Identifying Food Consumption Patterns among Young Consumers by Unsupervised and Supervised Multivariate Data Analysis
Ulf Hammerling, Eva Freyhult, Anna Edberg, Salomon Sand, Sisse Fagt, Vibeke Kildegaard Knudsen, Lene Frost Andersen, Anna Karin Lindroos, Daniel Soeria-Atmadja, Mats G. Gustafsson
European Journal of Nutrition & Food Safety · pp. 392–403 · Published 7 Jul 2014
10.9734/EJNFS/2014/9082Abstract
Although computational multivariate data analysis (MDA) already has been employed in the dietary survey area, the results reported are based mainly on classical exploratory (descriptive) techniques. Therefore, data of a Swedish and a Danish dietary survey on young consumers (4 to 5 years of age) were subjected not only to modern exploratory MDA, but also modern predictive MDA that via supervised learning yielded predictive classification models. The exploratory part, also encompassing Swedish 8 or 11-year old Swedish consumers, included new innovative forms of hierarchical clustering and bi-clustering. This resulted in several interesting multi-dimensional dietary patterns (dietary prototypes), including striking difference between those of the age-matched Danish and Swedish children. The predictive MDA disclosed additional multi-dimensional food consumption relationships. For instance, the consumption patterns associated with each of several key foods like bread, milk, potato and sweetened beverages, were found to differ markedly between the Danish and Swedish consumers. In conclusion, the joint application of modern descriptive and predictive MDA to dietary surveys may enable new levels of diet quality evaluation and perhaps also prototype-based toxicology risk assessment.
References (33)
- 1 Random Forests [DOI]
- 2 The Elements of Statistical Learning: Data Mining, Inference, and Prediction [DOI]
- 3 The Elements of Statistical Learning: Data Mining, Inference, and Prediction [DOI]
- 4 Diagnosis of multiple cancer types by shrunken centroids of gene expression [DOI]
- 5 Empirically Derived Eating Patterns Using Factor or Cluster Analysis: A Review [DOI]
- 6 Random forests for genomic data analysis [DOI]
- 7 Overview of Artificial Neural Networks [DOI]
- 8 Dietary Patterns: Challenges and Opportunities in Dietary Patterns Research [DOI]
- 9 General principles for the collection of national food consumption data in the view of a pan-European dietary survey [DOI]
- 10 Dietary patterns, approaches, and multicultural perspectiveThis is one of a selection of papers published in the CSCN–CSNS 2009 Conference, entitled Can we identify culture-specific healthful dietary patterns among diverse populations undergoing nutrition transition? [DOI]
- 11 Supervised learning with decision tree-based methods in computational and systems biology [DOI]
- 12 Comparing 3 Dietary Pattern Methods--Cluster Analysis, Factor Analysis, and Index Analysis--With Colorectal Cancer Risk: The NIH-AARP Diet and Health Study [DOI]
- 13 Latent class models for classification [DOI]
- 14 A Critical Assessment of Feature Selection Methods for Biomarker Discovery in Clinical Proteomics [DOI]
- 15 Comprehensive Decision Tree Models in Bioinformatics [DOI]
- 16 Analysis of meal patterns with the use of supervised data mining techniques—artificial neural networks and decision trees [DOI]
- 17 Latent Class Analysis of Complex Sample Survey Data [DOI]
- 18 A comparison of dietary patterns derived by cluster and principal components analysis in a UK cohort of children [DOI]
- 19 Latent Class Analysis Is Useful to Classify Pregnant Women into Dietary Patterns1–3 [DOI]
- 20 IgE sensitization to fungi mirrors fungal phylogenetic systematics [DOI]
- 21 Personalized medicine in psoriasis: developing a genomic classifier to predict histological response to Alefacept [DOI]
- 22 Decision tree models for data mining in hit discovery [DOI]
- 23 Dietary patterns among a national sample of British children aged 1½ – 4½; years [DOI]
- 24 Disentangling women's responses on complex dietary intake patterns from an Indian cross-sectional survey: a latent class analysis [DOI]
- 25 Computational detection of allergenic proteins attains a new level of accuracy with in silico variable-length peptide extraction and machine learning [DOI]
- 26 Cluster analysis and food group consumption in a national sample of Australian girls [DOI]
- 27 Quantitative Chemogenomics: Machine-Learning Models of Protein-Ligand Interaction [DOI]
- 28 Latent class analysis of complex sample survey data : Application to dietary data
- 29 Identifying dietary patterns using a normal mixture model: application to the EPIC study [DOI]
- 30 Computational Approaches for Translational Clinical Research in Disease Progression [DOI]
- 31 Interrogating health-related public databases from a food toxicology perspective: Computational analysis of scoring data [DOI]
- 32 R: A Language and Environment for Statistical Computing [DOI]
- 33 Discovery and characterisation of dietary patterns in two Nordic countries [DOI]
Cited by 4
Hujun Xie, Qing-Bo Jiao, Hao Li · Critical reviews in food science and nutrition · 2025
Hui-Peng Liew · Journal of public health · 2021
María Moraga · 2020
Showing 3 of 4 known citations — external sources report more than can currently be individually listed.
Related research
- "Child's Play"; Pestering Power Influencing a Parent's Purchasing Behavior in Sri Lanka — shares topic coverage
- Factors Affecting Adoption of E-commerce among Youngsters in J&K UT: An Empirical Study — shares topic coverage
- Impact of Food Systems Transformation on Dietary Patterns and Public Health in Africa: A Mini Review — shares topic coverage
- Investigations into the Nutritional Status of Older Adults and the Related Lifestyle Risk Factors as well as Comorbidities at Dhaka City, Bangladesh — shares topic coverage
- Unmasking Sarcopenic Obesity in Chronic Hepatitis: A Cross-Sectional Study of Body Composition and Pro-Inflammatory Diets in Lomé, Togo — shares topic coverage
Article metrics
Real usage data collected on this platform.
56
Page views
0
PDF downloads
45
Outbound clicks
4
Citations
Views over time
Views by country
Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".
Traffic sources
Referring site, by host.
Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.