Nutrition Intake Modelling

Nutrition intake modelling uses data science to inform food formulation, new product development, public health strategies and personalised nutrition.

Nutrition intake modelling allows us to understand population food, energy and nutrient intakes. Monitoring these intakes forms the basis of food nutrition and public health policies and to assess patterns and trends in a population.

Nutrition intake modelling uses data science to inform food formulation, new product development, public health strategies and personalised nutrition.

Nutrient intakes are calculated using food consumption data (which provides data on the amount and frequency of food/ beverage consumed) and data on nutrient concentration (generally from food composition databases). The concentration data can either be provided in the form of point estimates or distribution of values. Such data can come directly from the survey, or other databases.

The dietary intake of a nutrient of interest is calculated in an iterative way over all ā€œconsumption eventsā€. The calculation of intake from a single product is governed by a single equation given below.

Problem areas

  • Assess current nutritional intakes in targeted markets and populations
    • Understand who is under, meeting or exceeding requirements
    • Examine the foods which contribute to nutrient intakes
    • Intake distribution to understand low and high consumers
    • Customise foods of interest with flexible food grouping
    • Our bespoke analysis can determine intakes per age, sex, health status to identify the consumers at lowest or highest risk
  • Analyse targeted nutrition strategies and their real-world impacts from a Public Health Nutrition perspective
  • Identify optimal product formulations and predict the impact on targeted global markets and populations via intake scenario analysis. 
  • Model the potential effects of adding a new product on a populations intake and health
    • Overcome data gaps with probabilistic intake modelling
  • Incorporate in-house data or data from other sources 
    • Brand specific composition
    • Portion size
    • Novel ingredients
    • Additives
    • Sales and market share
  • Work with companies to assess health outcomes based on intervention studies carried out with Creme Global models
  • Use food consumption surveys from the US, Europe, China, Korea, Brazil, Mexico and many more.


Creme Global provides readily available software such as Creme NutritionĀ®, Creme Food SafetyĀ® and Creme CareĀ®. These applications come with pre-installed and validated data sets, user friendly interfaces, and are deployed using our patented high performance cloud computing technology. Our licences include full scientific and technical support.

Custom Designed Models

With our in-house expertise in software development, mathematical modelling and domain knowledge in food science and safety, nutrition and toxicology, Creme Global is uniquely placed to help clients obtain the best solution in developing custom models for them.

Technical Services

As well as providing unique software applications, our team provides technical services to clients using our in-house expertise. These services include in depth data analysis and modelling followed by routine or customised exposure and intake assessments delivered via comprehensive technical reports.

Download Case Study: ODIN

ODIN aims to provide high quality scientific evidence to prevent vitamin D deficiency in European citizens and improve nutrition and public health through food.
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It was a great pleasure to work with Creme Global on Recipe Management System (RMS) including calculation models and product comparisons for Bayn’s products. The team is highly skilled and professional and turned all our ideas into reality for quite a short period of time.

Srdjan Solaja, BAYN

Creme have built this salt model for FSAI and trained the Authority’s staff in its use so that further analysis can be done by FSAI in the future. This will allow FSAI to request salt reductions in foods with confidence that there will be a subsequent improvement in salt intake. This model allows for the efficient and accurate processing of salt reduction data so that FSAI can focus resources in those areas that have the greatest impact on public health.

Salt Reduction Project Manager, FSAI

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