Dietary trends and changing lifestyle patterns have been associated with the increasing occurrence of obesity in the Western world. These dietary trends are commonly studied using longitudinal food consumption surveys. An alternative to studying changes in eating behaviour may be found in recipe analysis of traditional cookbooks. Few such studies exist, however, and it is unclear whether dietary trends over time can be identified this way. The present paper analyses full-meal recipes from a traditional Dutch cookbook between 1950 and 2010. The selected recipes show an increase in energy density. Additionally, the protein weight per kcal increased. In general, the observed trends are similar to those identified by the Dutch National Food Consumption Survey. The analysis therefore suggests that traditional cookbooks can be used as an indicator to identify dietary trends over time, although further studies are necessary to support this hypothesis.

Taste is a key driver of food choice and intake. Taste preferences are widely studied, unlike the diet’s taste profile. This study assessed dietary taste patterns in the Netherlands by sex, BMI, age and education. A taste database, containing 476 foods’ taste values, was combined with 2-d 24-h recalls in two study populations. The percentage of energy intake from six taste clusters was assessed in the Dutch National Food Consumption Survey (DNFCS 2007–2010; n 1351) and in an independent observational study: the Nutrition Questionnaires plus (NQplus) study (2011–2013; n 944). Dietary taste patterns were similar across study populations. Men consumed relatively more energy from ‘salt, umami and fat’ (DNFCS; 24 % energy, NQplus study; 23 %)- and ‘bitter’ (7 %)-tasting foods compared with women (21 %, P<0·001, 22 %, P=0·005; 3 %, P<0·001, 4 %, P<0·001, respectively). Women consumed more % energy from ‘sweet and fat’ (15 %)- and ‘sweet and sour’ (13 %, 12 %, respectively)-tasting foods compared with men (12 %, P<0·001, 13 %, P=0·001; 10 %, P<0·001). Obese individuals consumed more % energy from ‘salt, umami and fat’- and less from ‘sweet and fat’-tasting foods than normal-weight individuals (‘salt, umami and fat’, men; obese both studies 26 %, normal-weight DNFCS 23 %, P=0·037, NQplus 22 %, P=0·001, women; obese 23 %, 24 %, normal weight 20 %, P=0·004, P=0·011, respectively, ‘sweet and fat’, men; obese 11 %, 10 %, normal weight 13 %, P<0·05, 14 %, P<0·01, women; obese 14 %, 15 %, normal weight 16 %, P=0·12, P=0·99). In conclusion, our taste database can be used to deepen our understanding of the role of taste in dietary intake in the Netherlands by sex, BMI, age and education.

The aim of the present paper is to summarise current and future applications of dietary assessment technologies in nutrition surveys in developed countries. It includes the discussion of key points and highlights of subsequent developments from a panel discussion to address strengths and weaknesses of traditional dietary assessment methods (food records, FFQ, 24 h recalls, diet history with interviewer-assisted data collection) v. new technology-based dietary assessment methods (web-based and mobile device applications). The panel discussion ‘Traditional methods v. new technologies: dilemmas for dietary assessment in population surveys’, was held at the 9th International Conference on Diet and Activity Methods (ICDAM9), Brisbane, September 2015. Despite respondent and researcher burden, traditional methods have been most commonly used in nutrition surveys. However, dietary assessment technologies offer potential advantages including faster data processing and better data quality. This is a fast-moving field and there is evidence of increasing demand for the use of new technologies amongst the general public and researchers. There is a need for research and investment to support efforts being made to facilitate the inclusion of new technologies for rapid, accurate and representative data.

Sufficient I intake is important for the synthesis of thyroid hormones, which play an important role in normal growth and development. Our aim was to estimate habitual I intake for the Dutch population and the risk of inadequate or excessive intakes. Further, we aimed to provide an insight into the dietary sources of I and the association with socio-demographic factors. Data from the Dutch National Food Consumption Survey 2007–2010 (n 3819; 7–69 years), and from the Dutch food and supplement composition tables were used to estimate habitual I intake with a calculation model. Contribution of food groups to I intake were computed and multiple linear regression was used to examine associations of intakes with socio-demographic factors. A total of ≤2 % of the population had an intake below the estimated average requirement or above the upper level. The main sources of I were bread containing iodised salt (39 %), dairy products (14 %) and non-alcoholic drinks (6 %). I intake (natural sources only, excluding iodised salt and supplements) was positively associated with (parental) education, which could at least partly be attributed to a higher consumption of dairy products. Among children, the consumption of bread, often containing iodised bakery salt, was positively associated with parental education. The I intake of the Dutch population (7–69 years) seems adequate, although it has decreased since the period before 2008. With the current effort to reduce salt intake and changing dietary patterns (i.e. less bread, more organic foods) it is important to keep a close track on the I status, important sources and potential risk groups.

Generally, there is a need for short questionnaires to estimate diet quality in the Netherlands. We developed a thirty-four-item FFQ – the Dutch Healthy Diet FFQ (DHD-FFQ) – to estimate adherence to the most recent Dutch guidelines for a healthy diet of 2006 using the DHD-index. The objectives of the present study were to evaluate the DHD-index derived from the DHD-FFQ by comparing it with the index based on a reference method and to examine associations with participant characteristics, nutrient intakes and levels of cardiometabolic risk factors. Data of 1235 Dutch men and women, aged between 20 and 70 years, participating in the Nutrition Questionnaires plus study were used. The DHD-index was calculated from the DHD-FFQ and from a reference method consisting of a 180-item FFQ combined with a 24-h urinary Na excretion value. Ranking was studied using Spearman’s correlations, and absolute agreement was studied using a Bland–Altman plot. Nutrient intakes derived from the 180-item FFQ were studied according to quintiles of the DHD-index using DHD-FFQ data. The correlation between the DHD-index derived from the DHD-FFQ and the reference method was 0·56 (95 % CI 0·52, 0·60). The Bland–Altman plot showed a small mean overestimation of the DHD-index derived from the DHD-FFQ compared with the reference method. The DHD-index score was in the favourable direction associated with most macronutrient and micronutrient intakes when adjusted for energy intake. No associations between the DHD-index score and cardiometabolic risk factors were observed. In conclusion, the DHD-index derived from the DHD-FFQ was considered acceptable in ranking but relatively poor in individual assessment of diet quality.

Nutrient-rich food (NRF) index scores are dietary quality indices based on nutrient density. We studied the design aspects involved in the development and validation of NRF index scores, using the Dutch consumption data and guidelines as an example. We evaluated fifteen NRF index scores against the Dutch Healthy Diet Index (DHD-index), a measure of adherence to the Dutch dietary guidelines, and against energy density. The study population included 2106 adults from the Dutch National Food Consumption Survey 2007–2010. The index scores were composed of beneficial nutrients (protein, fibre, fatty acids, vitamins, minerals), nutrients to limit (saturated fat, sugar, Na) or a combination. Moreover, the influence of methodological decisions was studied, such as the choice of calculation basis (100 g or 100 kcal (418 kJ)). No large differences existed in the prediction of the DHD-index by the fifteen NRF index scores. The score that best predicted the DHD-index included nine beneficial nutrients and three nutrients to limit on a 100-kcal basis, the NRF9.3 with a model R2 of 0·34. The scores were quite robust with respect to sex, BMI and differences in calculation methods. The NRF index scores were correlated with energy density, but nutrient density better predicted the DHD-index than energy density. Consumption of vegetables, cereals and cereal products, and dairy products contributed most to the individual NRF9.3 scores. In conclusion, many methodological considerations underlie the development and evaluation of nutrient density models. These decisions may depend upon the purpose of the model, but should always be based upon scientific, objective and transparent criteria.