New approach for the assessment of cluster diets.

Dietary risk assessment is a major public health concern, positioned in the context of establishing overall food safety policy. It requires some understanding of population food choices although geographical location and social-cultural environment are variable. Several years ago, a cluster analysis based on FAO consumption data, ranging from 1990 to 1994, was at the origin of the 13, so called, GEMS/Food cluster diets. This analysis required the initial identification of 19 food markers based on geographical and cultural differences. This paper proposes a new modelling of FAO food consumption database in order to define new cluster diets based on updated consumption data from 2002 to 2007 and better adapted statistical methods. Two statistical methods were combined to extract, consumption systems that generate a substructure from the initial food consumption database and then by deriving a clustering of countries according to their consumption system profiles. The clustering resulted in 17 cluster diets composed of 2 up to 30 countries. The few discrepancies between these new clusters and former ones may be due to more recent data, and to the fact that the new approach is based on another mathematical modelling which does not require any initial identification of food markers.

[1]  Alexey Ozerov,et al.  Multichannel Nonnegative Matrix Factorization in Convolutive Mixtures for Audio Source Separation , 2010, IEEE Transactions on Audio, Speech, and Language Processing.

[2]  D. James,et al.  Cluster analysis defines distinct dietary patterns for African-American men and women. , 2006, Journal of the American Dietetic Association.

[3]  Robert Tibshirani,et al.  A Framework for Feature Selection in Clustering , 2010, Journal of the American Statistical Association.

[4]  Mark D. Plumbley,et al.  Theorems on Positive Data: On the Uniqueness of NMF , 2008, Comput. Intell. Neurosci..

[5]  H. Sebastian Seung,et al.  Algorithms for Non-negative Matrix Factorization , 2000, NIPS.

[6]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[7]  Max Feinberg,et al.  Extraction of Food Consumption Systems by Nonnegative Matrix Factorization (NMF) for the Assessment of Food Choices , 2011, Biometrics.

[8]  Frank B. Hu,et al.  Dietary pattern analysis: a new direction in nutritional epidemiology , 2002, Current opinion in lipidology.

[9]  Sylvain Arlot,et al.  A survey of cross-validation procedures for model selection , 2009, 0907.4728.

[10]  Raymond J Carroll,et al.  Modeling Data with Excess Zeros and Measurement Error: Application to Evaluating Relationships between Episodically Consumed Foods and Health Outcomes , 2009, Biometrics.

[11]  M Marmot,et al.  Dietary patterns among a national random sample of British adults , 2001, Journal of epidemiology and community health.

[12]  Robert Tibshirani,et al.  Estimating the number of clusters in a data set via the gap statistic , 2000 .

[13]  Katherine L Tucker,et al.  Empirically derived eating patterns using factor or cluster analysis: a review. , 2004, Nutrition reviews.

[14]  Xin Liu,et al.  Document clustering based on non-negative matrix factorization , 2003, SIGIR.

[15]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[16]  K. D. Ikramov.  Sparse matrices , 2020, Krylov Subspace Methods with Application in Incompressible Fluid Flow Solvers.

[17]  Victoria Stodden,et al.  When Does Non-Negative Matrix Factorization Give a Correct Decomposition into Parts? , 2003, NIPS.

[18]  Pascale Barberger-Gateau,et al.  Dietary patterns derived by hybrid clustering method in older people: association with cognition, mood, and self-rated health. , 2008, Journal of the American Dietetic Association.