A Wolfram U Daily Study Group on computational food and nutrition begins on February 23, 2026.
Join me and fellow food and nutrition enthusiasts to learn how to compute, analyze and visualize data from Wolfram Language’s built-in knowledgebase of foods. Our topics for the study group include easy-to-use nutrition data retrieval and analysis tools, nutrient comparison plots and visualizations, statistical analysis of nutrition data, recipe management with LLMs, and food chemistry and physics with curated data and built-in formulas.
No prior Wolfram Language experience is required.
Please feel free to use this thread to collaborate and share ideas, materials and links to other resources with fellow learners.
Dates
February 23-27, 2026 (Monday through Friday), 11am-12pm CT (5-6pm GMT)
Thanks so much to everyone who attended today’s Daily Study Group! I had a great time and look forward to our discussion tomorrow, including informative and creative ways to visualize nutrition data.
Thank you for today’s suggestion to add vitamin E to the Nutrition Facts label. It’s a good idea since vitamins A, C and E are often discussed together, as all three are antioxidants. Vitamins A and C are already included on the label. We will discuss at next food development meeting. In meantime, ResourceFunction[“NutritionLabelData”] is a great tool and includes vitamin E.
Thanks today for the question about customizing a nutrition report to include fiber. Here’s an example of how to specify which nutrient properties you want in the report:
To follow up on our discussion of nutrients in raw vs cooked carrots, I created the attached notebook, which demonstrates how to compute the difference in beta-carotene using the entities for “raw carrot” and “cooked carrot.” Since beta-carotene is a precursor of vitamin A, I converted the results to vitamin A and compared with the RDA for vitamin A.
Also in the notebook, lycopene in tomatoes is another good example of higher concentration in cooked form, due to breakdown of the cell walls and water loss during cooking.
I hope the notebook is a good template for investigating more nutrients and foods!
I’m glad the notebook is useful. Cooking method is such an important part of nutrient content, but it’s been tricky to make nutrient retention computable. I would love to see any work you do in this area.
Today’s session “Recipe Management with LLMs” will include three resource functions from the Wolfram Function Repository: RecipeGraph, RecipeRiskAnalysis and BlendSpices. I have downloaded and attached the source notebooks from the repository if you would like to review the LLM prompts for each. I’m hoping these prompts will be a good starting point for you to design your own recipe, food or nutrition LLM functions. Please let me know if you see ways I can improve my prompts.
Thanks so much Jim for the excellent suggestion yesterday to offer a food and nutrition course that incorporates the Wolfram Language quiz and assessment functions (AssessmentFunction—Wolfram Documentation). Great idea, and I look forward to following up on that.
During the presentation today Gay used several LLM related functions. In order to use these functions you will need a subscription to the LLM Kit or an API key from an LLM provider like OpenAI.
Please try it out and let me know which questions and question formats worked well and which need improvement. For this initial version, each answer is being evaluated individually. I have set permissions to Read and Interact, but please let me know if you have any issues accessing the quiz. Thanks so much!
