MétaCan
Menu
← Back to cohort
Record W4293842101

A sensory perspective on the development of food likes in children: implication for food intake

2014· preprint· en· W4293842101 on OpenAlexaboutno aff
Sophie Nicklaus

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2014
Typepreprint
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
Fundersnot available
KeywordsPerspective (graphical)Sensory systemComputer scienceFood intakeFood sciencePsychologyCognitive psychologyArtificial intelligenceMedicineChemistry
DOInot available

Abstract

fetched live from OpenAlex

All aspects of eating habits (i.e. ‘how', ‘what', ‘when', and ‘how much' to eat) are learned, essentially during the first years of life (1). Moreover eating habits established in the early years will contribute to the development of subsequent eating habits (2-4). At this stage, nutrition is still critical for child development, so parental choices bear a key importance for future health. It therefore appears essential to understand the most important periods for the acquisition of healthy eating habits, as well as the most important factors driving eating habits. Since eating behaviour is complex and multi-determined, these factors may be multiple. Because in our sophisticated, superabundant food environment, a wide range of foods are available, it is particularly relevant to understand how the properties of foods may impact how they are liked and consumed by children. In this presentation, we will specifically focus on two sensory aspects of foods that may impact how they are liked and consumed, taste and flavour. Learning before complementary feedingEarly events may contribute to shape eating behaviour (5). Flavour learning may happen during pregnancy and lactation (6), through the exposure of the infant to flavours of the foods from the mother's diet (7). Taste exposure during milk feeding may also lead to different preferences in children. For instance, longer breastfeeding is associated with a higher acceptance of the umami taste (taste of glutamate) at the age of 6 months (8); which is interpreted in relation with the higher glutamate content of breast milk compared to formula milk. Learning after the onset of complementary feedingBeyond this stage of flavour discoveries, the most important phase for learning to eat is likely to be the transition from milk feeding to a diversified diet, i.e. the beginning of complementary feeding. At this moment, infants start to discover the sensory (texture, taste and flavour) and nutritional properties (energy density) of the foods that will ultimately compose their adult diet. This presentation will highlight some factors that favour the development of food acceptance at the beginning of complementary feeding. In particular, the influence of complementary feeding practices such as repeated exposure (9, 10), introducing a variety of foods (11), and of food sensory properties (12, 13) on the acceptance of new foods by infants will be outlined (14). Implication for food intakeIf the sensory properties can influence how a food is acceptance at the onset of complementary feeding, one may wonder whether it may also determine how much a food is eaten. In toddlers, adding sugar or fat to a food does not lead to an increase in the amount eaten in an ad libitum situation (15). However, adding salt is associated to a higher intake, in toddlers (15) and in school children (16). Optimal food formulation should be thought about in order to trigger pleasure during consumption; without threatening children's health. There is a need for further studies toward a better understanding of how children learn to like foods and how much to eat of them (17). References1. Schwartz C, Scholtens P, Lalanne A, Weenen H, Nicklaus S. Development of healthy eating habits early in life: review of recent evidence and selected guidelines. Appetite. 2011;57(3):796-807.2. Nicklaus S, Boggio V, Chabanet C, Issanchou S. A prospective study of food preferences in childhood. Food Quality and Preference. 2004 2004/0;15(7-8):805-18.3. Nicklaus S, Boggio V, Chabanet C, Issanchou S. A prospective study of food variety seeking in childhood, adolescence and early adult life. Appetite. 2005;44(3):289-97.4. Nicklaus S, Remy E. Early origins of overeating: Tracking between early food habits and later eating patterns. Current Obesity Reports. 2013 2013/03/07;2(2):179-84.5. Migraine A, Nicklaus S, Parnet P, Lange C, Monnery-Patris S, Des Robert C, et al. Effect of preterm birth and birth weight on eating behavior at 2 y of age. American Journal of Clinical Nutrition. 2013 June 2013;97(6):1270-7.6. Mennella JA, Jagnow CP, Beauchamp GK. Prenatal and postnatal flavor learning by human infants. Pediatrics. 2001;107(6):e88.7. Hausner H, Nicklaus S, Issanchou S, Mølgaard C, Møller P. Breastfeeding facilitates acceptance of a novel dietary flavour compound. Clinical nutrition. 2010;29(1):141-8.8. Schwartz C, Chabanet C, Laval C, Issanchou S, Nicklaus S. Breastfeeding duration: influence on taste acceptance over the first year of life. British Journal of Nutrition. 2013;109(6):1154-61.9. Remy E, Issanchou S, Chabanet C, Nicklaus S. Repeated exposure of infants at complementary feeding to a vegetable puree increases acceptance as effectively as flavor-flavor learning and more effectively than flavor-nutrient learning. Journal of Nutrition. 2013 May 22;143(7):1194-200.10. Caton SJ, Blundell P, Ahern SM, Nekitsing C, Olsen A, Møller P, et al. Learning to eat vegetables in early life: the role of timing, age and individual eating traits. PloS one. 2014;9(5):e97609.11. Mennella JA, Nicklaus S, Jagolino AL, Yourshaw LM. Variety is the spice of life: Strategies for promoting fruit and vegetable acceptance during infancy. Physiology

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.261
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueHAL (Le Centre pour la Communication Scientifique Directe)→Same topicObesity, Physical Activity, Diet→French-language works237,207→