  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.
