There is an emergent need to improve the existing food databases by updating food data and to develop suitable country-specific databases for those that do not have their own food composition table.

Worldwide population growth expectations increase the pressure to find ways to increase agro-food production in a sustainable manner, be it by increasing production capacity, cultivating currently natural areas or changing crop consumption patterns. Cereals are staple foods and the most frequently consumed are maize, wheat and rice. However, the production of these cereals may become insufficient for the growing needs. Millet and sorghum are traditional cereal crops, able to growth in adverse drought conditions, and do not need many resources to be harvested. Furthermore, they reveal very interesting nutritional profiles, including macro and micronutrients and bioactive compounds. We would like to highlight their fiber content, since fiber inadequacy is a worldwide condition and its adverse effects on health are well described.Over time, these indigenous cultures have become less popular than other cereals, due to their organoleptic and technological proprieties. However, techniques like bio-fortification, fortification, genetic improvement, fermentation, malting, and germination among others can be used to improve their technological properties and enhance their nutritional profile.

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Objective: To develop a semi-quantitative FFQ and to evaluate its validity and reproducibility for the assessment of total dietary intake of Kenyan urban adult population, given its non-existence in Kenya. Design: The current study adopted a cross-sectional design. A culture-sensitive semi-quantitative FFQ was developed and its validity was tested relative to three non-consecutive 24-h recalls (24hR). Reproducibility was tested by the test–retest method, with a 3-week interval. Spearman’s correlation coefficients and intra-class correlation coefficients were calculated for several macro- and micronutrients. Cross-classification into quartiles and Bland and Altman plots were analysed. Setting: Nairobi county (Dagoreti South and Starehe constituencies). Participants: A convenient sample was recruited in three different clusters in Nairobi. Results: A culture-sensitive 123-food-item semi-quantitative FFQ showed higher nutrient intakes compared with the 24hR (total energy median 12543·632 v. 8501·888 kJ, P < 0·001). Energy-adjusted and deattenuated Spearman’s correlations for macronutrients ranged between 0·21 (total fat) and 0·47 (protein). The agreement in the same quartile varied from 28 % (protein) to 41 % (carbohydrates). Including adjacent quartiles, the range increased: 76 % (protein and fat) to 81 % (carbohydrates). The extreme disagreement was low. The first FFQ application resulted in higher mean values for all nutrients compared with the second FFQ (total energy median 12459·952 v. 10485·104 kJ, P < 0·001). Energy-adjusted correlations for macronutrients ranged from 0·28 (carbohydrates) to 0·61 (protein). Intra-class correlation coefficients for macronutrients were moderate, between 0·6 and 0·7. Conclusions: The developed semi-quantitative FFQ was shown to be a valid and reproducible tool for ranking urban adult Kenyans according to their dietary intake.

Urbanisation is hastening the transition from traditional food habits to less healthy diets, which are becoming more common among Kenyans. No up‐to‐date studies on usual dietary intake and the main food sources of adult Kenyans are available. The aim of the present study was to identify the main food sources of nutrients in the diet of urban adult Kenyans and explore potential associations with demographic variables including age, sex, level of education, occupation and body mass index. The study adopted a cross‐sectional design. The dietary intake of 486 adult Kenyans from Nairobi was assessed using a validated, culture‐sensitive, semi‐quantitative food frequency questionnaire. Binary logistic regression models were used to evaluate associations between food sources and demographic variables. Macronutrient intakes as a proportion of total energy intake (TEI) were within international dietary guidelines. Cereals and grain products (34.0%), sugar, syrups, sweets and snacks (9.8%), fruits (9.7%) and meat and eggs (8.8%) were the major contributors to TEI. Cereals and grain products contributed 42.5% to carbohydrates, followed by fruits (12.4%) and sugar, syrups, sweets and snacks (10.6%). The most important sources of protein and total fat were cereals and grain products (23.3% and 19.7%, respectively) and meat and eggs (22.0% and 18.7%, respectively). Sex, age and level of education were associated with the choice of food groups. Although macronutrient intakes were within guidelines, the Kenyan diet was revealed to be high in sugars, salt and fibre, with differences in food sources according to demographic variables. These results can act as an incentive to national authorities to implement nutritional strategies aiming to raise awareness of healthier dietary patterns among Kenyans.