondents (N = 205) are not averse to bitter taste; while, socio-demographic traits influence bitter acceptance, as higher education level and gender (female) positively affect preferences, together with specific individual characteristics as high compensatory health beliefs. Moreover, results prove that participants positively respond to health-related information, whereas information on bitterness-taste generates lower preferences.

Sensory Profile, Consumer Preference and Chemical Composition of Craft Beers from Brazil

Craft beers are known for their distinct flavor, brew, and regional distribution. They are made using top-fermenting (ale) yeast, bottom-fermenting (lager) yeast, or through spontaneous fermentation. Craft beers are consumed and produced in Brazil in large quantities. However, they present a high level of polyphenols, which affects consumer preference as they may yield a taste of bitterness to beers. In this study, we analyzed the relationship between polyphenols and bitterness as well as the composition of the main styles of craft beers and consumer preference for them. Six different styles were analyzed according to their polyphenol content, bitterness, chemical composition, sensory profile, and preference. For preference, a panel of 62 untrained assessors was used. For sensory profile, quantitative descriptive analysis was performed using expert assessors (n = 8). The most preferred style was classic American pilsner, and the least preferred was standard American lager. The most preferred style showed less bitterness (9.52) and lower polyphenol content (0.61 mg EAG/mL), total solids (6.75 °Brix), and turbidity (7.27 NTU). This beer also exhibited reduced sensory notes of malty, fruity, smoked, hoppy, and phenolic but a higher perception of floral, sweet, and yeast notes; the bitterness attribute had a reduced perception. This study advances the understanding and complexity of the sensory profile of different styles of craft beers from Southern Brazil.

Correlation between hedonic liking and facial expression measurement using dynamic affective response representation

Emotional reactions towards products play an essential role in consumers' decision making, and are more important than rational evaluation of sensory attributes. It is crucial to understand consumers' emotion, and the relationship between sensory properties, human liking and choice. There are many inconsistencies between Asian and Western consumers in the usage of hedonic scale, as well as the intensity of facial reactions, due to different culture and consuming habits. However, very few studies discussed the facial responses characteristics of Asian consumers during food consumption. In this paper, explicit liking measurement (hedonic scale) and implicit emotional measurement (facial expressions) were evaluated to judge the consumers' emotions elicited by five types of juices. The contributions of this study included: (1) Constructed the relationship model between hedonic liking and facial expressions analyzed by face reading technology. Negative emotions "sadness", "anger", and "disgust" showed noticeable high negative correlation tendency to hedonic scores. The "liking" hedonic scores could be characterized by positive emotion "happiness". (2) Several emotional intensity based parameters, especially dynamic parameter, were extracted to describe the facial characteristic in sensory evaluation procedure. Both amplitude information and frequency information were involved in the dynamic parameters to remain more information of the emotional responses signals. From the comparison of four types of emotional descriptive parameters, the maximum parameter and dynamic parameter were suggested to be utilized for representing emotional state and intensities.