BACKGROUND

The endpoint of the food system is the consumption of that food by humans to sustain human health. To determine the effect of diet on human health, nutrition scientists use controlled feeding studies, which are prohibitively expensive. An alternative approach is to use large observational cohorts with self-reported dietary intake, but current methods of dietary intake assessment are extremely burdensome for human participants and contain substantial biases and errors. New AI-assisted methods that enable participants to capture diet in realtime are needed to reduce these biases. Further, the glycan content of food—those portions of the diet that are accessible by microbes—is not yet known, hindering the ability to predict the effects of foods on the gut microbiome. We propose to develop benchmark data and food image/text processing towards the prediction of the glycan content in images of mixed meals from real-world food diaries.

GOALS

• Test off-the-shelf computer vision algorithms on food images for single core foods
• Generate benchmark data set of real-world mixed meal photos paired with food records
• Engage with SCIO to pilot test whether NIR can distinguish a pair of visually similar foods
• Develop glycan library for core foods
• Build library of images for single core foods being analyzed for glycan content
• Determine optimal set of mixed meals to analyze for glycan content

IMPACT

• Improve upon the antiquated methods used by nutritional researchers to assess diet because validated continuous real-time dietary assessment does not yet exist
• Provide information on the glycans in foods that are primary carbon sources for our gut microbes. Solving this problem will radically alter nutrition research and accelerate our progress towards determining what people should eat to nourish the right gut microbes.