# Towards computer vision powered color-nutrient assessment of pureed food

## TL;DR

This paper introduces a computer vision system using transmittance imaging and deep autoencoders to estimate vitamin A content in pureed foods, aiming to automate nutritional assessment for malnutrition monitoring.

## Contribution

It presents a novel deep learning approach linking color transmittance to nutrient content in pureed foods, specifically using a fine-tuned autoencoder for vitamin A prediction.

## Key findings

- Achieved 80% accuracy in predicting sweet potato puree concentration.
- Demonstrated the potential of transmittance imaging for nutrient sensing.
- Discussed optical property differences affecting prediction errors.

## Abstract

With one in four individuals afflicted with malnutrition, computer vision may provide a way of introducing a new level of automation in the nutrition field to reliably monitor food and nutrient intake. In this study, we present a novel approach to modeling the link between color and vitamin A content using transmittance imaging of a pureed foods dilution series in a computer vision powered nutrient sensing system via a fine-tuned deep autoencoder network, which in this case was trained to predict the relative concentration of sweet potato purees. Experimental results show the deep autoencoder network can achieve an accuracy of 80% across beginner (6 month) and intermediate (8 month) commercially prepared pureed sweet potato samples. Prediction errors may be explained by fundamental differences in optical properties which are further discussed.