It has been shown that the identification of many foods including vegetables based on flavour cues is often difficult. The effect of providing texture cues in addition to flavour cues on the identification of foods and the effect of providing taste cues only on the identification of foods have not been studied. The aim of this study was to assess the role of smell, taste, flavour and texture cues in the identification of ten vegetables commonly consumed in The Netherlands (broccoli, cauliflower, French bean, leek, bell pepper, carrot, cucumber, iceberg lettuce, onion and tomato). Subjects (n ¼ 194) were randomly assigned to one smell (orthonasal), flavour (taste and smell) and flavour-texture (taste, smell and texture). Blindfolded subjects were asked to identify the vegetable from a list of 24 vegetables. Identification was the highest in the flavour-texture condition (87.5%). Identification was significantly lower in the flavour condition (62.8%). Identification was the lowest when only taste cues (38.3%) or only smell cues (39.4%) were provided. For four raw vegetables (carrot, cucumber, onion and tomato) providing texture cues in addition to flavour cues did not significantly change identification suggesting that flavour cues were sufficient to identify these vegetables. Identification frequency increased for all vegetables when perceived intensity of the smell, taste or flavour cue increased. We conclude that providing flavour cues (taste and smell) increases identification compared to only taste or only smell cues, combined flavour and texture cues are needed for the identification of many vegetables commonly consumed in The Netherlands.
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The recent success of Machine Learning encouraged research using artificial neural networks (NNs) in computer graphics. A good example is the bidirectional texture function (BTF), a data-driven representation of surface materials that can encapsulate complex behaviors that would otherwise be too expensive to calculate for real-time applications, such as self-shadowing and interreflections. We propose two changes to the state-of-the-art using neural networks for BTFs, specifically NeuMIP. These changes, suggested by recent work in neural scene representation and rendering, aim to improve baseline quality, memory footprint, and performance. We conduct an ablation study to evaluate the impact of each change. We test both synthetic and real data, and provide a working implementation within the Mitsuba 2 rendering framework. Our results show that our method outperforms the baseline in all these metrics and that neural BTF is part of the broader field of neural scene representation. Project website: https://traverse-research.github.io/NeuBTF/.
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This study examined the effect of meals varying in amount, size, and hardness of food pieces on the development of the chewing capabilities of 8-month-old infants. The study also examined changes in shivering, gagging, coughing, choking, and their ability to eat from a spoon. In an in-home setting two groups were given commercially available infant meals and fruits, purees with either less, smaller and softer or more, larger and harder pieces. Both groups were given these foods for 4 weeks and were monitored several times during this period. After the 4-week exposure period infants in both groups were given the same five test foods. Structured questionnaires with questions on eating behavior and the child's development were conducted 6 times in the 4 to 12-month period and video analyses of feedings were conducted 4 times between 8 and 9 months. After the 4-week exposure period, the group that had been exposed to the foods with more, larger and harder pieces showed a significantly higher rating for chewing a piece of carrot and potato for the first time, but not for a piece of banana nor for mashed foods. Shivering, gagging, coughing, choking, and ability to eat from a spoon were not different between the two groups. These results contribute to the insight that exposure to texture is important for young children to learn how to handle texture. PRACTICAL APPLICATIONS: (a) The study shows the feasibility of testing the effects of texture interventions on chewing capability and oral responses such as gagging, coughing, and choking in infants. (b) The study contributes to the insight that exposure to food texture to learn how to handle texture is important for infants and showed that exposing children to a higher amount of larger pieces improves their chewing capability for a piece of carrot and potato, at least immediately after the intervention.
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Mycelium biocomposites (MBCs) are a fairly new group of materials. MBCs are non-toxic and carbon-neutral cutting-edge circular materials obtained from agricultural residues and fungal mycelium, the vegetative part of fungi. Growing within days without complex processes, they offer versatile and effective solutions for diverse applications thanks to their customizable textures and characteristics achieved through controlled environmental conditions. This project involves a collaboration between MNEXT and First Circular Insulation (FC-I) to tackle challenges in MBC manufacturing, particularly the extended time and energy-intensive nature of the fungal incubation and drying phases. FC-I proposes an innovative deactivation method involving electrical discharges to expedite these processes, currently awaiting patent approval. However, a critical gap in scientific validation prompts the partnership with MNEXT, leveraging their expertise in mycelium research and MBCs. The research project centers on evaluating the efficacy of the innovative mycelium growth deactivation strategy proposed by FC-I. This one-year endeavor permits a thorough investigation, implementation, and validation of potential solutions, specifically targeting issues related to fungal regrowth and the preservation of sustained material properties. The collaboration synergizes academic and industrial expertise, with the dual purpose of achieving immediate project objectives and establishing a foundation for future advancements in mycelium materials.
Within the food industry there is a need to be able to rapidly react to changing regulatory requirements and consumer preferences by adjusting recipes, processes, and products. A good knowledge of the properties of food ingredients is crucial in this process. Currently this knowledge is available in scattered heterogeneous resources such as scientific peer-reviewed articles, databases, recipes, food blogs as well as in the experience of food-experts. This prevents, in practice, the efficient integration and use of this knowledge, leading to inefficiency and missed opportunities. In this project we will build a structured database of properties of food ingredients, focusing in particular on the taste and texture properties. By large-scale collection and text mining on a large number of textual resources, a comprehensive data set on ingredient properties will be created, along with knowledge on the relationships between these ingredients. This database will then be used for to find new potential applications for healthy and taste enhancing ingredient combinations by network-based discovery methods and artificial intelligence algorithms will be used. A concrete focus will be on application questions formulated by the industrial partners. The resulting hypothesis will be validated in a real life setting at the premises of the industrial partners. The deliverables of this project will be: - A reusable open-access ingredient database that is accessible via a user-friendly web portal - A set of state-of-the-art mining algorithms that can address a wide variety of industry driven use cases - Novel product formulations that can be further developed for the consumer and business2business market
In this project we will build a structured database of properties of food ingredients, focusing in particular on the taste and texture properties. By large-scale collection and text mining on a large number of textual resources, a comprehensive data set on ingredient properties will be created, along with knowledge on the relationships between these ingredients. This database will then be used for to find new potential applications for healthy and taste enhancing ingredient combinations by network-based discovery methods and artificial intelligence algorithms will be used. A concrete focus will be on application questions formulated by the industrial partners. The resulting hypothesis will be validated in a real life setting at the premises of the industrial partners.The deliverables of this project will be:• A reusable open-access ingredient database that is accessible via a user-friendly web portal• A set of state-of-the-art mining algorithms that can address a wide variety of industry driven use cases• Novel product formulations that can be further developed for the consumer and business2business market
