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Abstract

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.

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... Many other studies coupled these analyses of quality traits with sensory trials when analyzing processes to change beer characteristics, such as ultrasound-assisted thermal processing [4], antioxidant activity through natural additives [5][6][7], for sweet potato beers [8] and commercial beers [9]. In the case of traditional consumer sensory tests, they tend to be subjective, and require a laboratory with individual booths that meet specific requirements; it also involves the recruitment of a large number of participants, which often requires an incentive to participate, which leads to higher costs and time for conducting the sessions and analyzing the data [10][11][12]. Therefore, all these studies' translational results are not easy to implement in the broad brewing industry to be used in every single batch since they require laboratory instrumentation, specialized personnel, and skills for operation, data acquisition, and analysis and may be time-consuming and costly. ...
... The beer samples were analyzed using the robotic pourer RoboBEER (The University of Melbourne, Parkville, Vic, Australia), as Gonzalez Viejo et al. [10] described. Each beer bottle was poured using the pourer to record 5-min videos and further analyzed using computer vision algorithms in Matlab ® R2020b (Mathworks, Inc., Natick, MA., USA) to obtain the physical parameters related to foam and color (Table 3). ...
... Most of the bottom fermentation beers were clustered close to vectors related to foam drainage (FDrain) of beers and color red, green and blue (RGB) and L; the latter may be explained due to the lighter color that these beers tend to have [49] compared to those form top and spontaneous fermentation. Furthermore, the spontaneous beers were grouped mostly close to bubble formation with the distribution of bubbles between small, medium, and large size (SmBubb, MedBubb, and LgBubb) contributing to a higher lifetime of foam (LTF), similar results have been previously reported for both physicochemical and sensory data [10,13,50,51]. On the other hand, top fermentation beers clustered mostly closer to all the gas sensors' sensitivity with a variation of foamability and bitterness which mainly influenced overall aroma, and flavor liking along with higher beer carbonation mouthfeel (Mcarb) (Figure 3a). ...
Article
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Beer quality is a difficult concept to describe and assess by physicochemical and sensory analysis due to the complexity of beer appreciation and acceptability by consumers, which can be dynamic and related to changes in climate affecting raw materials, consumer preference, and rising quality requirements. Artificial intelligence (AI) may offer unique capabilities based on the integration of sensor technology, robotics, and data analysis using machine learning (ML) to identify specific quality traits and process modifications to produce quality beers. This research presented the integration and implementation of AI technology based on low-cost sensor networks in the form of an electronic nose (e-nose), robotics, and ML. Results of ML showed high accuracy (97%) in the identification of fermentation type (Model 1) based on e-nose data; prediction of consumer acceptability from near-infrared (Model 2; R=0.90) and e-nose data (Model 3; R=0.95), and physicochemical and colorimetry of beers from e-nose data. The use of the RoboBEER coupled with the e-nose and AI could be used by brewers to assess the fermentation process, quality of beers, detection of faults, traceability, and authentication purposes in an affordable, user-friendly, and accurate manner.
... This has been done by implementing algorithms that can analyze imagery (visible and thermal) and identify animal behavior, posture and corporal condition, among others [33][34][35]. Furthermore, infrared thermal imagery (IRTI) and visible or red, blue and green (RGB) images have been used to assist the assessment of emotion changes in humans [36][37][38][39] and animals with great success, as presented in this study. In cattle, the measurement of eye temperature from IRTIs has been used to relate to stress levels in several situations [40]. ...
... Other algorithms with increased accuracy have also been developed and used to detect changes in physiological parameters and emotions in human consumers when exposed to several products [38]. The implementation of these techniques have been published by Torrico, et al. [42] and Gonzalez Viejo et al. [36,37], using customized algorithms to measure skin temperature and HR as responses to several food stimuli in human consumers through non-invasive video analysis and IRTI. ...
... The deployment was conducted by evaluating the model with data from the remaining 132 cow samples and conducting linear regression to assess the accuracy of the model's outputs with respect to the observed data. Figure 5a shows that cows within 6 and 8 years old had slightly higher HR (83-85 BPM) than younger or older cows (73-82 BPM), a similar trend was shown for RR of cows 7 years old, which had the highest mean value (40 BrPM) compared to other cows (29)(30)(31)(32)(33)(34)(35)(36)(37). The abrupt movements in this figure were reported as a variance of x and y axes; therefore, this figure shows that the 5-and 6-year-old cows had fewer movements in both directions (x and y axes) compared to younger and older cows. ...
Article
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New and emerging technologies, especially those based on non-invasive video and thermal infrared cameras, can be readily tested on robotic milking facilities. In this research, implemented non-invasive computer vision methods to estimate cow’s heart rate, respiration rate and abrupt movements captured using RGB cameras and machine learning modelling to predict eye temperature, milk production and quality are presented. RGB and infrared thermal videos (IRTV) were acquired from cows using a robotic milking facility. Results from 102 different cows with replicates (N=150) showed that an artificial neural network (ANN) model using only inputs from RGB cameras presented high accuracy (R=0.96) in predicting eye temperature (oC), using IRTV as ground truth, daily milk productivity (kg-milk-day-1), cow milk productivity (kg-milk-cow-1), milk fat (%) and milk protein (%) with no signs of overfitting. The ANN model developed was deployed using an independent 132 cow samples obtained on different days, which also rendered high accuracy and was similar to the model development (R=0.93). This model can be easily applied using affordable RGB camera systems to obtain all the proposed targets, including eye temperature, which can also be used to model animal welfare and biotic/abiotic stress. Furthermore, these models can be readily deployed in conventional dairy farms.
... This, along with the extended questionnaires typically presented to participants, make the techniques time-consuming and subjective, as they rely on the consumers responses that may be biased, due to many factors such as cultural effects; for example, Asians have shown a trend to be polite and avoid using the extremes of the scales [15,16]. Therefore, the use of biometrics to assess consumers' subconscious responses by tapping into the autonomic nervous system has been implemented, obtaining interesting results, which will also be reviewed in this paper [17,18]. ...
... Methods involving computer vision have been developed to be used in different industries such as medical, marketing, psychology, agriculture, food, and beverages, among others. This technology has been used for different applications, which include an assessment of hand hygiene [42], face recognition and tracking [18], object [50] and text recognition [51], and color analysis [7,43,50], among others. The food industry is among the top industries with the fastest growth in the automation of quality assessment using machine or CV [52]. ...
... However, this paper will only focus on the supervised group, as it the type that has been mostly applied to food and beverages; it may be divided into (i) classification or pattern recognition and (ii) regression algorithms [30,88]. The classification learners are used to categorize samples into different groups and have been applied for different purposes in distinct fields such as agriculture [50], medical diagnosis, food and beverages [17,18]. Some of the main classifier types consist of (i) decision trees, (ii) discriminant analysis, (iii) logistic regression, (iv) naïve Bayes, (v) support vector machines, (vi) nearest neighbor, (vii) ensemble and (viii) artificial neural networks (ANN) [89]. ...
Article
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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 to get results in real-time. Therefore, there is a need to test and implement emerging technologies 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 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 researchers about the concepts of artificial intelligence and machine learning as well as the correct design and interpretation of modeling related to the lack of inclusion of relevant data, additional to presenting over- or under-fitted models.
... In carbonated beverages, visual attributes linked to bubbles are directly related to their quality traits. This is due to the relationship between bubbles, and other sensory characteristics of the products, such as mouthfeel, release of aromas, and changes in tastes and flavors [2,[11][12][13][14][15]. The main components in carbonated beverages that determine bubble characteristics, foam formation, and stability are the CO 2 content and its source, as well as some tensioactive or surfactant substances such as proteins and sugars. ...
... The foam formation in the glass also depends on the pouring method, temperature of the liquid, and CO 2 concentration in the beverage [51,52]. Lower temperatures are preferred, especially for beer and sparkling wine, because, as previously mentioned, CO 2 solubility increases [2] and, therefore, avoids an excessive foam formation, which is often desired by consumers [13]. The role of proteins in foam stability is due to their structure, which presents molecules of both hydrophilic and hydrophobic properties. ...
... This method consists of an automated integrated camera system, which includes an infrared thermal FLIR AX8™ camera and Android ® tablet coupled with a Bio-Sensory application to display the sensory questionnaire; the method also involves the analysis of videos from participants using computer vision to assess eight emotions, two dimensions (valence and arousal), heart rate and body temperature [111]. Gonzalez Viejo et al. [13] used this method along with an electroencephalogram (EEG) device to assess brain wave responses to assess beers from different types of fermentation and foamability and used ANN to develop three models to classify beer samples into low and high levels of liking of (i) flavor, (ii) carbonation mouthfeel and (iii) overall liking using only the subconscious responses as inputs; all models presented high accuracy >80% (Figure 1a). Furthermore, Gonzalez Viejo et al. [112] evaluated the perceived quality and liking of foam-related parameters from visual assessment using videos from the pouring of beer samples from the RoboBEER to uniform the pouring and using the integrated camera system and Bio-Sensory App along with eye-tracking; the authors were able to develop an ANN model to classify beers into low and high level of liking of foamability with 82% accuracy using only the biometric responses from consumers as inputs (Figure 1b). ...
Article
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Quality control, mainly focused on the assessment of bubble and foam-related parameters, is critical in carbonated beverages due to their relationship with the chemical components as well as their influence on sensory characteristics such as aroma release, mouthfeel, and perception of tastes and aromas. Consumer assessment and acceptability of carbonated beverages are mainly based on carbonation, foam, and bubbles, as a flat carbonated beverage is usually perceived as low quality. This review focuses on three beverages; beer, sparkling water, and sparkling wine. It explains the characteristics of foam and bubble formation, and the traditional methods as well as emerging technologies based on robotics and computer vision to assess bubble and foam-related parameters. Furthermore, it explores the most common methods and the use of advanced techniques using an artificial intelligence approach to assess sensory descriptors both for descriptive analysis and consumers acceptability. Emerging technologies based on the combination of robotics, computer vision, and machine learning as an approach to artificial intelligence have been developed and applied for the assessment of beer and, to a less extent, sparkling wine. This, with the objective of assessing the final products quality using more reliable, accurate, affordable and less time-consuming methods. However, despite carbonated water being an important product due to its increasing consumption, more research needs to focus on exploring more efficient, repeatable, and accurate methods to assess carbonation, and bubble size, distribution and dynamics.
... Therefore, the initial assessment of the visual attributes of dishes of insect-based foods is very important, as they create the first impression for consumers and determine their eagerness to taste the product or not. Furthermore, the presentation of food, beverages, and even the packaging of food products using imagery presented on digital screens renders statistically similar information when presenting the same product for taste or handling, as it creates the first impression for consumers when judging a product [15][16][17]. Hence, the visual renderings of insect-based food may help to break negative emotions related to first impressions. ...
... Due to the influence of emotions in decision-making, especially for new food products and even food packaging for consumer acceptability [15,16,[18][19][20][21], it is important to assess how insect-based food products make consumers feel, besides studying only their acceptability. Therefore, some studies have been conducted to evaluate emotional responses towards foods with insects [22], specifically focused on disgust [23,24]. ...
... More recently, the use of emojis has been implemented for this purpose, as consumers tend to identify emotions using proxy images related to expressed emotions as non-verbal cues, as these are more closely associated with their feelings [25][26][27]. Subconscious responses from consumers have also been evaluated for chocolate and beer using computer vision techniques and machine learning to assess their facial expressions [15,16,20,28,29]. These studies may produce more relevant information that may be missed using conventional self-reported sensory analysis, especially for insect-based food products. ...
Article
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Insect-based food products offer a more sustainable and environmentally friendly source of protein compared to vegetable and animal proteins. Entomophagy is less familiar for Non-Asian cultural backgrounds and is associated to emotions such as disgust and anger, which is the basis of neophobia towards these products. Common sensory analysis may offer some insights about liking, visual, aroma and tasting appreciation and purchase intention of insect-based food products. However, more robust methods are required to assess these complex interactions with the emotional and subconscious responses related also to cultural background. This study focused on the sensory and biometric responses of consumers towards insect-based food snacks and machine learning modeling. Results showed higher liking and emotional responses for those samples containing insects as ingredients (not visible) and with no insects. Lower liking and negative emotional responses were related to those samples showing the insects. Artificial Neural Network models to assess liking based on biometric responses showed high accuracy for different cultures (> 92%). A general model for all cultures with 89% accuracy was also achieved.
... 13,14 Emotional response measurements may also provide more information on product liking during sensory evaluation. 15 A combination of self-reported, intrinsic facial expression recognition for emotional assessment and heart rate variability has been successfully implemented for the understanding of consumer acceptability of beer, 16 chocolate, 17,18 insect-based foods 19 and coffee labels. 20 Also, in yoghurt, a combination of sensory methods, including self-reported and facial expression measurements, 21 have been effectively used to understand consumers acceptability. ...
... Similar results were observed in the present study, where Berry, the least-liked sample, decreased the heart rate of participants. However, in other studies, such as for chocolate 17 and beer 16 tasting, the heart rate did not indicate significant differences between the sample types tested. This shows that the physiological responses of consumers depend on the food stimuli to which they are exposed. ...
... This coincides with previous studies reporting that, with highly bitter food, both adults and babies tend to rotate the head to the left as a sign of rejection towards potentially toxic substances. 16,52,53 The emoji (smiley) did not appear to be an effective discriminator for yoghurt products because it correlated with negative rather than positive terms, suggesting misclassification. This shows that a combination of biometrics and self-reported liking can provide more extensive details about consumer acceptability towards yoghurts. ...
Article
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Background Sensory biometrics provide advantages for consumer tasting by quantifying physiological changes and emotional response from participants, removing variability associated with self-reported responses. This study aimed to assess measure the physicochemical composition and consumers' emotional and physiological responses towards different commercial yoghurts, including dairy and plant-based yoghurts. The physiochemical composition of these products was also measured and linked with consumer responses. Results Six samples (Control, Coconut, Soy, Berry, Cookies and Drinkable) were evaluated for overall liking by N = 62 consumers using a 9-point hedonic scale. Videos from participants were recorded using the Bio-Sensory application during tasting to assess emotions and heart rate. Physicochemical parameters Brix, pH, density, color (L, a and b), firmness, and near-infrared (NIR) spectroscopy were also measured. Principal component analysis (PCA) and a correlation matrix were used to assess relationships between the measured parameters. Heart rate was related positively related to firmness, yaw head movement and overall liking, which were further associated to with the Cookies samples associated with them. Two machine learning (ML) regression models were developed using (i) NIR absorbance values as inputs to predict the physicochemical parameters (Model 1) and (ii) the outputs from Model 1 as inputs to predict consumers overall liking (Model 2). Both models presented very high accuracy (Model 1: R=0.98; Model 2: R=0.99). Conclusion The presented methods showed to be highly accurate and reliable to be potentially used by the industry to assess yoghurts quality traits and acceptability.
... [9][10][11] From previous studies, differences between cultures in the use of hedonic scales have been found, with Asians being more polite and avoiding extremes even though they liked or disliked the product extremely. [12][13][14][15][16] To avoid this situation, biometric responses of the panellists can be measured and used to obtain accurate food sensory perception data. Biometric data are more reliable and accurate than intensity and hedonic-scale tests for sensory analysis as they capture responses from the autonomic nervous system (ANS), such as emotions, facial features, and the physiological changes in the body while tasting a product, that may not be altered or controlled by consumers (unconscious response). ...
... The integration of these techniques would be an efficient strategy to gather more information from consumers to correlate with sensory characteristics while tasting a product. Recently, this method was validated 24 and used successfully as a reliable tool to assess the acceptability of beer, 15,16,25 insect-based snacks, 18 and chocolates by consumers. 17,26 Compared with previous studies, [15][16][17][18][24][25][26][27] the current study assessed whether the supplementation of Si would affect the sensory attributes of the seeds obtained from drought-stressed lentil plants, taking into consideration the nutritional aspects of seeds as well. ...
... Recently, this method was validated 24 and used successfully as a reliable tool to assess the acceptability of beer, 15,16,25 insect-based snacks, 18 and chocolates by consumers. 17,26 Compared with previous studies, [15][16][17][18][24][25][26][27] the current study assessed whether the supplementation of Si would affect the sensory attributes of the seeds obtained from drought-stressed lentil plants, taking into consideration the nutritional aspects of seeds as well. No previous research has investigated the emotional or the physiological responses of consumers associated with sensory evaluation of drought-stressed seeds. ...
Article
BACKGROUND Lentil is an important nutritionally rich pulse crop in the world. Despite having a prominent role in human health and nutrition, it is very unfortunate that global lentil production is adversely limited by drought stress, causing a huge decline in yield and productivity. Drought stress can also affect the nutritional profile of seeds. Silicon (Si) is an essential element for plants and a general component of the human diet found mainly in plant-based foods. This study investigated the effects of Si on nutritional and sensory properties of seeds obtained from lentil plants grown in Si-supplied drought-stressed environment. RESULTS Significant enhancements in the concentration of nutrients (protein, carbohydrate, dietary fibre, Si) and antioxidants (ascorbate, phenol, flavonoids, total antioxidants) were found in seeds. Significant reductions in antinutrients (trypsin inhibitor, phytic acid, tannin) were also recorded. A novel sensory analysis was implemented in this study to evaluate the unconscious and conscious responses of consumers. Biometrics were integrated with a traditional sensory questionnaire to gather consumers responses. Significant positive correlations (R=0.6-1) were observed between sensory responses and nutritional properties of seeds. Seeds from Si-treated drought-stressed plants showed higher acceptability scores among consumers. CONCLUSION The results demonstrated that Si supplementation can improve the nutritional and sensory properties of seeds. This study offers an innovative approach in sensory analysis coupled with biometrics to accurately assess consumer’s preference towards tested samples. In the future, the results of the current study would help in making a predictive model for sensory traits and nutritional components in seeds using machine learning modelling techniques.
... Nevertheless, the effect of the product-associated parameters can be measured as the result of consumer subconscious responses [11]. Hence, biometric techniques can collect more details such as brainwave signals, facial expression, and heart rate from consumers (untrained panels) [12]. Therefore, biometric methods can be used as a tool to understand consumer acceptance of the taste of new food products [13]. ...
... Rights reserved. Spontaneously fermented bear achieved better sensory response in both PCA (biometric and sensory description) and EEG study [12] Content courtesy of Springer Nature, terms of use apply. Rights reserved. ...
... However, sensory evaluation showed better overall impression for Château Topoľčianky wine brand than the other sample. Thus, it was clear that inducing happiness alone will not increase the sensory satisfaction of [12]. These are the studies that experimented with EEG for selecting the preferable samples in a given lot. ...
Article
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Sensory satisfaction is the key to consumer acceptance which also decides the success of any food products in the market. Though different sensory parameters like appearance, odor, and texture are considered for deciding the overall acceptability of food, taste plays a major role. As sensory panels cannot be a true representation of consumer’s taste perception, industries focus on market surveys. In reality, consumers taste perception varies according to the product cost, brand, and their age and health condition. The process of food tasting starts from tongue, where different taste receptors respond to various taste stimuli and pass the signals to the cortex of the brain region. These signals cause the electric current to flow through the brain neural networks and increase oxygen-containing blood utilization in specific brain areas. Using non-invasive gadgets such as electroencephalography, magnetoencephalography, functional MRI, and brain computer interface (BCI) technique, these signals can be sensed and decoded into useful sensory data. This review explains the taste recognition pathways of different taste stimuli and the basic steps involved in BCI techniques for detecting and discriminating them. In addition, it also explores the BCI-related taste-driven sensory studies and the limiting factors associated with them to emerge as a future sensory method. Graphical abstract
... By contrast, physiological responses analysis would instead be more appropriate to evaluate the sensational aspects due to taste perception, and it turns out to be a faster unconscious rating method [3]. Recently, heart rate has been used as one of the most common parameters to assess physiological responses associated with consumption and visual assessment of food and beverages [6][7][8]. Immediate and automatic responses with none or very little time for thinking is a better decision-making process for selecting the most appropriate item. ...
... However, these methods are intrusive, discomfort and challenging to apply since they required electrodes to be attached to the body, such as on the hands, chest, and scalp [9][10][11]. These might not present accurate results, due to the possibility of affecting sensory, behavior and physiological responses such as increasing of arousal caused by the stress/anxiety of having sensors attached to the bodies and being monitored [7][8]. Therefore, the use of contact method is less appropriate because it could produce bias results in sensory analysis of food and beverages since the subjects will be aware of the measurements that may influence their heart rate and other physiological responses [12]. ...
Article
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Pleasant or unpleasant feeling stimulated by taste usually expressed to describe the acceptance or rejection of food and beverages intake by a human. Since it stimulates the emotional reactions, therefore it also induces other response such as heart rate variations. Traditional sensory tools used only subjective measurement such as self-report, to estimate the feeling of tasting. This method sometimes failed to show some differences between the pleasant and unpleasant type of feelings unconsciously. Previous unconscious measurement methods used the intrusive technique by placing some sensors in contact with the body, which may affect the results of sensory analysis. This study was conducted to avoid the effects of using contact sensors and validate the contact-less method of monitoring heart rate due to emotional changes by extracting plethysmographic signal from the green component of the video images. The videos were recorded while the subject responded to pleasant, unpleasant and neutral stimuli. The findings indicated that the heart rate was significantly related to taste stimuli that also reflected the subjective feelings. The unpleasant-taste influenced heart rate to increase more compared to pleasant-taste and neutral-taste. This proposed approach can be used to remotely detect the feeling/emotion that not overtly express through facial expression, speech or gestures.
... Within this context, Viejo et al. evaluated in [7] the EEG, heart rate, temperature, and facial expressions of beer consumers. In [8], He et al. recorded facial expressions of participants exposed to orange and fish odors. ...
... Moreover, changes in facial expression are harder to determine when tasting food products, compared to smelling a perfume or watching a video [25], since the jaw movement produced by chewing and the occasional facial occlusion (because of the hand that takes the sample to the mouth) are often the cause of misreading in FER algorithms. These might be some of the reasons why similar studies seem inconclusive [7,31,32]. ...
Article
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Sensory experiences play an important role in consumer response, purchase decision, and fidelity towards food products. Consumer studies when launching new food products must incorporate physiological response assessment to be more precise and, thus, increase their chances of success in the market. This paper introduces a novel sensory analysis system that incorporates facial emotion recognition (FER), galvanic skin response (GSR), and cardiac pulse to determine consumer acceptance of food samples. Taste and smell experiments were conducted with 120 participants recording facial images, biometric signals, and reported liking when trying a set of pleasant and unpleasant flavors and odors. Data fusion and analysis by machine learning models allow predicting the acceptance elicited by the samples. Results confirm that FER alone is not sufficient to determine consumers’ acceptance. However, when combined with GSR and, to a lesser extent, with pulse signals, acceptance prediction can be improved. This research targets predicting consumer’s acceptance without the continuous use of liking scores. In addition, the findings of this work may be used to explore the relationships between facial expressions and physiological reactions for non-rational decision-making when interacting with new food products.
... Consumers assess several characteristics of beer to determine the overall quality [7] including sensory or taste [8,9]. Codină et al. [9] conducted a sensory analysis on Romanian beers and found a significant correlation between the sensory attributes and the chemical parameters through physicochemical parameters that are responsible for the sensorial attributes of beer. ...
... Taste and aroma may be perceived separately, but they are often integrated to produce a total flavor impression [10]. Aroma is an important factor that the consumer perceives for assessing the quality of beer [7]. Beer aroma mostly consists of higher alcohols and esters [11]. ...
Article
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Beer flavor and sensory quality are affected by storage time and temperature due to chemical breakdown and aging. This study aimed to investigate the organoleptic properties of temperature-abused, unpasteurized craft beer and analyze the chemical breakdown associated with the process. Sensory tests were performed using a triangle test to determine consumer identification of temperature-abused beer. The chemical tests were conducted to determine the chemical breakdown of the two beer groups: control beer (COB) and temperature-abused beer (TAB). The chemical analysis of the two beer groups showed significant changes in multiple chemical compounds such as ethyl esters, linear aldehydes, and sulphur-compounds; however, the sensory analysis results were not significant even though 39% of participants were able to detect differences. in this study, two factors identified that caused chemical reactions in the TABs were oxidation and live yeast cells. In conclusion, these results can be used by beer producers to ensure a quality product throughout the distribution chain by controlling time and temperature.
... This makes it significantly important to understand the role or acceptance of the yogurts by different cultures using a cross-cultural sensory experiment [19]. There have been several studies involving various products, such as coffee labels [20], chocolate [21], beer [22] and artificial sweeteners [23], where a combination of implicit and explicit sensory methods have been successfully integrated together to assess the consumer acceptability. In the case of the study with coffee labels, the biometrics technology of facial expression recognition was also shown to be a reliable method for sensory evaluation [20]. ...
... The plot shows an association of positive terms with the overall liking scores and shows negative terms to be opposite to liking. In a beer tasting study [22], unconscious biometric responses were successfully integrated with conscious sensory responses for predicting consumer liking of beers. However, in another study by van Bommel et al. [38] where implicit facial expressions were compared with the explicit self-reported emotions, little overlap was found between methods, which were not directly comparable. ...
Article
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Hedonic scale testing is a well-accepted methodology for assessing consumer perceptions but is compromised by variation in voluntary responses between cultures. Check-all-that-apply (CATA) methods using emotion terms or emojis and facial expression recognition (FER) are emerging as more powerful tools for consumer sensory testing as they may offer improved assessment of voluntary and involuntary responses, respectively. Therefore, this experiment compared traditional hedonic scale responses for overall liking to (1) CATA emotions, (2) CATA emojis and (3) FER. The experiment measured voluntary and involuntary responses from 62 participants of Asian (53%) versus Western (47%) origin, who consumed six divergent yogurt formulations (Greek, drinkable, soy, coconut, berry, cookies). The hedonic scales could discriminate between yogurt formulations but could not distinguish between responses across the cultural groups. Aversive responses to formulations were the easiest to characterize for all methods; the hedonic scale was the only method that could not characterize differences in cultural preferences, with CATA emojis displaying the highest level of discrimination. In conclusion, CATA methods, particularly the use of emojis, showed improved characterization of cross-cultural preferences of yogurt formulations compared to hedonic scales and FER.
... The HR responses are a combination of parasympathetic (safe conditions) and sympathetic (stress conditions) activities [57]. Gonzalez Viejo et al. [13] found a decrease in HR when consumers tasted beer samples with higher bitterness, while Rousmans et al. [32] reported that ST was higher when tasting citric acid. In the present study, a similar trend was found, as consumer HR was lower with bitter taste chocolates and ST increased when tasting the sour chocolate ( Figure 1A). ...
... Some studies have reported that there are weak ANS responses to sweet taste compared to the other basic tastes, mainly due to the habituation of the organism to sweetness [32,58]. However, other studies have shown that the assessment of both ANS responses and FE are able to provide more information about the unconscious responses from consumers when tasting different food products [7,9,13,27]. Moreover, it is shown that conscious responses alone may be insufficient to identify the aspects that affect liking, hence a combination of implicit and explicit measures, as conducted in this study, may contribute to a better understanding of liking and complete eating behavior [59]. ...
Article
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Facial expressions are in reaction to basic tastes by the response to receptor stimulation. The objective of this study was to assess the autonomic nervous system responses to basic tastes in chocolates and to identify relationships between conscious and unconscious responses from participants. Panelists (n = 45) tasted five chocolates with either salt, citric acid, sugar, or monosodium glutamate, which generated four distinctive basic tastes plus bitter, using dark chocolate. An integrated camera system, coupled with the Bio-Sensory application, was used to capture infrared thermal images, videos, and sensory responses. Outputs were used to assess skin temperature (ST), facial expressions, and heart rate (HR) as physiological responses. Sensory responses and emotions elicited during the chocolate tasting were evaluated using the application. Results showed that the most liked was sweet chocolate (9.01), while the least liked was salty chocolate (3.61). There were significant differences for overall liking (p < 0.05) but none for HR (p = 0.75) and ST (p = 0.27). Sweet chocolate was inversely associated with angry, and salty chocolate positively associated with sad. Positive emotion-terms were associated with sweet samples and liking in self-reported responses. Findings of this study may be used to assess novel tastes of chocolate in the industry based on conscious and emotional responses more objectively.
... Findings were consistent with those from similar studies from previous publications conducted in a sensory laboratory [4][5][6][7][8][9], which confirms the reliability of the proposed virtual method. Further developments involve the assessment of multiple participants to record their biometrics simultaneously and optimize the sensory session time. ...
... On the other hand, in Study 4, the trained panel was able to accurately assess the intensity of aromas in different wine samples (Shiraz and Chardonnay) [3]. Conclusions: Findings were consistent with those from similar studies from previous publications conducted in a sensory laboratory [4][5][6][7][8][9], which confirms the reliability of the proposed virtual method. Further developments involve the assessment of multiple participants to record their biometrics simultaneously and optimize the sensory session time. ...
Article
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The recent pandemic due to the appearance of COVID-19 in 2020 has led to lockdowns worldwide, which have affected companies and universities conducting sensory evaluations. Therefore, a novel method to conduct sensory sessions for descriptive and consumer tests using biometrics in isolation has been developed.
... There were non-significant differences (p > 0.05) between AOIs for different emotions. However, the variability in standard error (SE) shows some trends that can be used to predict liking among other parameters using machine learning modelling [6,33,34]. Figure 4 shows significant differences (p < 0.05) between samples for both the time to first view and time viewed. The AOI manufacturer was the one that took longer for participants to first view (4.53 s), which means it was the last AOI they see when evaluating the labels. ...
... The proposed system allows further analysis and the development of prediction models using machine learning techniques based on biometrics. The latter approach has been used in the case of consumer acceptability based on visual evaluation of beer pouring videos using eye-tracking, emotional and physiological responses [34] and for consumers acceptability towards beer tasting using biometrics such as emotions, heart rate, and body temperature [33]. Other authors have used machine learning modelling to predict food choice using eye-tracking gaze data when evaluating food images [38] and to predict participants age from their gaze patterns [39]. ...
Article
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New and emerging non-invasive digital tools, such as eye-tracking, facial expression and physio-logical 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 applica-tion 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 la-bels: 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.
... Also, considering the growing population with gluten-relateddiseases, breweries are seeking alternatives for offering high-quality gluten-free beers, as using gluten-free cereals or enzymes treatments (Hager et al., 2014;Rubio-Flores and Serna-Saldivar, 2016). These alternatives can interfere in quality parameters appreciated by consumers, as beer foam (Hughes and Baxter, 2001;Viejo et al., 2019;Deotale et al., 2020). ...
... Such data suggest a high content of iso-alpha acids produced during the brewing process, which contribute to the bitterness associated with beer (Oladokun et al. 2016). In addition, compounds derived from hops that contribute to the bitterness of beer showed antimicrobial activity which enhanced the shelf life of the beer and affect their aroma (Dresel et al. 2016); however, although the shelf life of the beer could be longer, the drinker preferred beer products with a low level of bitterness (Viejo et al. 2019), which could contribute to reduce the consumption of beer. It has been reported that the alcohol content may vary and it can be mostly attributed to the production method and the raw materials used. ...
Article
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Beer is one of the oldest alcoholic beverages; however, it may be contaminated by aflatoxin B1 (AFB1), a highly toxic mycotoxin. The content of AFB1 in 384 samples from 16 trademarks of clear lager beers, produced by two Mexican breweries, was evaluated and physicochemical parameters were determined. Samples were collected (4 canned or bottled beers per trademark per month) from July to December 2014 in Sonora, Northwest Mexico. Overall, results showed that all beers contained AFB1 in a range of 0.203–0.2408 μg/L. pH values, total acidity and alcohol level were significantly different (p < 0.05) between breweries; while colour and bitterness units showed no statistical differences. The Pearson correlation test revealed a significant positive (p < 0.05) relationship between the alcohol content and AFB1 levels. Although Maximum Residue Levels (MRL) have not been established for AFB1 in beer, there is a limit for processed cereals; accordingly, none of the samples exceeded the MRL of AFB1 set by Mexican standard (20 μg/L) or by the European Community (2 μg/L) for cereals and cereal-based products. However, the estimated exposure for heavy and very heavy beer consumer (0.0011, and 0.0013 μg kg/bodyweight/day, respectively) suggested that they may be exposed to significant levels of this toxin. Nonetheless, further research is still needed to determine if heavy beer consumption (> 1 L per day) represents a serious health hazard for consumers.
... IR thermography instruments are usually applied to assist medical diagnosis in the medical science field. For example, to record temperature distribution and variation around eyeball [7], to estimate consumer's reaction through facial temperature data [8], to testify influence to the human body from battery heating [9], or to monitor temperature variation of the human forehead whilst we enter meditation [10]. In addition, research of human face detection is also a hot spot during recent years. ...
Article
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So far, there have been many researches subject to technical requirements due to image registration. Using image registration can lower deviation from sequential images and make it possible to analyze the information variation of particular area subsequently. This study provides a procedure creating fixed image based on the data of facial IR thermography, where its methods include the visual saliency map by detected image, as well as cluster algorithm. Comparison is also made here to solve the merits and demerits by affine parameter to reach the optimum measure amongst Genetic Algorithm, Particle Swarm Optimization and Simulated Annealing Algorithm. Where there are two control parameters concerning this experiment: one is the calculation of time confined each alignment, while the other one is to use parallel computing toolbox or not. The optimum method will be chosen by the values of objective function based on the control parameters. Afterward, the optimal internal parameter is to be verified through Taguchi experiment and validity of this procedure in this study will be built in accordance with the parameter result as above. Therefore, difference of images before and after alignment can be validated by overlapping the images before and after alignment as well as the image quality measurements, where its results reveal that alignment procedure of IR thermography in this study is actually capable of performing human face alignment precisely, and subsequently do help data statistics and analysis concerning temperature area interdependence.
... However, it is known that such signal posses DNA traits [34] capable of providing unique person identification. EEG signal is now widely available and easy to use because of the portability and non-invasiveness of the newly developed EEG recording devices [37]. ...
Conference Paper
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The brain activity observed on multiple EEG electrodes is influenced by volume conductance and functional connectivity of a person performing a task. When the task is a biometric test, EEG signals represent the unique 'brain print' which is genetically defined by the functional connectivity that is represented by interactions between the electrodes, whilst the conductance component causes trivial correlations in EEG signals. Orthogonalisation using autoregressive modelling minimises the conductance component, and the connectivity features can be then extracted from the residuals. However, the results cannot be reliable for high-dimensional EEG data recorded via a multi-electrode system. The proposed method shows that the dimensionality can be significantly reduced if baselines that are required for estimating the residuals can be modelled by using EEG electrodes that make important contribution to the functional connectivity. The results show that the required models can be learnt by Machine Learning techniques which are capable of providing the maximal performance in the case of multidimensional EEG data. The study which has been conducted on a EEG benchmark including 109 participants shows a significant improvement of the identification accuracy.
... Contrary to other references that state that consumers approach when they are exposed to positive foods and avoid (retract) with negative foods [31], in this study it was found that consumers approach when they dislike the sample and retract when they like it. This trend was also found in a study assessing beer consumer acceptability using biometrics [32] and may be due to consumers feeling more comfortable and relaxed when they like a product, which makes them sit back [33]. Contrary to other studies that found that smaller bubbles are preferred [10,11,23], in this study, small bubbles elicited disgusted unconscious emotion (DisgustedFE), while medium bubbles were related with HappyFE and valence. ...
Article
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Bubbles are important for carbonated beverage quality since smaller bubbles contribute to higher acceptability. Therefore, the effects and acceptability of the application of audible sound in carbonated water were studied using three brands and applying five frequencies for one minute each in ascending order. Six samples, two from each brand, were used for treatments: (i) control and (ii) sonication. Physicochemical measurements consisted of total dissolved solids (TDS), electric conductivity (EC), pH, bubble size, and bubble size distribution. A sensory session (N = 30) was conducted using the Bio-Sensory application to assess acceptability and emotions using self-reported and biometric responses. Statistical analysis included: ANOVA (α = 0.05) and principal component analysis (PCA) for quantitative data and Cochran Q test with pairwise comparisons (p < 0.05) for self-reported emotion responses. Results showed that the sonication effect for the sample with higher TDS, EC, and pH (SPS) reduced bubble size by 46%, while in those with lowest TDS, EC, and pH (IceS) caused an increase of 158% compared to the control. For samples with intermediate values (NuS), there were non-significant differences (p > 0.05) compared to the control. Acceptability was higher for samples with sonication for the three brands. Emotional self-reported responses were more positive for samples with sonication, showing significant differences (p < 0.05) for emotions such as “happy” and “pleased” during both sound and visual assessments. From PCA, a positive relationship between bubble size and liking of bubbles was found as well as for the number of medium bubbles and happy facial expression. The audible sound generated by ubiquitous sound systems may potentially be used by the industry, applying it to the bottled product to modify bubble size and improve quality and acceptability of carbonated beverages.
... The scale parameters for scores 1-9 are dislike extremely, dislike very much, dislike moderately, dislike slightly, neither like nor dislike, like slightly, like moderately, like very much, and like extremely (Torrico et al. 2018). This method has been employed for sensory evaluation of a wide array of food products including meat products, dairy products, beer, and fortified foods, among many others (Torrico et al. 2018;Esmerino et al. 2015;Viejo et al. 2019). Recently, in a study by Qasem et al. (2017), okra extract powder (0 to 8%) was used to replace wheat flour in preparing sponge cake formulations, wherein hedonic sensory evaluation was carried out to assess texture, flavor, and appearance. ...
Article
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Sensory evaluation plays a vital role in the assessment of acceptance of novel food products and preferences for different cuisines. This process provides significant and valuable information to the food processing industries and food scientists regarding the sensory quality of food products. Traditional techniques generally employed for the sensory evaluation assess only in a qualitative sense and cannot perform a precise quantitative assessment. However, recently, novel techniques such as fuzzy set theory have been effectively used in assessing the sensory characteristics of various traditional as well as novel food products developed through fortification and modified processing techniques. The aim of the fuzzy set theory is to treat ambiguous phenomena mathematically, express the degree of incomprehensibility in human thinking along with connecting it to a real number. Furthermore, fuzzy logic mimics human behavior for reasoning and decision making. In fuzzy modeling, linguistic entities such as “not satisfactory, fair, medium, good and excellent” are employed for describing the sensory attributes of food products (including color, aroma, taste, texture and mouthfeel) obtained through subjective evaluation, which are combined with the accurate and precise data attained through objective evaluation to draw conclusions regarding acceptance, rejection and ranking, along with strong and weak characteristics of the food under study. This analysis also assists in finding the preference of quality attributes and sets criteria for acceptance or rejection of the newly developed foods. This review provides an overview of the application of fuzzy concepts to the sensory evaluation of traditional and novel food products (often enriched with nutraceuticals) in the food industry, along with the corresponding advantages.
... It measures voltage fluctuations resulting from the ionic current in the synaptic area of the neurons within the brain by recording the responses of multiple electrodes or the new technology of passive or active biosensors connected to few electrodes (MindWave™ Mobile EEG headset, NeuroSky, Inc. San Jose, CA, USA), and contacts to the subject's scalp and forehead (Muñoz, López-Gallego, Arias-Salazar, & Serna-Rodríguez, 2019:;Viejo, Fuentes, Howell, Torrico, & Dunshea, 2019). EEG reflects the postsynaptic potentials of neurons, and these electrical changes are reflected in the EEG recorded at the scalp within milliseconds, making this methodology outstanding for tracking rapid shifts in brain functioning (Bell & Cuevas, 2012;Pizzagalli, 2007). ...
Article
Background A good understanding of the physiological and emotional response of consumers to food products is essential for success in food product design and food service. In the field of food research, many traditional sensory measurements, such as name, preference, acceptance, liking and hedonic valuation, have been used for the evaluation of consumer feelings and preferences. Scope and approach Recently, to enhance this understanding, the electrophysiological method electroencephalography (EEG) was applied to measure brain activity, which encompasses the dipole rotation from sensory perceptions to the emotional response of the consumer. Therefore, this review focuses on the principles and applications of EEG in food research. Key findings and conclusions The EEG technique measures the electric potential at the scalp during stimulation with pictures, sounds, odour, and tastes, turning the response into a signal that can be used to explain perceptive, attentive, or emotional processes. It has been suggest that EEG can be used to evaluate consumer preferences for food associated with hedonic valuation. EEG is a useful technique to support traditional sensory measurements, since it directly measures the implicit physiological and emotional responses of consumers. Moreover, it provides a deep understanding of the consumer's cortical processes related to food, which is useful to match consumer demand in food product development and service.
... Similarly, benzene is an aromatic compound developed in beer production and has been previously reported in GC-MS analysis [26]. The fluctuations in the different gases was expected due to the variability in the release of CO 2 molecules and other volatiles, which are dependent on the bubbles breakage rate as most aromas are contained within the bubbles [27,28]. ...
... Visual assessment is the most important in food and beverages as it is the first impression that consumers have when judging a product (Donadini, Fumi, & Newby-Clark, 2014;Fuentes, Wong, & Gonzalez Viejo, 2020). It has been shown, specifically for the case of beer, that the evaluation of videos of beers being poured based on descriptors, such as maximum volume of foam, foam stability, clarity, and perceived quality, had similar results in terms of perceived quality and overall liking to tasting sessions based on aromas, mouthfeel and tastes/flavors of the same beers (Gonzalez Viejo, Fuentes, Howell, Torrico, & Dunshea, 2019;Gonzalez Viejo, Fuentes, Howell, Torrico, & Dunshea, 2018). The latter effect can also be supported by research in beer and other sparkling beverages that have shown that the visual perception of foamability and subconscious perception of bubble stability, size and distribution are the most crucial quality traits that determine the likability and acceptability of consumers (Dale, West, Eade, Rito-Palomares, & Lyddiatt, 1999;Donadini, Fumi, & de Faveri, 2011;. ...
Article
The social isolation settings derived from the COVID-19 pandemic affected the standard sensory evaluation techniques used in the food and beverage industry. This situation forced companies and researchers to assess other options to continue conducting these tests in remote contactless locations. This study aimed to evaluate two sets of samples (i) six images from Geneva affective picture database (GAPED) and (ii) six videos of beer pouring using traditional self-reported sensory data and emotional response from consumers biometrics. Specifically, four research questions (RQ) arouse from this study: RQ1: are there significant differences between GAPED images and beers in unconscious and self-reported responses from consumers?, RQ2: are there any correlations between subconscious and self-reported responses from consumers when assessing beer?, RQ3: can consumers differentiate positive, neutral and negative images based on subconscious and self-reported responses?, RQ4: are there any relationships between subconscious and self-reported responses when assessing GAPED images and beers, and how are samples associated with variables? A total of 113 Mexican consumers participated in the virtual sensory session using an online videoconference software to record videos of participants during the session. Results showed there were significant differences (p<0.05) between samples, especially for self-reported responses (RQ1), and several correlations between variables, such as positive correlations between the perceived quality of beers and happy emoji (r=0.84), and negative correlation with confused emoji (r=-0.97; RQ2). Besides, using the proposed methods, consumers were able to correctly differentiate through elicited emotions the positive, neutral and negative GAPED images (RQ3). Regarding RQ4, several relationships were found between variables in both GAPED images and beers; however, it was found that different emotions were elicited depending of the stimuli used. The proposed method showed to be a reliable and practical option to conduct visual and potentially tasting sensory tests in isolation and recruit participants from different countries without travelling to collect their responses.
... Munoz, et al. [13] used single electrode wireless EEG to assess consumer behaviour exposed to different sales techniques. Similarly, Viejo, et al. [14] used EEG mobile headset to determine acceptability of beers based on subconscious responses. ...
Article
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Conventional measurements used to study consumer response to food products may be subject to cognitive bias, as measurement data was consumer's reported thoughts or through questionnaires. Therefore, for an unbiased approach electroencephalography (EEG), an electrophysiological method can provide implicit and extensive data. EEG uses electrical activity of brain to record and explain perceptive, attentive as well as emotional processes of consumer towards foods. The asymmetry of EEG signal between right and left hemispheres of anterior (frontal lobe) or posterior (parietal and occipital lobe) parts of brain can be used to determine acceptability of stimuli in a stimulated person. The accurate measurement through EEG enables marketers to compare consumer response to different marketing stimuli and impact moments associated with particular product or brand for better positioning of product in market.
... Human behavior tools assess different aspects of consumer cognitive processes and can be used to further understand consumer perception. They include eye tracking [199][200][201], electrophysical responses such as heart rate, blood pressure, and skin conductance, electrodermal activity or galvanic skin response, brain activity through electroencephalography [202][203][204][205], fMRI [206][207][208][209][210], and facial expression [211]. These methods measure emotional and physiological responses and provide additional information on how consumers perceive a product. ...
Article
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Tenderness, juiciness, and flavor have been associated with consumer acceptance of beef, lamb, and pork. Drivers of consumer liking are interrelated across these species, but there are differences in consumer preferences. Animal age, animal diet, and subsequent marbling impact consumer liking across species. For beef, consumer research prior to the 1990s showed that tenderness was the main driver of liking. Consumer tenderness and juiciness liking are highly correlated. More recent research has shown that as overall tenderness improved and tenderness variation decreased, flavor has become a more important driver of beef consumer liking. Flavor is affected by consumer preparation methods, familiarity with different flavor presentations, and animal production systems. Animal diet impacts consumer perception of beef tenderness and flavor, especially when comparing forage-fed versus grain-fed beef. Flavor preferences vary across countries more so than preferences for beef based on consumer tenderness preferences and are most likely influenced by the consumption of locally produced beef and the flavor-derived type of beef traditionally consumed. Drivers of pork consumer liking have been shown to be affected by pH, color, water holding capacity, animal diet, and the presence of boar taint compounds. While tenderness and juiciness continue to be drivers of consumer liking for pork, flavor, as impacted by animal diet and the presence of boar taint compounds, continues to be a driver for consumer liking. For lamb, the flavor, as affected by diet, and animal age continue to be the main drivers of consumer liking. Lamb consumers vary across countries based on the level of consumption and preferences for flavor based on cultural effects and production practices.
... According to the PCA, aromatic compounds such as esters (ethyl caproate, ethyl decanoate, ethyl laurate, and ethyl octanoate) had a positive relationship with the foaming parameters; this may be because the bubbles in the foam contain most aromatic volatiles, compared with the liquid phase, and when they start bursting gradually, these are released and perceived [33,34]. The relationship between Brix, o-tolualdehyde (cherry aroma), and "a" coincides with Abeytilakarathna et al. [35], who reported a correlation between Brix and red colors in red fruits, and Gonzalez Viejo et al. [19], who found a similar relationship in beer samples. ...
Article
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Increasing beer quality demands from consumers have put pressure on brewers to target specific steps within the beer-making process to modify beer styles and quality traits. However, this demands more robust methodologies to assess the final aroma profiles and physicochemical characteristics of beers. This research shows the construction of artificial intelligence (AI) models based on aroma profiles, chemometrics, and chemical fingerprinting using near-infrared spectroscopy (NIR) obtained from 20 commercial beers used as targets. Results showed that machine learning models obtained using NIR from beers as inputs were accurate and robust in the prediction of six important aromas for beer (Model 1; R = 0.91; b =0.87) and chemometrics (Model 2; R = 0.93; b =0.90). Additionally, two more accurate models were obtained from robotics (RoboBEER) to obtain same aroma profiles (Model 3; R = 0.99; b =1.00) and chemometrics (Model 4; R = 0.98; b =1.00). Low-cost robotics and sensors coupled with computer vision and machine learning modeling could help brewers in the decision-making process to target specific consumer preferences and to secure higher consumer demands.
... This coincides with findings by Kim et al. [46], who reported that samples of beverages and biscuits with the highest sugar content elicited positive emotions such as affectionate, pleased, joyful, glad, and happy. On the other hand, bitterness has been associated with rejection due to genetic factors and the innate relationship of bitter products with poisonous compounds [24,47,48]. Overall taste liking is the result of the intrinsic balance among the basic tastes that are sensed by the receptors located in the gustative system [49]. ...
Article
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Some chemical compounds, especially, alcohol, sugars, and alkaloids such as hordenine, have been reported as elicitors of different emotional responses. This preliminary study was based on six commercial beers selected according to their fermentation type with two beers of each (spontaneous, bottom, and top). Chemometry and sensory analysis were performed for all samples to determine relationships and patterns between chemical composition and emotional responses from consumers. Results showed that sweeter samples were associated with higher perceived liking by consumers and positive emotions, which corresponded to spontaneous fermentation beers. There was high correlation (R = 0.91; R2 = 0.83) between hordenine and alcohol content. Beers presenting higher concentrations of both and higher bitterness were related to negative emotions. Further studies should be conducted giving more time for emotional response analysis between beer samples and comparing alcoholic and non-alcoholic beers with similar styles to separate the effects of alcohol and hordenine. This preliminary study was a first attempt to associate beer compounds with the emotional responses of consumers using non-invasive biometrics.
... The visual attributes linked to bubbles are causally related to their quality. This is due to the relationship between bubbles, and other sensory characteristics, such as mouthfeel, the release of aromas, and changes in tastes and flavors [92,93]. The main components that determine bubble characteristics, foam formation, and stability are the dioxide carbon content, as well as some tensioactive or surfactant substances such as proteins and sugars. ...
Chapter
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Nowadays, the consumers ask for more than a drink, and in the market, it can be found a huge variety of beverages, where colloidal science is used for controlling flavor, color, presence of technological or nutritional value compounds, nutraceutical / bioactive compounds and, also, the beverage turbidity. Emulsions, foams, and suspensions are the basis of an extensive variety of use in the beverage industry. Beverage's emulsions are considerably diluted, contain little amounts of a dispersed oil phase in the finished product, and must remain physically stable for a long period of time. A beverage foam is a colloidal dispersion in which a gas is dispersed in a continuous liquid phase and foams are different from emulsions because it is the dispersion medium that has colloidal dimensions. Suspension drinks are dispersions of an insoluble substance (like lemon juice and pulp) in an aqueous or non-aqueous continuous phase. This chapter intends to make an overview of the modern advances in beverage-emulsions technology. Some examples are given, within the huge world of the beverages industry, from cream liqueurs, soft drinks, functional beverages to bottled water, fruit drinks, sparkling wine, and beer.
... Nevertheless, the representativeness of the population was not pursued, as the sample size was relatively small (N = 40), although it was large enough to find significant differences [69]. The number of participants in implicit measurement experiments is usually low due to the time required to individually gather the data from each participant, normally ranging from 30 to 75 respondents [34,70]. ...
Article
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Packaging is no longer a mere structural element that only aims to preserve foods, but it is also a powerful marketing tool able to affect product perception, purchase decision and consumers’ food choices. Incorporating consumers’ voices into packaging design through co-creation could maximise its impact on the market. The main goal of this exploratory study was to test the usefulness of co-creation with consumers for packaging design. For that purpose, a survey with 200 Spanish participants was conducted to find out which of the presented visual and textual packaging attributes were the most appropriate. A validation study with 40 participants using implicit (eye tracker, galvanic skin response and automatic facial expression analysis) and explicit measurements was used to test the packaging co-created by consumers against some of its possible competitors in the market. The co-creation process with consumers allowed for the identification of the visual and textual attributes, among the available options, that best fit their preferences, whereas the validation process confirmed that the packaging design co-created by consumers was equally or even preferred over the competitors. The information gathered might help designers and marketers to guide the packaging design for fish products in the Spanish market.
... Results from this study evidence the multiparameter prediction capacity of ANNs, making this method a powerful tool to assess multiple physiological parameters related to the representation of plant water status. This multitarget capability of ANN has been auspiciously used to assess many chemometric parameters of beers (Gonzalez Viejo et al., 2020;Gonzalez Viejo et al., 2019a;Gonzalez Viejo et al., 2019b). In winegrapes studies, the use of ANN successfully predicted aroma profiles of wines from different vintage weather and agronomical data (Fuentes et al., 2020c); evaluated the effect of berry cell death on wine quality (Fuentes et al., 2020b), and determined the effect of smoke contamination in canopies, berries and wine . ...
Article
Expectations are pre-existing beliefs developed due to prior interactions with products. This study aimed to understand how expectations raised from different packaging materials (four textures x two font colours) affect the approach-based consumer behaviours. Participants evaluated tetra pack, pouch, glass and plastic bottles using white and orange colour fonts in blind (tasting without information), expectation (only packaging) and informed (tasting with packaging) sessions. The sessions had significant effects on freshness and overall liking. Participants had higher expectations of overall liking, which were not fulfilled during the informed session, resulting in an assimilation effect. Participants penalized the juice from the glass-orange bottle and tetra pack-white with mean drops of 1.77 and 1.41 (too little sour). No penalty was observed for the tetra pack in the informed session. Negative emotions “worried” (0.22), “bored” (0.13) and “sad” (0.09) had inverse effects on purchase intention in the expectation session, while positive emotions “happy” (2.35), “enthusiastic” (6.61) and “joyful” (2.38) in the informed sessions had a positive influence on purchase intention. Therefore, marketers and product developers can use this early product research tool to identify expectations and emotions that drive liking and purchase behaviours in the market.
... These new technologies are based on biometrics and physiological responses from panellists assessed from video analysis and remote sensing techniques while tasting different products [40,41]. AI application based on biometrics, has been applied for different beverages [42,43], combined with robotic beverage pourers and integrated e-noses [44][45][46][47][48]. ...
Conference Paper
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Climate change has posed major risks for viticulture and winemaking around the world, related to increased ambient temperatures, the variability of rain events, higher occurrence and intensity of climatic anomalies, such as frosts and bushfires. These changes have directly impacted grapevine phenology by compressing stages and pushing forward in time harvest to hottest months, producing a dual warming effect. Bushfires also directly impact berry smoke contamination, which can be passed to the wine in the winemaking process producing smoke taint. Due to these events' complexities and their effects on viticulture and winemaking, a smarter approach is required to obtain relevant information and process it efficiently for more appropriate decision-making by different practitioners. In the last 10 years, artificial intelligence has offered various applications to be included in viticultural and winemaking operations, which has rendered important advances and information to deal with climate change adversities.
... The mean initial delays in our temporal curves ranged from 1.6 to 2.8 seconds, though our measure also includes the time taken to place the sample in mouth. Although biometric methods may have other pitfalls and biases, the use of measures like pupil dilation, heart rate, or facial expressions (Crist et al., 2018;Gonzalez Viejo, Fuentes, Howell, Torrico, & Dunshea, 2019) may provide additional information of perceptual timing to help validate our findings. Future methods should consider utilizing more advanced biometric measures in combination with classical sensory methods; alternatively, use of a computer controlled gustometer may help control timing of stimulus delivery. ...
Article
Prior data suggest humans can distinguish between isointense bitter stimuli (i.e., bitterness may not be unitary). Cues for such discrimination remain unclear but temporal and regional differences have both been implicated. Here, ten bitterants – caffeine, quinine, L-phenylalanine, L-tryptophan, urea, naringin, SOA (sucrose octaacetate), and 3 hop extracts –were assessed in water using time-intensity scaling. Trained assessors (n=14) rated overall intensity of each bitterant continuously for 90 seconds in triplicate using line scales. During tasting, solutions were swished in the mouth for 10 seconds and then swallowed. Temporal curves using normalized intensity ratings for each bitterant and replicate were obtained for each assessor. From these curves, various parameters were extracted using a Python script (provided in Supplementary Materials). For each parameter, differences between bitterants were tested in repeated measures ANOVAs that accounted for sample and replicate (fixed) and panelist (random) effects. Relationships between bitterants and parameters were explored further via Principal Component and Cluster Analysis. Collectively, these analyses revealed two distinct groups. Group 1 (caffeine, quinine, L-phenylalanine, L-tryptophan, urea) was characterized by its faster initial (in-mouth) rate of onset and faster rate of decay. Group 2 (naringin, SOA, hop extracts) was characterized by a slower initial rate of onset, an increase in intensity after swallowing, and a slower rate of decay. These data indicate bitter stimuli found in foods show substantial differences in their temporal profiles. Additional work is needed to determine causes of these temporal differences, and whether these properties may be systematically related to differential liking and/or intake of bitter food products.
... Interestingly, only two out of 22 studies considered physiological measures (De Wijk et al., 2019;Xu et al., 2019) within a context study, possibly due to the complexities of obtaining physiological measures beyond the laboratory. Some studies used wireless biometric measures for food related responses, but the physiological measures were often less sensitive in discriminating small differences between products in the laboratory (Gonzalez Viejo et al., 2019). This highlights a question concerning whether physiological measures are suitable to understand general day-to-day food choice behaviour in the laboratory or whether they are more suitable for a 'one-off' exciting and emotional product/ experience. ...
Article
Traditional consumer acceptance and preference testing rarely predicts food choice behaviour accurately. Increased interest in focusing on the total consumer experience, examining the relationship between the food and the consumer, and understanding that emotional processing drives human actions has led to the development of several instruments to capture consumer response beyond hedonic liking. This review aimed to identify and provide a comprehensive overview of the tools that have been used to measure emotion, implicitly and explicitly, in relation to food in the context of food behaviour. A second aim was to highlight the relative merits of key methodologies, research gaps and, based on the review findings, provide some recommendations for selecting food-evoked emotional response measures. The studies were assessed qualitatively using a 2 level (implicit, explicit) x 3 level (cognitive, behavioural, physiological) categorisation framework to conduct an exhaustive overview of measures used in current research. A total of 193 peer-reviewed studies evaluating consumer emotional response to food [published January 1997-March 2021] were identified, classified, and reviewed. Particularly, the why, when, what, where, who, and how of measuring food evoked emotion was discussed. No one “go-to” method to measure consumer emotional responses was evident and the optimal approach remains unknown. Several studies highlighted that, in principle, combining multiple measures would provide a clearer multidimensional insight into consumer emotional responses influencing consumer food choice behaviour. However, a clear gap remains in research investigating how emotional responses contribute to formulate better sensory experience and/or predict food choice.
... Usually, they correlate with the consumers' first impression, leading to a liking or disliking response. Alcohol content (correlated with sweetness), carbon dioxide content (also related with the visual attributes, mouthfeel and release of aromas), and the absence of off-flavors greatly affect the acceptance among the consumers [11][12][13][14]. Characteristics of beer quality (or of good beer) are [10] generally dependent on the beer style, but some basic properties include: ...
Article
Full-text available
The world’s beer market has never been more diverse. Most beers are produced in modern and technologically advanced breweries that use high quality raw materials, thus resulting in minor differences of physical–chemical properties between various beers (of the same style). However, consumers focus on constant quality and sensory properties of their chosen beer. Sensory evaluation is not an easy task and involves flexible methods for determination of differences and changes between beers. It is commonly used in breweries to provide constant quality of finished products, but also to ensure the quality of different raw materials (water, malt, hops) and to minimize the influence of the production process on final quality of beer.
... Results from this study evidence the multiparameter prediction capacity of ANNs, making this method a powerful tool to assess multiple physiological parameters related to the representation of plant water status. This multitarget capability of ANN has been auspiciously used to assess many chemometric parameters of beers (Gonzalez Viejo et al., 2020;Gonzalez Viejo et al., 2019a;Gonzalez Viejo et al., 2019b). In winegrapes studies, the use of ANN successfully predicted aroma profiles of wines from different vintage weather and agronomical data (Fuentes et al., 2020c); evaluated the effect of berry cell death on wine quality (Fuentes et al., 2020b), and determined the effect of smoke contamination in canopies, berries and wine . ...
Article
The implementation of artificial intelligence (AI) in parallel with remote sensing could be a powerful tool to manage irrigation scheduling on crops with narrow thresholds between water stress levels, such as cherry trees. This research assessed the water status of 'Regina' cherry trees using machine learning (ML) modeling from data extracted automatically using infrared thermal imagery (IRTI). These models were used to predict stomatal conductance (gs) and stem water potential (Ψs) (Model 1) and a complete assessment using a matrix differential analysis procedure per IRTI of cherry tree canopies' temperature and relative humidity (Model 2). Results showed that the supervised ML regression models presented high and significant correlation coefficients (R = 0.83 and R = 0.81, respectively) without signs of overfitting assessed through their performance. The complex interactions among climatic factors, the soil moisture, and canopy architecture observed in cherry trees or any other fruit tree oblige exploring the performance of ML-based models to offer simple alternatives for decision-making processes in the field.
... Biometrics based on physical characteristics of the human body demonstrate a good degree of suitability due to their uniqueness. Although face, fingerprint, voice, eyes and other physical biometric systems are already in operation, a fundamental limitation of such systems is that the data is more challenging to acquire and is often invasive to the user [7,8]. For example, many high-resolution and close-up images are required for training a facial recognition system. ...
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Fitness and activity tracking devices acquire, process and store rich behavioural data that are consumed by the end-user to learn health insights. This rich data source also enables a secondary use of being part of a biometric authentication system. However, there are many open research challenges with the use of data generated by fitness and activity trackers as a biometric source. In this article, the challenge of using data acquired from low-cost devices is tackled. This includes investigating how to best partition the data to deduce repeatable behavioural traits, while maximizing the uniqueness between participant datasets. In this exploratory research, 3 months’ worth of data (heart rate, step count and sleep) for five participants is acquired and utilized in its raw form from low-cost devices. It is established that dividing the data into 14-h segments is deemed the most suitable based on measuring coefficients of variance. Several supervised machine learning algorithms are then applied where the performance is evaluated by six metrics to demonstrate the potential of employing this data source in biometric-based security systems.
... Face recognition in images, in real-time video, or offline video are three of the most studied topics in computer vision [1]. Facial recognition is a way of recognizing a human face through technology. ...
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Face recognition plays an important role in the identity recognition system, the color and geometry feature has been claimed able to be used as parameter for face recognition. This study aims to analize the performance of geometric features, color features, and both of them on the human face using Gaussian Naïve Bayes (GNB) and the other Machine Learning method. This study using various geometric features: the distance between the eyes, nose, mouth by using Euclidean distance, and classified using GNB, K-Nearest Neighbour (KNN), and Support Vector Machine (SVM). The result compared with color feature: normalized RGB values, mean of normalized RGB, and RGB Variant as color features. The feature values obtained are assembled and processed using GNB and the other ML method to classified and recognized the faces. The dataset obtained from Aberdeen faces the dataset, which has 687 color faces from Ian Craw at Aberdeen. Between 1 and 18 images of 90 individuals. Some variations in lighting, varied viewpoints, and the resolution have varied between 336x480 to 624x544. The experimental results show that the system successfully recognized the face based on the determined algorithm and based on three models, SVM reached nearly 74.83%, GNB reached nearly 74.67%, and KNN with K = 5 reached nearly 72.17%.
... Other taste test examples include the exploration of cultural biases, motor behavior and decision-making when the hippocampus and dorsolateral prefrontal cortex of respondents were examined when blind tasting Coca-Cola and Pepsi (McClure et al., 2004). More recently, Viejo et al. (2019) used EEG to determine the consumer acceptability of beers, based only on their subconscious responses. ...
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Purpose This methodological paper aims to demonstrate the potential benefits of using consumer neuroscientific methodologies to measure consumers’ subconscious responses when consuming wine during a taste experiment. By comparing conscious and subconscious evaluations during a tasting experience this study illustrates how this methodology offers a more nuanced understanding of the consumer evaluation of wine during a consumption experience. Design/methodology/approach The research made use of a single-case taste test experiment whereby a wine expert blind-tasted 20 white wine varietals. Throughout each tasting, subconscious responses were measured using electroencephalography (EEG), combined with conscious measures of stated preferences using a questionnaire. Findings Stark differences were observed between the results of the conscious and subconscious wine evaluation measures, underscoring the complex nature of consumer decision-making and preference development. This study practically demonstrates the use and value of EEG as a consumer neuroscientific methodology in a wine marketing context. Originality/value This paper demonstrates the value of neuroscience techniques in identifying differences in the conscious and subconscious wine evaluation measures. This study practically demonstrates the use and value of EEG as a consumer neuroscientific methodology in a wine marketing context.
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Food products elicit both psychological and physiological reactions in consumers that influence their liking and buying decisions. In particular, the physiological reactions toward food products have recently become of interest to better understand consumer behavior. To increase the chances of success in the market, pre-sale food product assessments must incorporate the consumers’ physiological responses. This paper presents a novel sensory analysis system encompassing the measurement of several physiological parameters such as facial emotion recognition, galvanic skin response (GSR), and heart rate or pulse. Data fusion and machine learning methods allow predicting consumer acceptance of food samples. Experiments conducted with a large cohort of participants (120) suggest that facial expressions alone are not sufficient to determine consumer acceptance. However, when GSR and pulse signals are also considered, acceptance prediction is significantly improved. This work aims to contribute to the understanding of the human physiological reactions when interacting with food and to apply this knowledge to the food industry.
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There is an increasing interest, in consumer behaviour research related to food and beverage, in taking a step further from the traditional self-report questionnaires and organoleptic properties assessment. With the growing availability of psychophysiological data acquisition devices, and advancements in the study of the underlying signal sources seeking affective state assessment, the use of psychophysiological data analysis is a natural evolution in organoleptic testing. In this paper we propose a protocol for what can be defined as neuroorganoleptic analysis, a method that combines traditional approaches with psychophysiological data acquired during sensory testing. Our protocol was applied to a case study project named MobFood, where four samples of food were tested by a total of 83 participants, using preference and acceptance tasks, across three different sessions. Best practices and lessons learned regarding the laboratory setting and the acquisition of psychophysiological data were derived from this case study, which are herein described. Preliminary results show that certain Heart Rate Variability (HRV) features have a strong correlation with the preferences self-reported by the participants.
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The aim of this research is to investigate the possibility of applying a laser distance meter (LDM) as a complementary measurement method to image analysis during beer foam stability monitoring. The basic optical property of foam, i.e., its high reflectivity, is the main reason for using LDM. LDM measurements provide relatively precise information on foam height, even in the presence of lacing, and provide information as to when foam is no longer visible on the surface of the beer. Sixteen different commercially available lager beers were subjected to analysis. A camera and LDM display recorded the foam behavior; the LDM display which was placed close to the monitored beer glass. Measurements obtained by the image analysis of videos provided by the visual camera were comparable to those obtained independently by LDM. However, due to lacing, image analysis could not accurately detect foam disappearance. On the other hand, LDM measurements accurately detected the moment of foam disappearance since the measurements would have significantly higher values due to multiple reflections in the glass.
Article
Measures of drinking and eating behaviors may be assessed both explicitly (e.g., sensory and quality judgments) and implicitly (e.g., Electroencephalography, EEG), although the relationship between the results of both approaches remains unclear and each might be differentially affected by acquired knowledge. The main aim of the present study was to determine the strength of the relationship between these measures in sensory and hedonic processing of beers depending on the degree of tasting expertise. Beer experts, experts in non-beer beverages or edibles, and non-expert consumers took part in a sensory analysis procedure where they rated beers in terms of their sensory attributes and general quality—visual, olfactory, and gustatory phases—as well as their global hedonic value while their brain activity was recorded. The results suggest that participants evaluated the sensory properties of the beers in a rather similar manner. However, during the gustatory phase, experts and general tasters differed in terms of the activation of brain areas related to memory processes, while general tasters and consumers differed in brain activation related to hedonic processing. The relationship between self-reported quality judgments and EEG activity — particularly in relation to recognition and working memory components — appeared to be stronger in experts in comparison with the other groups (lowest |r| = .67, p < .01). Although lower in number, significant relationships were also found in general tasters and consumers, primarily involving hedonic processing (lowest |r| = .58, p < .01) and recognition memory (lowest |r| = .57, p < .01) components. Moreover, those relationships differed significantly, mostly between experts and consumers (lowest |z| = 2.68, p < .01), in terms of the involvement of working memory components. Taken together, the results of this study suggest that beer experts have a more efficient pattern of gustatory processing and show a better fit between explicit (judgments) and implicit (EEG) measures of sensory and hedonic quality of beers.
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The importance of branding in marketing strategies makes it essential to understand the elements that give value-added to the brands. The very nature of ecological brands adds value to them. Knowing the underlying emotions of the elements that add value to brands can justify the benefit of applying neuromarketing to branding. The objective of this study is to justify the use of neuromarketing tools in the ecological branding strategy by analysing the existing literature on branding, ecological branding and neuromarketing. The existing relationship between the elements that give value-added to the brand and the emotional variables that neuromarketing measures, justifies the use of neuromarketing tools in the ecological branding strategy.
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Foam stability and retention is an important indicator of beer quality and freshness. A full, white head of foam with nicely distributed small bubbles of CO2 is appealing to the consumers and the crown of the production process. However, raw materials, production process, packaging, transportation, and storage have a big impact on foam stability, which marks foam stability monitoring during all these stages, from production to consumer, as very important. Beer foam stability is expressed as a change of foam height over a certain period. This research aimed to monitor the foam stability of lager beers using image analysis methods on two different types of recordings: RGB and depth videos. Sixteen different commercially available lager beers were subjected to analysis. The automated image analysis method based only on the analysis of RGB video images proved to be inapplicable in real conditions due to problems such as reflection of light through glass, autofocus, and beer lacing/clinging, which make it impossible to accurately detect the actual height of the foam. A solution to this problem, representing a unique contribution, was found by introducing the use of a 3D camera in estimating foam stability. According to the results, automated analysis of depth images obtained from a 3D camera proved to be a suitable, objective, repeatable, reliable, and sufficiently sensitive method for measuring foam stability of lager beers. The applied model proved to be suitable for predicting changes in foam retention of lager beers.
Chapter
Packaging creates the first impression from consumers when selecting commercial food or beverages. Different packaging components are important as they contain all areas of interest related to branding, shape, design, and nutritional information, which could determine willingness to purchase and success of products in the market. However, traditional packaging acceptability assessments based on focus groups, acceptance and preference tests may be biased and subjective. Therefore, novel assessment methods have been developed based on more objective parameters, including non-invasive biometrics such as eye-tracking, emotional responses from consumers and changes in physiological parameters, such as heart rate and body temperature. Emerging technologies have also been studied for packaging assessment, such as virtual/augmented reality and artificial intelligence tools, including computer vision and machine learning modelling. Furthermore, counterfeiting has been a major issue among commercial products, with food and beverages accounting for 10% counterfeited, including packaging and branding. This chapter focuses on the latest research on intelligent and digital technologies for packaging development, assessing consumer acceptability towards packaging and authentication using new and emerging digital technologies.
Article
Purpose Visual stimulation affects the taste of food and beverages. This study aimed to understand how latte art affects coffee consumption by collecting participants' brainwave data and their taste responses. Design/methodology/approach Seventy subjects participated in a two-stage experiment. Electroencephalography (EEG) was employed to measure brainwave activity. With an interval of one week, each stage involved coffee consumption with and without latte art. The responses to the taste of the coffee were also collected for analysis. Findings Significant differences were found in the participants' alpha and beta brainwave bands. When drinking coffee with latte art, the participants' alpha bands were significantly lower, whereas the beta bands were higher. These findings were supported by Bayesian statistics. A significant increase was found in the participants' taste of sweetness and acidity with latte art, and Bayesian statistics confirmed the results for sweetness although the evidence on the increase in acidity was anecdotal. No difference was found in the taste of bitterness. Originality/value This study highlights the effect of latte art on coffee consumption. The authors analysed the empirical evidence from this two-stage experimental study in the form of the participants' brainwave data and their responses to taste. This study's original contribution is that it explored the crossmodal effects of latte art on consumers' taste of coffee from a neuroscientific perspective. The results of this study can provide empirical evidence on how to effectively use latte art in practical business environments.
Book
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n recent years, new and emerging digital technologies applied to Food Science have been gaining attention and increased interest from researchers and the food/beverage industries. Especially those digital technologies that can be used throughout the food value chain are accurate, easy to implement, affordable, and user-friendly. Hence, this special issue (SI) was dedicated to novel technology based on sensor technology and machine/deep learning modeling strategies to implement Artificial Intelligence (AI) in food and beverage production and for consumer assessment. This special issue (SI) published quality papers from researchers in Australia, New Zealand, the United States, Spain, and Mexico, including food and beverage products such as grapes and wine, chocolate, honey, whiskey, avocado pulp, and a variety of other food products.
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Recipes for traditional and sour non-alcoholic beers were developed in this study employing a special yeast species Saccharomycodes ludwigii. They were characterized for their basic physicochemical properties, antioxidative activity as well as subjected to the quantitative and qualitative analysis of their biologically-active compounds, and to the sensory assessment. Sour non-alcoholic beers were brewed with the addition of juice from fruits of red-colored Cornelian cherry (Cornus mas L.) variety, which are characterized by naturally sour taste and aroma. Ethyl alcohol content in the beers manufactured ranged from 0.41%v/v in traditional non-alcoholic beers to 0.43%v/v in sour non-alcoholic beers. The final products had a low energy value, ranging from 116 to 148 kcal/500 mL of beer. The sour beers had several times higher antioxidative potential and significantly higher polyphenols concentration compared to the control ones. In addition, they were rich in anthocyanins and iridoids, and presented novel sensory attributes.
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The high failure rate of new market introductions, despite initial successful testing with traditional sensory and consumer tests, necessitates the development of other tests. This study explored the ability of selected physiological and behavioral measures of the autonomic nervous system (ANS) to distinguish between repeated exposures to foods from a single category (breakfast drinks) and with similar liking ratings. In this within-subject study 19 healthy young adults sipped from five breakfast drinks, each presented five times, while ANS responses (heart rate, skin conductance response and skin temperature), facial expressions, liking, and intensities were recorded. The results showed that liking was associated with increased heart rate and skin temperature, and more neutral facial expressions. Intensity was associated with reduced heart rate and skin temperature, more neutral expressions and more negative expressions of sadness, anger and surprise. Strongest associations with liking were found after 1 second of tasting, whereas strongest associations with intensity were found after 2 seconds of tasting. Future studies should verify the contribution of the additional information to the prediction of market success.
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The authors investigate consumers’ willingness to switch from a preferred manufacturer brand to an unfamiliar private-label brand if taste is perceived as identical. Consumer decisions are examined through recordings of electrical brain activity in the form of electroencephalograms (EEGs) and self-reported data captured in surveys. Results reveal a willingness of consumers to switch to a less-expensive brand when the quality is perceived to be the same as the more expensive counterpart. Cost saving options for consumers and advertising considerations for managers are discussed. Available at http://digitalcommons.kennesaw.edu/kjur/vol2/iss1/5
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Why we like or dislike certain products may be better captured by physiological and behavioral measures of the autonomic nervous system (ANS) than by conscious or classical sensory tests. Responses to pleasant and unpleasant food odors presented in varying concentrations were assessed continuously using facial expressions and responses of the ANS. Results of 26 young and healthy female participants showed that the unpleasant fish odor triggered higher heart rates and skin conductance responses, lower skin temperature, fewer neutral facial expressions and more disgusted and angry expressions (p < 0.05). Neutral facial expressions differentiated between odors within 100 ms, after the start of the odor presentation followed by expressions of disgust (180 ms), anger (500 ms), surprised (580 ms), sadness (820 ms), scared (1020 ms), and happy (1780 ms) (all p-values < 0.05). Heart rate differentiated between odors after 400 ms, whereas skin conductance responses differentiated between odors after 3920 ms. At shorter intervals (between 520 and 1000 ms and between 2690 and 3880 ms) skin temperature for fish was higher than that for orange, but became considerable lower after 5440 ms. This temporal unfolding of emotions in reactions to odors, as seen in facial expressions and physiological measurements supports sequential appraisal theories.
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The chemistry of beer flavor instability remains shrouded in mystery, despite decades of extensive research. It is, however, certain that aldehydes play a crucial role because their concentration increase coincides with the appearance and intensity of "aged flavors". Several pathways give rise to a variety of key flavor-active aldehydes during beer production, but it remains unclear as to what extent they develop after bottling. There are indications that aldehydes, formed during beer production, are bound to other compounds, obscuring them from instrumental and sensory detection. Because freshly bottled beer is not in chemical equilibrium, these bound aldehydes might be released over time, causing stale flavor. This review discusses beer aging and the role of aldehydes, focusing on both sensory and chemical aspects. Several aldehyde formation pathways are taken into account, as well as aldehyde binding in and release from imine and bisulfite adducts.
Conference Paper
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This study aims at finding the relationship between EEG signals and human emotions. EEG signals are used to classify two kinds of emotions, positive and negative. First, we extracted features from original EEG data and used a linear dynamic system approach to smooth these features. An average test accuracy of 87.53% was obtained by using all of the features together with a support vector machine. Next, we reduced the dimension of features through correlation coefficients. The top 100 and top 50 subject-independent features were achieved, with average test accuracies of 89.22% and 84.94%, respectively. Finally, a manifold model was applied to find the trajectory of emotion changes.
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Autonomic nervous system (ANS) activity is viewed as a major component of the emotion response in many recent theories of emotion. Positions on the degree of specificity of ANS activation in emotion, however, greatly diverge, ranging from undifferentiated arousal, over acknowledgment of strong response idiosyncrasies, to highly specific predictions of autonomic response patterns for certain emotions. A review of 134 publications that report experimental investigations of emotional effects on peripheral physiological responding in healthy individuals suggests considerable ANS response specificity in emotion when considering subtypes of distinct emotions. The importance of sound terminology of investigated affective states as well as of choice of physiological measures in assessing ANS reactivity is discussed.
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As champagne or sparkling wine is poured into a glass, the myriad of ascending bubbles collapse and radiate a multitude of tiny droplets above the free surface into the form of very characteristic and refreshing aerosols. Ultrahigh-resolution MS was used as a nontargeted approach to discriminate hundreds of surface active compounds that are preferentially partitioning in champagne aerosols; thus, unraveling different chemical fingerprints between the champagne bulk and its aerosols. Based on accurate exact mass analysis and database search, tens of these compounds overconcentrating in champagne aerosols were unambiguously discriminated and assigned to compounds showing organoleptic interest or being aromas precursors. By drawing a parallel between the fizz of the ocean and the fizz in Champagne wines, our results closely link bursting bubbles and flavor release; thus, supporting the idea that rising and collapsing bubbles act as a continuous paternoster lift for aromas in every glass of champagne.
Conference Paper
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This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first is the introduction of a new image representation called the "integral image" which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features from a larger set and yields extremely efficient classifiers. The third contribution is a method for combining increasingly more complex classifiers in a "cascade" which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. The cascade can be viewed as an object specific focus-of-attention mechanism which unlike previous approaches provides statistical guarantees that discarded regions are unlikely to contain the object of interest. In the domain of face detection the system yields detection rates comparable to the best previous systems. Used in real-time applications, the detector runs at 15 frames per second without resorting to image differencing or skin color detection.
Article
Sensory evaluation relies on explicit responses from consumers. Unconscious responses may complement the information regarding the emotional states of consumers. In this study, physiological, facial expression and sensory/emotional responses to different visual (images) and chocolate stimuli were evaluated using two groups (participants with Asian and Western backgrounds). Panellists (N=60; 60% Asian-background and 40% Western-background) evaluated 15 images (5-positive/5-neutral/5-negative) and 4 chocolate samples (milk/60%-cocoa/70%-cocoa/candy-inclusions). Consumers assessed their emotions (3-point scale) and liking (9-point scale). Non-invasive peripheral skin temperature (ST), heart rate (BPM), and facial expressions using FaceReaderTM (FR) were assessed. Western-background participants showed similar heart rate (55-59 vs. 54-59) and temperature (0.6-1.5 oC difference) compared to Asian-background participants for images and chocolate samples. BPM (54-59) was not different among stimuli. Consumer emotions (images=-0.87 to 1.00 and chocolate=0.27 to 0.60) and liking (chocolate=5.20 to 6.33) were evaluated for both groups. For Asian-background participants, ST was positively correlated to FR-happy (r=0.45) and negatively correlated to FR-angry (r=-0.23) and FR-sad (r=-0.20). For Western-background participants, ST was positively correlated to FR-sad (r=0.23) and negatively correlated to FR-angry (r=-0.35). Cultural differences were found when assessing images based on sensory responses. These findings will be useful to better understand acceptability based on unconscious and emotional responses.
Book
Understanding "what" consumers want and "why" are two of the most significant hurdles faced by any business creating products for consumers. Properly conducted sensory research experiments can provide answers to these questions and more. Sensory evaluation provides strategic information at various stages in the product lifecycle including the front end of innovation, new product development, product optimization, marketplace audits, and quality control among others. Sensory research can help identify issues that contribute to a product's success (or failure). This fourth edition draws on the author's practical experience in partnering with business associates in marketing and development teams to bring creativity and innovation to consumer driven product development in today's global business environment. The field of sensory science continues to grow and is now recognized as a strategic source of information for many Fortune 500 companies. Many scientists working in this field depend on the core textbooks such as this one to enhance their working knowledge base with practical business applications. * Appeals to sensory professionals in both in academia and business * Methods to integrate sensory descriptive information and consumer assessment * Coordinate marketing messages and imagery with the product's sensory experience.
Article
Cross-cultural research is becoming increasingly relevant in sensory and consumer science. The design of cross-cultural studies involves several methodological challenges that are not commonly faced in studies involving a single culture. However, several of these challenges have not yet received enough attention in the field, which poses several limitations to the validity and generalizability of the findings of cross-cultural studies. In this context, the aim of the present work is to review some of the most relevant methodological issues that should be considered when designing cross-cultural studies. In particular, five topics are addressed: sampling procedures, conceptual equivalence, linguistic equivalence, data collection procedures and cultural differences in response style. Suggestions and recommendations are discussed for each of the topics, which are expected to encourage greater methodological rigor and contribute to more theoretical robust cross-cultural studies in the field.
Article
In line with research in non-Western countries becoming main-stream, the need to validate existing research methods with consumers from these populations increase. The present research contributes hereto by quasi-replicating with Korean and Chinese consumers previous research concerning the risk of hedonic product responses being biased by co-elicitation of CATA/RATA questions for sensory product characterisation. Using consumers in several Western countries it was previously reported that bias could occur, but was unlikely to. Eleven studies involving ∼1000 East Asian consumers confirmed this conclusion. The studies were conducted with diversified populations and across multiple product categories. Across 7 studies, there were no instances where CATA co-elicitation was found to bias hedonic scores. However, in one of four studies where RATA responses were co-elicited bias did occur, and hedonic scores were, on average, lower when RATA responses were co-elicited. It is recommended that the research be replicated with consumers residing in their home countries and extended to other East and South-East Asian counties.
Article
There are currently no standardized objective measures to assess beer quality based on the most significant parameters related to the first impression from consumers, which are visual characteristics of foamability, beer color and bubble size. This study describes the development of an affordable and robust robotic beer pourer using low-cost sensors, Arduino® boards, Lego® building blocks and servo motors for prototyping. The RoboBEER is also coupled with video capture capabilities (iPhone 5S) and automated post hoc computer vision analysis algorithms to assess different parameters based on foamability, bubble size, alcohol content, temperature, carbon dioxide release and beer color. Results have shown that parameters obtained from different beers by only using the RoboBEER can be used for their classification according to quality and fermentation type. Results were compared to sensory analysis techniques using principal component analysis (PCA) and artificial neural networks (ANN) techniques. The PCA from RoboBEER data explained 73% of variability within the data. From sensory analysis, the PCA explained 67% of the variability and combining RoboBEER and Sensory data, the PCA explained only 59% of data variability. The ANN technique for pattern recognition allowed creating a classification model from the parameters obtained with RoboBEER, achieving 92.4% accuracy in the classification according to quality and fermentation type, which is consistent with the PCA results using data only from RoboBEER. The repeatability and objectivity of beer assessment offered by the RoboBEER could translate into the development of an important practical tool for food scientists, consumers and retail companies to determine differences within beers based on the specific parameters studied.
Article
Quality assessment of food products and beverages might be performed by the human senses of smell, taste, sound and touch. Likewise, sparkling wines and carbonated beverages are fundamentally assessed by sensory evaluation. Computer vision is an emerging technique that has been applied in the food industry to objectively assist quality and process control. However, publications describing the application of this novel technology to carbonated beverages are scarce, as the methodology requires tailored techniques to address the presence of carbonation and foamability. Here we present a robotic pourer (FIZZeyeRobot), which normalizes the variability of foam and bubble development during pouring into a vessel. It is coupled with video capture to assess several parameters of foam quality, including foamability (the ability of the foam to form) drainability (the ability of the foam to resist drainage) and bubble count and allometry. The foam parameters investigated were analyzed in combination to the wines scores, chemical parameters obtained from laboratory analysis and manual measurements for validation purposes. Results showed that higher quality scores from trained panelists were positively correlated with foam stability and negatively correlated with the velocity of foam dissipation and the height of the collar. Significant correlations were observed between the wine quality measurements of total protein, titratable acidity, pH and foam expansion. The percentage of the wine in the foam was found to promote the formation of smaller bubbles and to reduce foamability, while drainability was negatively correlated to foam stability and positively correlated with the duration of the collar. Finally, wines were grouped according to their foam and bubble characteristics, quality scores and chemical parameters. The technique developed in this study objectively assessed foam characteristics of sparkling wines using image analysis whilst maintaining a cost-effective, fast, repeatable and reliable robotic method. Relationships between wine composition, bubble and foam parameters obtained automatically, might assist in unraveling factors contributing to wine quality and directions for further research.
Book
Sensory evaluation methods are extensively used in the wine, beer and distilled spirits industries for product development and quality control, while consumer research methods also offer useful insights as the product is being developed. This book introduces sensory evaluation and consumer research methods and provides a detailed analysis of their applications to a variety of different alcoholic beverages. Chapters in part one look at the principles of sensory evaluation and how these can be applied to alcoholic beverages, covering topics such as shelf life evaluation and gas chromatography - olfactometry. Part two concentrates on fermented beverages such as beer and wine, while distilled products including brandies, whiskies and many others are discussed in part three. Finally, part four examines how consumer research methods can be employed in product development in the alcoholic beverage industry.
Book
This book is a practical guide to sensory evaluation methods and techniques in the food, cosmetic and household product industries. It explains the suitability of different testing methods for different situations and offers step-by-step instructions on how to perform the various types of tests. Covering a broad range of food and non-food product applications, the book is designed to be used as a practical reference in the testing environment; a training manual for new recruits into sensory science, and a course book for students undertaking industrial training or academic study. © 2009 S.E. Kemp, T. Hollowood and J. Hort. All rights reserved.
Article
Brain signals have been investigated within the medical field for more than a century to study brain diseases like epilepsy, spinal cord injuries, Alzheimer's, Parkinson's, schizophrenia, and stroke among others. They are also used in both brain computer and brain machine interface systems with assistance, rehabilitative, and entertainment applications. Despite the broad interest in clinical applications, the use of brain signals has been only recently investigated by the scientific community as a biometric characteristic to be used in automatic people recognition systems. However, brain signals present some peculiarities, not shared by the most commonly used biometrics, such as face, iris, and fingerprints, with reference to privacy compliance, robustness against spoofing attacks, possibility to perform continuous identification, intrinsic liveness detection, and universality. These peculiarities make the use of brain signals appealing. On the other hand, there are many challenges which need to be properly addressed. The understanding of the level of uniqueness and permanence of brain responses, the design of elicitation protocols, and the invasiveness of the acquisition process are only few of the challenges which need to be tackled. In this paper, we further speculate on those issues, which represent an obstacle toward the deployment of biometric systems based on the analysis of brain activity in real life applications and intend to provide a critical and comprehensive review of state-of-the-art methods for electroencephalogram-based automatic user recognition, also reporting neurophysiological evidences related to the performed claims.
Article
Our goal is to reveal temporal variations in videos that are difficult or impossible to see with the naked eye and display them in an indicative manner. Our method, which we call Eulerian Video Magnification, takes a standard video sequence as input, and applies spatial decomposition, followed by temporal filtering to the frames. The resulting signal is then amplified to reveal hidden information. Using our method, we are able to visualize the flow of blood as it fills the face and also to amplify and reveal small motions. Our technique can run in real time to show phenomena occurring at the temporal frequencies selected by the user.
Article
Facial expressions of 23 neonates were analyzed for specific features following oral stimulation with distilled water, 0.1 and 1.0 M sucrose, 0.15 and 0.25 M urea, and 0.0001 M quinine hydrochloride. Responses were videotaped and later decoded in a double-blind setting. While some features were present for all stimulations, other components were consistently associated with a specific taste quality. Within a quality, increasing the concentration elevated the incidence of features associated with that quality. Estimates of magnitude and hedonic tone conveyed by the total facial response to each stimulus suggested that the face was, within limits, an accurate reflection of stimulus quality and intensity.
Article
The use of brain computer interface (BCI) devices in research and applications has exploded in recent years. Applications such as lie detectors that use functional magnetic resonance imaging (fMRI) to video games controlled using electroencephalography (EEG) are currently in use. These developments, coupled with the emergence of inexpensive commercial BCI headsets, such as the Emotiv EPOC ( http://emotiv.com/index.php ) and the Neurosky MindWave, have also highlighted the need of performing basic ergonomics research since such devices have usability issues, such as comfort during prolonged use, and reduced performance for individuals with common physical attributes, such as long or coarse hair. This paper examines the feasibility of using consumer BCIs in scientific research. In particular, we compare user comfort, experiment preparation time, signal reliability and ease of use in light of individual differences among subjects for two commercially available hardware devices, the Emotiv EPOC and the Neurosky MindWave. Based on these results, we suggest some basic considerations for selecting a commercial BCI for research and experimentation. STATEMENT OF RELEVANCE: Despite increased usage, few studies have examined the usability of commercial BCI hardware. This study assesses usability and experimentation factors of two commercial BCI models, for the purpose of creating basic guidelines for increased usability. Finding that more sensors can be less comfortable and accurate than devices with fewer sensors.
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