Identify clustered perceptions towards nutrition-related topics (e.g. guidelines, meat consumption, trends like low carb diets etc) based on social media information.
This type of analysis could serve as exploratory research aiming to generate hypotheses or to identify profiles of participants to be recruited for more in-depth qualitative research.
Development of research questions in collaboration with sustainability colleauges and research group purposeful marketing.
Challenges
Main challenges in estimating dietary intake with machine learning?
- Data Quality: The accuracy of dietary intake estimates depends on the quality of input data, which can be affected by factors such as self-reported bias, underreporting, and measurement error.
- Data Availability: There may be limited data available for training and validation, which can make it difficult to develop accurate models.
- Diversity of Diets: Different populations have diverse dietary patterns and cultural preferences, which can make it challenging to develop models that work well for everyone.
- Heterogeneity of Food Intake: People’s food intake can vary widely, even within a single day, which makes it difficult to accurately model and predict their dietary intake.
- Complex Relationships between Food and Nutrients: The relationship between food intake and nutrient intake is complex and influenced by factors such as cooking methods, food processing, and portion sizes.
- Personalized Recommendations: There is a need for personalized dietary recommendations based on individual characteristics, such as age, gender, and lifestyle, which can be challenging to incorporate into machine learning models.
