The estimated intake of palm oil in Spain and Germany.

Palm oil is one of the most widely used vegetable oils by the food industry but there is limited information on actual intake. A detailed intake assessment of palm oil for Spain and Germany was conducted. For Spain, mean palm oil intakes ranged from 2.06 g/day in the elderly to 4.54 g/day in children and adolescents, while high-level intakes ranged from 10.34 g/day in the elderly to 20.88 g/day in toddlers. For Germany, mean palm oil intakes ranged from 3.06 g/day in toddlers to 6.22 g/day in the very elderly, while high-level intakes ranged from 13.61 g/day in toddlers to 30.10 g/day in the elderly. For both countries, the main contributing food categories to mean palm oil intake were biscuits, cakes, bread, breakfast cereal and margarine. In summary, the present paper provides a realistic assessment of the intake of palm oil in two EU countries for different population groups.

Tiered intake assessment for low- and no-calorie sweeteners in beverages.

A tiered intake assessment approach is presented and applied to derive the maximum potential global low- and no-calorie sweeteners (LNCS) intake estimates. The US and Uk markets served as representative for the world and the EU region, respectively, to determine the maximum potential exposure for acesulfame potassium (AceK), aspartame, saccharin, steviol glycosides, and sucralose in various subpopulations, including brand-loyal consumers. Conservative intake estimates for LNCS used in non-alcoholic beverages were calculated for the general population 2 + y, toddlers (12-35 months (US) or 18-35 months (UK)), young children 3-9 y, adolescents 10-17 y, adults 18-64 y, elderly 65-74 y, and very elderly 75 + y based on assumed uses in high beverage consumption markets, leveraging either the 2-day food consumption data from the 2013-2016 US National Health and Nutrition Examination Survey or the 4-day food consumption data from the 2008-2017 UK National Diet and Nutrition Survey Rolling Programme. Strong concordance between the refined budget method and the brand-loyal deterministic approach was shown, the latter assumes the maximum industry-reported global LNCS use level is present in 100% of non-alcoholic beverages. This study shows that safety of LNCS in beverages at proposed use levels can be supported for any geography, with all intake estimates falling below the acceptable daily intake in refined assessments. Importantly, this study shows the refined budget method to be a valid first-tier screening assessment in prioritising those LNCS that may benefit from more refined intake assessments when warranted.

Tiered intake assessment for food colours.

A tiered intake assessment approach, ranging from the conservative default and refined budget method to refined dietary exposure assessments using national food consumption surveys is presented and applied to derive maximum potential global colour intake estimates. The US and UK markets served as representative for the world and the EU, respectively, to determine the maximum potential exposure ceilings for eleven colours in various sub-populations, including brand-loyal consumers. Industry-reported global use levels were assigned as the maximum level. Conservative intake assessments for food colours used in non-alcoholic beverages were estimated for the general population 2 + y, toddlers, children 3-9 y, adolescents 10-17 y, adults 18-64 y, elderly 65-74 y, very elderly 75 + y based on assumed uses in high intake markets. Refined dietary exposures were estimated using either the 2-day food consumption data from the 2013-2016 US National Health and Nutrition Examination Survey or the 4-day food consumption data from the 2008-2016 U.K. National Diet and Nutrition Survey Rolling Programme. In the most refined market-share adjusted assessment, brand-specific market volume data were used to place appropriate weight on corresponding beverage type uses. Strong concordance between the refined budget method and the brand-loyal deterministic approach was shown, in which the latter assumes that the maximum use level of the colour is present in 100% of non-alcoholic beverages. This study shows that safety of colours - both synthetic and natural - in beverages at proposed use levels can be supported for any geography, with all intake estimates falling below the acceptable daily intake in refined assessments. Importantly, this study demonstrates that the refined budget method is a valid first-tier screening assessment to prioritise food colours that may benefit from more refined intake assessments when warranted.

A harmonised approach for identifying core foods for total diet studies.

Total diet studies (TDS) are used to gather information on chemical substances in food, thereby facilitating risk assessments and health monitoring. Candidate foods for inclusion in a TDS should represent a large part of a typical diet to estimate accurately the exposure of a population and/or specific population groups. There are currently no harmonised guidelines for the selection of foods in a TDS, and so the aim of this study was to explore the possibility of generating a harmonised approach to be used across Europe. Summary statistics data from the European Food Safety Authority's (EFSA) Comprehensive Food Consumption Database were used in this research, which provided data from national food consumption surveys in Europe. The chosen methodology for the selection of foods was based on the weight of food consumed and consumer rate. Using the available data, 59 TDS food lists were created, representing over 51 000 people across 17 countries and seven population groups. All TDS food lists represented > 85% of the populations' diets (85.9-96.3%), while the number of foods in the TDS food lists ranged from 15 to 102. Comparison of the TDS food lists indicated that the most commonly consumed foods included wheat bread and rolls, pastries and cakes, tomatoes, apples, bananas, and chicken, while cow's milk, tap water and orange juice were the most commonly consumed beverages across Europe. This work was complete to support EFSA and other institutions in the development of harmonised TDS into the future.

Identifying core foods for total diet studies: a comparison of four different approaches.

Total diet studies (TDS) are recognised as a cost-effective approach in estimating dietary exposure to chemicals in food. It has been advised that candidate foods for inclusion in TDS analysis should represent a large part of the typical diet to estimate accurately the exposure of a population group. To date a variety of approaches have been used to determine which foods should be included in a core TDS food list, with no agreed method. Therefore, the aim of this study was to compare four of these approaches by creating TDS food lists for adult populations in Europe using summary statistics data from the EFSA Comprehensive Food Consumption Database. Both a food group approach and a total diet approach were employed, and foods were selected for inclusion in the TDS food lists if they met the criteria as defined by consumption weight and/or a 5% consumer rate. Using all four approaches the representation of the diet across the TDS food lists was > 85%. The food group approach showed a slight advantage in diet representation, but produced considerably longer TDS food lists in comparison with the total diet approach. The addition of a 5% consumer rate to both approaches had little impact on results. In conclusion, the total diet approach may act as a more cost-effective approach in comparison with the food group approach while still achieving comprehensive results in the creation of core TDS food lists.

Dietary patterns in Irish adolescents: a comparison of cluster and principal component analyses.

OBJECTIVE: Pattern analysis of adolescent diets may provide an important basis for nutritional health promotion. The aims of the present study were to examine and compare dietary patterns in adolescents using cluster analysis and principal component analysis (PCA) and to examine the impact of the format of the dietary variables on the solutions. DESIGN: Analysis was based on the Irish National Teens Food Survey, in which food intake data were collected using a semi-quantitative 7 d food diary. Thirty-two food groups were created and were expressed as either g/d or percentage contribution to total energy. Dietary patterns were identified using cluster analysis (k-means) and PCA. SETTING: Republic of Ireland, 2005-2006. SUBJECTS: A representative sample of 441 adolescents aged 13-17 years. RESULTS: Five clusters based on percentage contribution to total energy were identified, 'Healthy', 'Unhealthy', 'Rice/Pasta dishes', 'Sandwich' and 'Breakfast cereal & Main meal-type foods'. Four principal components based on g/d were identified which explained 28 % of total variance: 'Healthy foods', 'Traditional foods', 'Sandwich foods' and 'Unhealthy foods'. CONCLUSIONS: A 'Sandwich' and an 'Unhealthy' pattern are the main dietary patterns in this sample. Patterns derived from either cluster analysis or PCA were comparable, although it appears that cluster analysis also identifies dietary patterns not identified through PCA, such as a 'Breakfast cereal & Main meal-type foods' pattern. Consideration of the format of the dietary variable is important as it can directly impact on the patterns obtained for both cluster analysis and PCA.

Comparison of cluster and principal component analysis techniques to derive dietary patterns in Irish adults.

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 the k-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.

Intake estimates of naturally occurring phytosterols using deterministic and probabilistic methods in a representative Irish population.

Intakes of naturally occurring phytosterols were estimated in the Irish population using phytosterol concentration data obtained from the published literature. Both deterministic and probabilistic approaches were used. Concentration data were applied to dietary intake data as assessed in the North/South Ireland Food Consumption Survey (n = 1,379). With the probabilistic approach, a uniform distribution was used in place of fixed values to reflect variability and uncertainty in phytosterol concentrations. Foods consumed were also categorized into 19 food groups, and the phytosterol intake per food group was calculated. Using the deterministic approach, mean phytosterol intake for the total population was 254 mg/day (28.1 mg/1,000 kJ). Using the probabilistic approach, slightly higher mean phytosterol intakes of 287 mg/day were found. In terms of food groups, 'bread and bread-rolls', 'vegetables and vegetable dishes' and 'spreading fat and oils' were the top three contributing food groups to phytosterol intakes. This study has shown how both deterministic and probabilistic approaches may be used to determine phytosterol intakes in a population.

Analysis of meal patterns with the use of supervised data mining techniques--artificial neural networks and decision trees.

BACKGROUND: At present, the analysis of dietary patterns is based on the intake of individual foods. This article demonstrates how a coding system at the meal level might be analyzed by using data mining techniques. OBJECTIVE: The objective was to evaluate the usability of supervised data mining methods to predict an aspect of dietary quality based on dietary intake with a food-based coding system and a novel meal-based coding system. DESIGN: Food consumption databases from the North-South Ireland Food Consumption Survey 1997-1999 were used. This was a randomized cross-sectional study of 7-d recorded food and nutrient intakes of a representative sample of 1379 Irish adults. Meal definitions were recorded by the respondent. A healthy eating index (HEI) score was developed. Artificial neural networks (ANNs) and decision trees were used to predict quintiles of the HEI based on combinations of foods consumed at breakfast and main meals. RESULTS: This study applied both data mining techniques to the food and meal-based coding systems. The ANN had a slightly higher accuracy than did the decision tree in relation to its ability to predict HEI quintiles 1 and 5 based on the food coding system (78.7% compared with 76.9% and 71.9% compared with 70.1%, respectively). However, the decision tree had higher accuracies than did the ANN on the basis of the meal coding system (67.5% compared with 54.6% and 75.1% compared with 72.4%, respectively). CONCLUSIONS: 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.