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Article

FaceReader Insights into the Emotional Response of Douro Wines

by
Catarina Marques
1 and
Alice Vilela
2,*
1
CITAB, Centre for the Research and Technology of Agro-Environmental and Biological Sciences and Inov4Agro, University of Trás-os-Montes and Alto Douro, 5000 Vila Real, Portugal
2
CQ-VR, Chemistry Research Center, Department of Agronomy, School of Agrarian and Veterinary Sciences, University of Trás-os-Montes and Alto Douro, 5000 Vila Real, Portugal
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(21), 10053; https://doi.org/10.3390/app142110053
Submission received: 13 September 2024 / Revised: 23 October 2024 / Accepted: 31 October 2024 / Published: 4 November 2024
(This article belongs to the Special Issue Holistic Approaches in Sensory Research on Food and Non-food Products Using New Techniques and Digital Tools)
Browse Figures
Figure 1
<p>FaceReader screenshot recording the emotion “surprised”. Image retrieved from <a href="https://www.noldus.com/facereader/set-up" target="_blank">https://www.noldus.com/facereader/set-up</a>, accessed on 11 September 2024.</p> "> Figure 2
<p>Emotional profile elicited by the wine. (<b>a</b>) WW1; (<b>b</b>) WW2; (<b>c</b>) RW1; (<b>d</b>) RW2; (<b>e</b>) PW1; and (<b>f</b>) PW2.</p> "> Figure 3
<p>Arousal elicited by the wine per gender. (<b>a</b>) WW1; (<b>b</b>) WW2; (<b>c</b>) RW1; (<b>d</b>) RW2; (<b>e</b>) PW1; and (<b>f</b>) PW2.</p> "> Figure 3 Cont.
<p>Arousal elicited by the wine per gender. (<b>a</b>) WW1; (<b>b</b>) WW2; (<b>c</b>) RW1; (<b>d</b>) RW2; (<b>e</b>) PW1; and (<b>f</b>) PW2.</p> "> Figure 4
<p>An explanatory graphic obtained after PCA shows that Douro’s white, red, and Port wines (green squares) align according to the emotions they elicit (green triangles).</p> ">
Versions Notes

Abstract

:
Understanding consumers’ emotional responses to wine is essential for improving marketing strategies and product development. Emotions play a pivotal role in shaping consumer preferences. This study investigates the emotional reactions elicited by different types of Douro wines (white, red, and Port) through facial expression analysis using FaceReader software, version 9.0 (Noldus Information Technology, Wageningen, The Netherlands). A total of 80 participants tasted six wine samples, and their facial expressions were recorded and analyzed. FaceReader quantified the intensity of emotions such as happiness, sadness, anger, surprise, fear, and disgust. Arousal levels were also assessed. The results were analyzed through principal component analysis (PCA) to identify patterns and groupings based on emotional responses. White wines evoked more sadness due to their acidity, while red wines were associated with lower levels of sadness and greater comfort. Port wines elicited surprise, probably due to their sweet and fortified nature. Additionally, female participants showed consistently higher arousal levels than males across all wine types. The study highlights distinct emotional profiles for each type of wine and suggests that demographic factors, such as gender, influence emotional responses. These insights can inform targeted marketing and enhance the consumer experience through better alignment of wine characteristics with emotional engagement.

1. Introduction

Nowadays, the emotional state of consumers has become a widely applied marketing strategy to increase consumption, as emotions play a fundamental role in the process of purchasing a product [1,2,3]. Assessing wine extends beyond just sensory analysis; it includes an experience of an emotional response that is intrinsically linked to their sensory perception [4,5]. This emotional response can significantly influence how products are evaluated and preferred by consumers [6,7,8]. Emotions are a systematic array of chemical and neuronal responses generated by the brain upon detecting an emotionally significant stimulus, such as an object or situation. These responses involve biochemical processes and neural activity that influence an individual’s emotional state, affecting their behavior and perceptions [9,10,11]. Remarkably, the emotional evaluation of wine involves grasping the delicate intricacies of its flavor, texture, and bouquet, which together create a comprehensive experience. This experience can stir emotions such as pleasure, nostalgia, or even excitement, enhancing the overall pleasantness of the wine [12].
The quantification of emotions through facial expressions has been extensively studied in psychology and has sparked interest in evaluating basic emotions during the consumption of food products [13]. In response to this premise, the FaceReader program (by Noldus, Wageningen, The Netherlands) was developed, an innovative computational system that allows the direct capture of facial expressions of an individual exposed to a stimulus and the quantification of basic emotions over a defined period. The organoleptic properties of beverages significantly influence consumer behavior, as they can trigger involuntary facial expressions [14,15,16,17]. The FaceReader system is software that detects facial expressions within a short time frame and classifies them into seven categories: happy, sad, angry, surprised, scared, disgusted, and neutral, although it is possible to add more expressions to the system. It operates in three phases: discovering, modeling, and classifying the consumer’s face. This evaluation has been recently applied in food and beverage tasting contexts due to the emotional, descriptive information detected and the real-time acceptance of products [18].
However, while several studies use the advanced facial recognition software FaceReader to correlate emotions with other types of food and beverages [5,19,20,21,22], there is a notable gap in research explicitly examining the emotional reactions elicited by different types of wine, notably including one of the remarkable wines in the Douro region, Port wine. Although a study by Marques et al. [5] investigated emotional responses to white and red wines, it did not explore Port wine, leaving a gap in understanding how this unique category of wine affects consumer emotions.
This study aims to investigate the emotional responses elicited by different types of Douro wines (white, red, and Port wine) using the FaceReader software. By analyzing facial expressions during wine tasting, the study seeks to identify and quantify the emotions associated with the consumption of various wine categories, providing insights into how these emotional reactions correlate with the sensory characteristics of the wines.

2. Materials and Methods

2.1. Participant Recruitment and Informed Consent

Participants were recruited for this study through digital platforms and faculty members. Considering the number of participants recruited by Rocha et al. [21], 50 naïve panelists, as a reference for the minimal number, and 80 adult participants, ages between 18 and 64, including 30 men and 50 women, were selected as a robust and reliable number of participants to participate in the study voluntarily. Participants were selected based on voluntary willingness to participate, with no incentives offered to ensure unbiased participation.
Regarding the sampling method, a non-probability convenience sampling approach was employed, where participants were recruited primarily from individuals accessible through university networks and online platforms. This method allowed for the efficient recruitment of participants within the defined demographic, although it limits the generalizability of the results.
To provide a more detailed demographic breakdown of the participants in this study, they were grouped by age as follows: 18–24 years (20 participants), 25–34 years (18 participants), 35–44 years (15 participants), 45–54 years (14 participants), and 55–64 years (13 participants).
Before the test commenced, each participant provided informed consent by voluntarily signing a consent form. The form ensured personal data protection and confidentiality and allowed the recording of participants’ images. According to the European General Data Protection Regulation, all collected data were treated anonymously to protect the participant’s privacy and confidentiality.

2.2. Samples

Six wine samples from the Douro Region were chosen randomly to assess the emotional response to wine tasting; two were selected for each category (white, red, and Port wine). Table 1 presents the wine type, sample code, grape varieties, alcohol content, total acidity, pH, and tasting temperature of the samples used in the study. The alcohol content was present on the bottle label. The grape varieties of each wine were recovered from the wine’s internet site. Total acidity and pH were evaluated in the laboratory by applying OIV analytical methods [23], namely titration with bromothymol blue as an indicator and comparison with an end-point color standard for the determination of the total acidity and potentiometry for the pH determination.
These wine samples were selected based on the grape varieties they contain. They represent wines produced in the Douro Region, including DOC and Port wines, made from the main grape varieties approved by the IVV (Instituto da Vinha e do Vinho) for this region. Moreover, one of the samples (PW1) also contains grapes from an old vine, pre-filoxera (Daktulosphaira vitifoliae) grape vines. Old vines still exist in some slops in the Douro Valley.
The wines were served at their respective recommended temperatures. White and sweet wines are generally served between 7 and 12 °C, while red wines are typically served at a slightly higher temperature. This temperature consistency was maintained across all samples (white, red, and Port wines) during the tasting.

2.3. Procedure and Materials

The study occurred during the winter and spring seasons, coinciding with the academic period. By the morning, the participants attended a 30 min session at the University of Trás-os-Montes and Alto Douro, during which we explained the study procedure and its purpose to them.
Three sessions were performed according to the type of wine (white, red, and Port wine), each served at room temperature. In the same session, the tasters were given 2 min between samples and instructed to drink water to clean their palates.
During the tasting/recording session, participants comfortably sat approximately 1 m away from a Samsung mobile phone, which was used to record their faces against a plain white background with suitable lighting conditions. Participants refrained from talking during the stimulation phase to ensure a clear frontal view of their faces. Participants were informed that the evaluation would involve tasting samples of wine and were instructed to avoid unnecessary movements that could interfere with the measurements.
Approximately 25 mL of wine was poured into a 21.5 cl ISO tasting glass [24] for each sample. The participants put the wine in their mouths, and the video recording started, lasting between 10 and 20 s per sample. The recordings were captured at 2160 × 3840 (UHD) resolution, ensuring high-quality visual data for subsequent analysis. This standardized procedure allowed the consistent and controlled recording of participants’ facial reactions while tasting the wine samples, facilitating the collection of a comprehensive dataset for further examination through a facial expression reader.

2.4. Data Analysis by FaceReader

The collected results were analyzed using FaceReader software, version 9.0 (Noldus Information Technology, Wageningen, The Netherlands). For video analysis, the basic version of the program was utilized, employing the default face model and a sample rate set to capture every frame. Subsequently, an Excel file containing quantified emotions was generated.
Data analysis involved three stages, following the methodology described by Wijk et al. [25]. Firstly, face detection proceeded to model facial expressions and concluded with classifying expressions using an artificial neural network. This classification process allowed the quantification of basic emotions, including happy, sad, angry, surprised, scary, and disgusted. The quantification of these emotions ranged between 0 (indicating no detection of the emotion) and 1 (representing maximum detection). FaceReader has been trained using intensity values annotated by human experts. A mixture of emotions often causes facial expressions; two (or more) expressions may coincide with high intensity. Therefore, the sum of the intensity values for the expressions at a particular time point usually does not equal 1.
For some people, FaceReader can be biased toward certain expressions. So, to correct person-specific biases, participant calibration was performed using images in which the participant looks neutral. The calibration procedure uses the image or frame with the lowest model error and expressions other than neutral found in this image for calibration.
FaceReader also assesses facial arousal, showing whether the test participant is active (+1) or inactive (0). This arousal is determined by evaluating the activity of 20 action units (AUs) from the facial action coding system (FACS) [26].
The calculation of arousal involves using the activation values (AV) of 20 specific action units (AUs) as input. These include AU 1 (inner brow raiser), AU 2 (outer brow raiser), AU 4 (brow lowerer), AU 5 (upper lid raiser), AU 6 (cheek raiser), AU 7 (lid tightener), AU 9 (nose wrinkler), AU 10 (upper lip raiser), AU 12 (lip corner puller), AU 14 (dimpler), AU 15 (lip corner depressor), AU 17 (chin raiser), AU 20 (lip stretcher), AU 23 (lip part), AU 24 (lip pressor), AU 25 (lips part), AU 26 (jaw drop), and AU 27 (mouth stretch), along with the inverse of AU 43 (eyes closed). AU 43 is inverted because closed eyes generally indicate low arousal, contrasting with the other AUs that typically signify high arousal. The average AU activation values (AAV) are calculated over the tasting. During the initial seconds of the analysis, the AAV is based on the data available up to that point, where the AAV is computed as the mean of the AV over the preceding seconds. These average values are then subtracted from the current AU activation values to obtain the corrected activation values (CAV). This step adjusts for consistently activated AUs, which might be attributed to an individual’s inherent expression tendencies. The CAV is determined as follows: CAV = max(0, AV − AAV). Finally, arousal is measured by averaging the five highest CAV, resulting in arousal = mean (of the top 5 CAV) [27]. Figure 1 shows a FaceReader screenshot recording the emotion “surprised”.

2.5. Ethical Statement

The ethics committee of the University of Trás-os-Montes and Alto Douro approved this project. All participants provided a written informed consent form. The participants received no financial compensation. The reference number is Doc91-CE-UTAD-2022, 9 November 2022.

2.6. Statistical Data Treatment

An Excel (Microsoft) file containing the quantified emotions was generated. Then, the data were exported to SPSS (IBM SPSS Statistics 29) to perform a PCA to illustrate the emotional experiences reported by participants.

3. Results and Discussion

3.1. Emotional Response

The results obtained from FaceReader for each wine sample analyzed in this section are displayed in Figure 2a–f, which illustrates the emotional profiles elicited by different types of wines, categorized as white (WW1, WW2), red (RW1, RW2), and Port (PW1, PW2). The authors analyzed the results according to the program instructions and took into account that one of the authors is a wine expert.
Each emotional profile spans 12 s, covering the interval from when the sample is ingested until the microexpressions reach maximum neutrality. It is crucial to highlight that, apart from the emotions illustrated in the figure, ‘neutral’ was the emotion most consistently detected. The ‘neutral’ response was excluded from the figures to enhance our understanding of the remaining measured emotions. This exclusion was made to provide a more comprehensive analysis of the other emotional responses captured during the tasting experience.
The emotions recorded are grouped into six categories: happiness, sadness, anger, surprise, fear, and disgust. The values in the figure indicate the intensity or likelihood of each emotion being exhibited at different time points during the tasting experience.
White wines WW1 and WW2 show a notable sadness, especially at the initial tasting moments (1st second, 0.244 and 0.224). This could be correlated with the acidity and freshness characteristic of white wines, which may evoke a more melancholic response than other wine types. The acidity (5.6 and 6.0 g/L tartaric acid; pH 3.4 and 3.2, respectively—Table 1) might have stimulated a sensory experience perceived as less comforting, hence the association with sadness. The sadness is typically characterized by raised inner eyebrows and lowered corners of the mouth [28] and is linked to a lowered mood, discouragement, and melancholy. Additionally, this emotion is associated with a lack of energy and a sense of fatigue [28,29]. Furthermore, when a wine elicits an unpleasant response, such as excessive volatile acidity, which produces a vinegar-like aroma and taste, the taster will likely reject the sample and verbally request an alternative wine. This underscores how certain sensory flaws, particularly those related to acidity, can lead to negative emotional experiences and ultimately result in the rejection of the wine [13].
More favorable emotions might emerge if the evaluations were extended as the taster became more accustomed to the wine and its characteristics. White wines are also the only wines in the study that manifest some gripping intensity of happiness around seconds 6 and seconds 10 and 11. This indicates that as the perception of acidity diminished due to saliva lubrication, the tasters began to appreciate more prominent flavor notes. This suggests that happiness might be connected to the satisfaction and contentment experienced when tasting these enjoyable flavors.
In this study, happiness is the only positive emotion linked to feelings of contentment and satisfaction that are usually expressed through smiling [30,31,32].
In contrast to the white wines, red wines RW1 and RW2 exhibit a reduction in the expression of sadness. Red wines are typically fuller-bodied and richer in flavor, with less acidity (in our wines—RW1 and RW2-5.3 and 5.11 g/L tartaric acid for total acidity and pH of 3.7 and 3.96, respectively, Table 1), which may evoke a more pleasant and comforting emotional response, thereby diminishing feelings of sadness.
Port wines PW1 and PW2 show a heightened response to the emotion of surprise. This could be linked to the fortified nature of these wines (19.7 and 19.4% vol., respectively, Table 1), which are also sweet (around 90–130 g/L in sugars) and more intense, leading to a surprising sensory experience. Additionally, the reduction in sadness in these wines suggests that their sweetness and complexity may counterbalance any negative emotions.
Overall, scared, disgusted, and angry were the least intense emotions. Being familiar with the presented product can lessen the emotion of fear, as prior experience and recognition of its sensory characteristics play a role [33,34].
Other factors may influence emotional responses to wines besides wine type. For instance, the quality of wine can be linked to more pleasant or unpleasant emotions [35]. The inherent qualities of wine can also influence the strength of emotions felt during wine consumption [36].
These findings illustrate how different types of wines can evoke distinct emotional profiles. White wines, due to their acidity, are more associated with sadness, red wines reduce sadness, and Port wines, due to their unique characteristics, elicit surprise.

3.2. Arousal per Gender

Arousal, as measured by FaceReader, reflects the intensity of a participant’s emotional state, ranging from calm to highly active. In this context, arousal is derived from analyzing facial expressions to determine how engaged or stimulated an individual is during a specific experience. FaceReader calculates this arousal by assessing the activation of various facial muscles, mainly focusing on the action units (AUs) associated with emotional intensity. Higher arousal levels indicate greater emotional engagement, while lower levels suggest a more relaxed state.
Figure 3 illustrates the arousal levels induced by different wines across genders, presented separately for each wine type: (a) WW1, (b) WW2, (c) RW1, (d) RW2, (e) PW1, and (f) PW2.
The analysis of arousal levels using FaceReader (Figure 3) reveals a consistent pattern across all wine samples, where females exhibited higher arousal than males. This heightened arousal among female participants suggests a more robust emotional response to the tasting experience. The difference in arousal levels could be attributed to several factors, including potential variations in sensitivity to taste or emotional expressiveness between genders. Females may be more attuned to subtle flavor nuances, leading to a more pronounced emotional reaction [37,38], or they might naturally express their emotional states more intensely through facial expressions [39,40]. This consistent trend across all wine varieties emphasizes the need to consider gender differences when interpreting emotional responses in sensory evaluations.
Moreover, these findings align with previous research by Ashton et al. [41], which found that consumers’ emotional responses to alcoholic beverages can vary depending on the type of wine. While red wine often induces feelings of relaxation and fatigue, white wine generally does not elicit strong emotional reactions. This distinction, coupled with the observed gender differences in arousal, underscores the complex interplay between wine type, gender, and emotional response.

3.3. Statistical Grouping Analysis

A principal component analysis (PCA) was carried out to categorize and understand patterns within the emotional responses. This allows the grouping of wines based on the emotions they elicit. By visualizing how different wines cluster together according to the emotional responses they evoke, it is possible to identify similarities or relationships among the various variables.
Figure 4 presents an explanatory graphic derived from PCA, illustrating how white, red, and Port wines from Douro are aligned based on the specific emotions they evoke.
Figure 4 presents three clear groups based on wine types: white, red, and Port wine, consistent with the results in Figure 2. To identify the structure of relationships among the observed data, a principal component analysis (PCA) was performed. The first principal component explains 75.15% of the total variance, and the second explains 24.85%. Together, these two components capture the emotional responses elicited by the different wine types.
White wines evoke stronger and more diverse emotional reactions, as they are freshening and acidic wines linked to the aromatic compounds produced during grapevine metabolism, including terpenes, norisoprenoids, benzene derivatives, and C6 alcohols [42]. Köster and Mojet [43] suggest that emotional responses are often shaped by physiological factors (hunger, satiation, and physiological reward mechanisms), psychological factors (age, expectations based on previous experiences, memory and habit formation, emotional coping mechanisms, restrained eating tendencies or personality traits such as manic–depressive tendencies), and sociological factors (economic status, social nature of the eating situation, or eating culture). Emotional state influences how pleasant food is, and the effect varies between new and familiar foods [30]. For instance, sweetness is commonly linked to positive emotions, whereas bitterness can provoke negative feelings [44,45]. Furthermore, flavors such as salty and sour can trigger many emotions, including surprise, sadness, and fear.
Port wines, while not as strongly associated with emotional diversity as white wines, show a notable connection to happiness, surprise, and fear. These wines, known for their sweetness and richness, evoke a heightened response to the emotion of surprise, which could be attributed to their complex flavor profiles and the contrast between sweetness and other flavor dimensions, such as bitterness or alcohol warmth. Port wines may elicit more reflective or bittersweet emotions, possibly due to the more profound, more contemplative experience they provide. The sociocultural context in which Port wines are typically consumed, often in relaxed or celebratory settings, could also influence the emotional responses associated with them. Studies on multisensory food and drink experiences have shown that wine’s sensory complexity can evoke surprise when unexpected flavor contrasts, such as between sweetness and bitterness, are encountered [46,47].
On the other hand, red wines occupy a separate cluster in the PCA plot, suggesting an absence of emotional triggers. Away from variables related to emotions like happiness, anger, or sadness, red wines appear to evoke more neutral or subdued emotional reactions. This could be due to the tannic or astringent properties commonly found in red wines, which may lead to a more restrained emotional response. Tannins, which contribute to the mouth-drying sensation, are a significant component in red wine and can lead to a perception of astringency. This drying and puckering sensation is primarily a result of tannin binding with salivary proteins, leading to the aggregation of protein-tannin complexes that heighten this sensation. Astringency, while an essential characteristic of red wine, is often perceived as less pleasant when not balanced with sweetness or other flavors, which might explain why red wines evoke more neutral or subdued emotional reactions compared to sweeter wines like Port [48,49]. Instead of eliciting intense emotions, red wines are often associated with feelings of familiarity and comfort [50]. This might suggest that red wines, being more robust and complex, are typically enjoyed in contexts where the emotional experience is less about immediate pleasure or surprise and more about a sense of comfort, tradition, or calmness. They may be more aligned with routine consumption and less likely to evoke pronounced emotional responses than the more vibrant reactions elicited by white or Port wines.

4. Conclusions

This study uses the advanced facial recognition software FaceReader to provide valuable insights into the emotional responses elicited by different types of Douro wines. The findings reveal that various types of wine (white, red, and Port wine) evoke distinct emotional profiles among consumers.
Notably, white wines induce a more diverse range of emotions, with sadness being prominent due to their acidity. In contrast, red wines evoke less sadness, possibly due to their more affluent and fuller-bodied nature. With their unique characteristics, Port wines often lead to surprise, highlighting the influence of wine type on emotional reactions.
The analysis of arousal levels further indicates that female participants consistently exhibit higher arousal across all wine samples than males. This suggests that women may have a heightened sensitivity to the tasting experience due to greater attunement to subtle flavor nuances or more intense emotional expressiveness. These gender differences in emotional response underscore the importance of considering demographic factors when interpreting sensory evaluations.
Principal Component Analysis (PCA) allowed for categorizing wines based on the emotions they elicit, highlighting clear distinctions between the emotional experiences associated with white, red, and Port wines. This analysis reinforces the idea that wine type plays a crucial role in shaping emotional responses and suggests potential applications in marketing and product development, where tailoring wine offerings to evoke desired emotional responses could enhance consumer satisfaction.
FaceReader offers several advantages for assessing emotional responses during wine tasting, including objective, real-time emotion detection, non-invasiveness, and time-efficient analysis. It provides standardized output across basic emotions like happiness and surprise, which can help compare consumer reactions. However, its limitations include difficulty capturing more subtle or complex emotions, potential variability in accuracy for participants with facial features like beards or surgical changes, and challenges with cultural or individual differences in expression. While it detects primary emotional states accurately, it may struggle to capture the more profound, nuanced emotions often associated with wine-tasting experiences.
By understanding consumers’ emotional responses to different wine types, wine brands can effectively tailor their products and marketing campaigns to specific demographic segments, increasing consumer satisfaction and brand loyalty. Overall, this research contributes to a deeper understanding of the complex interplay between wine characteristics, consumer emotions, and demographic factors, offering valuable insights into the wine industry in both production and marketing strategies. Future research could explore the emotional responses elicited by other wine styles and incorporate additional sensory evaluation methods to further enrich our understanding of consumer preferences.

Author Contributions

Conceptualization, C.M. and A.V.; methodology, C.M.; software, C.M. and A.V.; validation, C.M. and A.V.; formal analysis, C.M.; investigation, C.M.; resources, C.M.; data curation, C.M. and A.V.; writing—original draft preparation, C.M.; writing—review and editing, C.M. and A.V.; visualization, C.M.; supervision, A.V.; funding acquisition, C.M. and A.V. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the project e-Flavor [grant number POCI-01-0247-FEDER-049337] sponsored by the FEEI, supported by the FEDER, through the Competitiveness and Internationalization Operational Program; by the CQ-VR [grant numbers UIDB/00616/2020 and UIDP/00616/2020, https://doi.org/10.54499/UIDB/00616/2020]; and by the CITAB [grant number UIDB/04033/2020], FCT, Portugal; and COMPETE. Additionally, this study was funded by the FCT—Portuguese Foundation for Science and Technology (grant number UI/BD/150728/2020), under the Doctoral Program “Agricultural Production Chains—from Fork to Farm” (grant number PD/00122/2012), and from the European Social Funds and the Regional Operational Program Norte 2020.

Institutional Review Board Statement

This project was approved by the ethics committee of the University of Trás-os-Montes and Alto Douro, and all participants provided a written informed consent form. The participants received no financial compensation—reference number Doc91-CE-UTAD-2022, 9 November 2022.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Acknowledgments

In addition to the financial support provided by the e-Flavor project, FCT—Portuguese Foundation for Science and Technology, and the research centers CQ-VR and CITAB, the authors would like to acknowledge the tasting panel that participated in the sensory tests.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. FaceReader screenshot recording the emotion “surprised”. Image retrieved from https://www.noldus.com/facereader/set-up, accessed on 11 September 2024.
Figure 1. FaceReader screenshot recording the emotion “surprised”. Image retrieved from https://www.noldus.com/facereader/set-up, accessed on 11 September 2024.
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Figure 2. Emotional profile elicited by the wine. (a) WW1; (b) WW2; (c) RW1; (d) RW2; (e) PW1; and (f) PW2.
Figure 2. Emotional profile elicited by the wine. (a) WW1; (b) WW2; (c) RW1; (d) RW2; (e) PW1; and (f) PW2.
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Figure 3. Arousal elicited by the wine per gender. (a) WW1; (b) WW2; (c) RW1; (d) RW2; (e) PW1; and (f) PW2.
Figure 3. Arousal elicited by the wine per gender. (a) WW1; (b) WW2; (c) RW1; (d) RW2; (e) PW1; and (f) PW2.
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Figure 4. An explanatory graphic obtained after PCA shows that Douro’s white, red, and Port wines (green squares) align according to the emotions they elicit (green triangles).
Figure 4. An explanatory graphic obtained after PCA shows that Douro’s white, red, and Port wines (green squares) align according to the emotions they elicit (green triangles).
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Table 1. Wine type, sample code, grape varieties, alcohol content, total acidity, pH, and tasting temperature of the samples used in the study.
Table 1. Wine type, sample code, grape varieties, alcohol content, total acidity, pH, and tasting temperature of the samples used in the study.
Wine TypeCodeGrape VarietyAlcohol
(% vol.)		Total Acidity
(g/L Tartaric Acid)		pHTasting Temperature
WhiteWW1Malvasia Fina, Rabigato, Fernão Pires12.75.603.409 °C
WhiteWW2Gouveio, Rabigato, Viosinho13.06.003.209 °C
RedRW1Touriga Nacional, Touriga Franca, Tinta Roriz, Tinto Cão14.05.303.7017 °C
RedRW2Tinta Roriz, Tinta Barroca, Touriga Franca, Touriga Nacional13.55.113.9617 °C
Port *PW1Touriga Nacional, Touriga Franca, Tinta Roriz, Tinta Francisca, Tinta Barroca, Souzão, Tinto Cão, Tinta Amarela (old vines)19.73.503.9016 °C
Port *PW2Touriga Nacional, Touriga Franca, Tinta Roriz, Tinto Cão, Tinta Barroca, Tinta Amarela19.44.003.6016 °C
* Sweet Port wine with 90–130 g/L sugar.
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Marques, C.; Vilela, A. FaceReader Insights into the Emotional Response of Douro Wines. Appl. Sci. 2024, 14, 10053. https://doi.org/10.3390/app142110053

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Marques C, Vilela A. FaceReader Insights into the Emotional Response of Douro Wines. Applied Sciences. 2024; 14(21):10053. https://doi.org/10.3390/app142110053

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Marques, Catarina, and Alice Vilela. 2024. "FaceReader Insights into the Emotional Response of Douro Wines" Applied Sciences 14, no. 21: 10053. https://doi.org/10.3390/app142110053

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Marques, C., & Vilela, A. (2024). FaceReader Insights into the Emotional Response of Douro Wines. Applied Sciences, 14(21), 10053. https://doi.org/10.3390/app142110053

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