Kees de Graaf

Evoked Emotions Predict Food Choice

In the current study we show that non-verbal food-evoked emotion scores significantly improve food choice prediction over merely liking scores. Previous research has shown that liking measures correlate with choice. However, liking is no strong predictor for food choice in real life environments. Therefore, the focus within recent studies shifted towards using emotion-profiling methods that successfully can discriminate between products that are equally liked. However, it is unclear how well scores from emotion-profiling methods predict actual food choice and/or consumption. To test this, we proposed to decompose emotion scores into valence and arousal scores using Principal Component Analysis (PCA) and apply Multinomial Logit Models (MLM) to estimate food choice using liking, valence, and arousal as possible predictors. For this analysis, we used an existing data set comprised of liking and food-evoked emotions scores from 123 participants, who rated 7 unlabeled breakfast drinks. Liking scores were measured using a 100-mm visual analogue scale, while food-evoked emotions were measured using 2 existing emotion-profiling methods: a verbal and a non-verbal method (EsSense Profile and PrEmo, respectively). After 7 days, participants were asked to choose 1 breakfast drink from the experiment to consume during breakfast in a simulated restaurant environment. Cross validation showed that we were able to correctly predict individualized food choice (1 out of 7 products) for over 50% of the participants. This number increased to nearly 80% when looking at the top 2 candidates. Model comparisons showed that evoked emotions better predict food choice than perceived liking alone. However, the strongest predictive strength was achieved by the combination of evoked emotions and liking. Furthermore we showed that non-verbal food-evoked emotion scores more accurately predict food choice than verbal food-evoked emotions scores.

The determinants of food choice

Health nudge interventions to steer people into healthier lifestyles are increasingly applied by governments worldwide, and it is natural to look to such approaches to improve health by altering what people choose to eat. However, to produce policy recommendations that are likely to be effective, we need to be able to make valid predictions about the consequences of proposed interventions, and for this, we need a better understanding of the determinants of food choice. These determinants include dietary components (e.g. highly palatable foods and alcohol), but also diverse cultural and social pressures, cognitive-affective factors (perceived stress, health attitude, anxiety and depression), and familial, genetic and epigenetic influences on personality characteristics. In addition, our choices are influenced by an array of physiological mechanisms, including signals to the brain from the gastrointestinal tract and adipose tissue, which affect not only our hunger and satiety but also our motivation to eat particular nutrients, and the reward we experience from eating. Thus, to develop the evidence base necessary for effective policies, we need to build bridges across different levels of knowledge and understanding. This requires experimental models that can fill in the gaps in our understanding that are needed to inform policy, translational models that connect mechanistic understanding from laboratory studies to the real life human condition, and formal models that encapsulate scientific knowledge from diverse disciplines, and which embed understanding in a way that enables policy-relevant predictions to be made. Here we review recent developments in these areas.

The Differential Role of Smell and Taste For Eating Behavior

Food choice and food intake are guided by both sensory and metabolic processes. The senses of taste and smell play a key role in the sensory effects on choice and intake. This article provides a comprehensive overview of, and will argue for, the differential role of smell and taste for eating behavior by focusing on appetite, choice, intake, and satiation. The sense of smell mainly plays a priming role in eating behavior. It has been demonstrated that (orthonasal) odor exposure induces appetite specifically for the cued food. However, the influence of odors on food choice and intake is less clear, and may also depend on awareness or intensity of the odors, or personality traits of the participants. Taste on the other hand, has a clear role as a (macro)nutrient sensing system, during consumption. Together with texture, taste is responsible for eating rate, and thus in determining the oral exposure duration of food in the mouth, thereby contributing to satiation. Results from these experimental studies should be taken to real-life situations, to assess longer-term effects on energy intake. With this knowledge, it will be possible to steer people’s eating behavior, as well as food product development, toward a less obesogenic society.

Energy Intake Compensation After 3 Weeks of Restricted Energy Intake in Young and Elderly Men

OBJECTIVES Decreased energy intake in older persons poses these people at risk of progressive weight loss. It may result from a failure to regulate energy intake and expenditure after periods of underfeeding. The objective of this study was to investigate if a period of underfeeding differentially influences energy intake of older compared with young men and, additionally, to study potential underlying mechanisms, namely changes in gastric emptying rate and cholecystokinin (CCK) levels in blood. DESIGN/SETTING Dietary intervention of 3 phases. After a phase of energy balance, we fed participants in phase 2 by a mean of 70% of their needs for 21 days. During phase 3, we assessed ad libitum energy intake of the participants during 9 days. At the end of phases 1 and 2, we assessed appetite, gastric emptying, and CCK levels in blood in response to a test meal. PARTICIPANTS Fifteen young (age 24 years [range 20-34], body mass index 23.0 kg/m(2) ± 2.3) and 17 older (age 68 years [64-85], body mass index 24.5 kg/m(2) ± 1.9) men participated in this study. RESULTS During energy balance, mean energy intake of young men (14.3 ± 2.3 MJ/day) was significantly higher than that of older men (11.3 ± 1.8 MJ/day, P < .001). After the period of underfeeding, energy intake in phase 3 amounted to 16.3 ± 2.6 MJ/day in young men and to 14.4 ± 3.2 MJ/day in older men. Ad lib energy intake after underfeeding did not differ between young and older men (analysis of covariance, with energy intake during phase 1 as covariate, P = .99). There were no differential changes in body weight, body composition, resting energy expenditure, gastric emptying rate, CCK-8 levels, and appetite between young and older men during the study. CONCLUSION Our results do not indicate that older men have an impaired ability to control energy intake after a period of underfeeding compared with younger men. TRIAL REGISTRATION NCT00561145.

Severity of olfactory deficits is reflected in functional brain networks-An fMRI study

Even though deficits in olfactory function affect a considerable part of the population, the neuronal basis of olfactory deficits remains scarcely investigated. To achieve a better understanding of how smell loss affects neural activation patterns and functional networks, we set out to investigate patients with olfactory dysfunction using functional magnetic resonance imaging (fMRI) and olfactory stimulation. We used patients’ scores on a standardized olfactory test as continuous measure of olfactory function. 48 patients (mean olfactory threshold discrimination identification (TDI) score = 16.33, SD = 6.4, range 6 ‐ 28.5) were investigated. Overall, patients showed piriform cortex activation during odor stimulation compared to pure sniffing. Group independent component analysis indicated that the recruitment of three networks during odor stimulation was correlated with olfactory function: a sensory processing network (including regions such as insula, thalamus and piriform cortex), a cerebellar network and an occipital network. Interestingly, recruitment of these networks during pure sniffing was related to olfactory function as well. Our results support previous findings that sniffing alone can activate olfactory regions. Extending this, we found that the severity of olfactory deficits is related to the extent to which neural networks are recruited both during olfactory stimulation and pure sniffing. This indicates that olfactory deficits are not only reflected in changes in specific olfactory areas but also in the recruitment of occipital and cerebellar networks. These findings pave the way for future investigations on whether characteristics of these networks might be of use for the prediction of disease prognosis or of treatment success.

An E-liquid Flavor Wheel: A Shared Vocabulary based on Systematically Reviewing E-liquid Flavor Classifications in Literature.

Abstract Introduction E-liquids are available in a high variety of flavors. A systematic classification of e-liquid flavors is necessary to increase comparability of research results. In the food, alcohol, and fragrance industry, flavors are classified using flavor wheels. We systematically reviewed literature on flavors related to electronic cigarette use, to investigate how e-liquid flavors have been classified in research, and propose an e-liquid flavor wheel to classify e-liquids based on marketing descriptions. Methods The search was conducted in May 2017 using PubMed and Embase databases. Keywords included terms associated with electronic cigarette, flavors, liking, learning, and wanting in articles. Results were independently screened and reviewed. Flavor categories used in the articles reviewed were extracted. Results Searches yielded 386 unique articles of which 28 were included. Forty-three main flavor categories were reported in these articles (eg, tobacco, menthol, mint, fruit, bakery/dessert, alcohol, nuts, spice, candy, coffee/tea, beverages, chocolate, sweet flavors, vanilla, and unflavored). Flavor classifications of e-liquids in literature showed similarities and differences across studies. Our proposed e-liquid flavor wheel contains 13 main categories and 90 subcategories, which summarize flavor categories from literature to find a shared vocabulary. For classification of e-liquids using our flavor wheel, marketing descriptions should be used. Conclusions We have proposed a flavor wheel for classification of e-liquids. Further research is needed to test the flavor wheels’ empirical value. Consistently classifying e-liquid flavors using our flavor wheel in research (eg, experimental, marketing, or qualitative studies) minimizes interpretation differences and increases comparability of results. Implications We reviewed e-liquid flavors and flavor categories used in research. A large variation in the naming of flavor categories was found and e-liquid flavors were not consistently classified. We developed an e-liquid flavor wheel and provided a guideline for systematic classification of e-liquids based on marketing descriptions. Our flavor wheel summarizes e-liquid flavors and categories used in literature in order to create a shared vocabulary. Applying our flavor wheel in research on e-liquids will improve data interpretation, increase comparability across studies, and support policy makers in developing rules for regulation of e-liquid flavors.