Developing a robust methodology to analyse taste buds (fungiform papillae) using smart phones by consumers
Fungiform papillae (FP) are ‘mushroom-like’ papillae that appear as pinkish spots, located on the anterior part of the tongue, containing taste buds. Research has suggested that the anatomical structure of FP varies greatly across individuals and could be a marker for taste sensitivity, and further linked to food preference and choice. Until now, manual FP counting from digital photography was the most popular method of quantification. This is extremely time consuming and error prone. Automated methods have started to be developed in recent years; however, this requires the image to be high quality and taken under very strict conditions using professional cameras.
This project aims to:
- Fully automate the quantification of FP using cutting edge computer vision methods, such that we are able to provide reliable counts from lower quality images.
- Develop an interactive imaging app that can be used by to guide the self-photography of FP at home. We will use the app platform to additionally explore automated capture of food choices and nutritional information from plated food (which can add new dimensions to future studies).
- Integrate this new technology in a food sensory study investigating the relationship between FP, taste sensitivity and taste preference.
The successful applicant will develop algorithms using deep machine learning to quantify FP on the tongue utilising images collected via smart phones with an interactive app, so that consumers can easily take this information by themselves. The app itself will use computer vision techniques to interactively help the user take a high-quality photograph. These two novelties together contribute to a new platform for conducting taste research in the general public.
Although applicants are expected to have a computer science background, they will be integrated into a team of food scientists to help create a new image dataset to train the machine learning models, and to co-develop the app with domain input to ensure the system delivers the best quality data it can.
Supervisors: Prof Andrew French (School of Computer Science), Dr Qian Yang (School of Biosciences).
For further details and to arrange an interview please contact Prof Andrew French at andrew.p.french@nottingham.ac.uk.