In 2009 competent organisations in the European Union provided the European Food Safety Authority (EFSA) with data from the most recent national dietary survey at the level of individuals' consumption. Twenty different Member States provided EFSA with data from 22 different national dietary surveys, with consumption figures for adults and, when available, for children. Member States' dietary data were assembled into the EFSA Comprehensive European Food Consumption Database. In this paper an overview of the methodologies and protocols employed in the different national dietary surveys is provided. Specifically, details about dietary assessment methods, interview administration, sampling design, portion size estimation, dietary software, evaluation of under-reporting and non-dietary information collected are described. This information is crucial to evaluate the level of accuracy of food consumption data and to anticipate and acknowledge the utmost important sources of heterogeneity of national databases included in the Comprehensive Database. The Comprehensive Database constitutes a unique resource for the estimation of consumption figures across the European Union and represents a useful tool to assess dietary exposure to hazardous substances and nutrient intake in Europe. Nevertheless, the many substantial methodological differences that characterise the Comprehensive Database are acknowledged and critically discussed.

The aims of the present study were to examine and compare dietary patterns in adults using cluster and factor analyses and to examine the format of the dietary variables on the pattern solutions (i.e. expressed as grams/day (g/d) of each food group or as the percentage contribution to total energy intake). Food intake data were derived from the North/South Ireland Food Consumption Survey 1997–9, which was a randomised cross-sectional study of 7 d recorded food and nutrient intakes of a representative sample of 1379 Irish adults aged 18–64 years. Cluster analysis was performed using thek-means algorithm and principal component analysis (PCA) was used to extract dietary factors. Food data were reduced to thirty-three food groups. For cluster analysis, the most suitable format of the food-group variable was found to be the percentage contribution to energy intake, which produced six clusters: ‘Traditional Irish’; ‘Continental’; ‘Unhealthy foods’; ‘Light-meal foods & low-fat milk’; ‘Healthy foods’; ‘Wholemeal bread & desserts’. For PCA, food groups in the format of g/d were found to be the most suitable format, and this revealed four dietary patterns: ‘Unhealthy foods & high alcohol’; ‘Traditional Irish’; ‘Healthy foods’; ‘Sweet convenience foods & low alcohol’. In summary, cluster and PCA identified similar dietary patterns when presented with the same dataset. However, the two dietary pattern methods required a different format of the food-group variable, and the most appropriate format of the input variable should be considered in future studies.

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Serum albumin concentration on the first postoperative day is a better predictor of surgical outcome than many other preoperative risk factors. It is a low cost test that may be used as a prognostic tool to detect the risk of adverse surgical outcomes.

ANNs and decision trees were successfully used to predict an aspect of dietary quality. However, further exploration of the use of ANNs and decision trees in dietary pattern analysis is warranted.