A new take on measuring relative nutritional density: The feasibility of using a deep neural network to assess commercially-prepared pureed food concentrations

TL;DR

This study demonstrates the feasibility of using a deep neural network with multispectral imaging to accurately assess the relative nutritional density of commercially prepared pureed foods, aiding quality assurance.

Contribution

It introduces a novel deep neural network approach combined with multispectral imaging for computational analysis of pureed food nutrient density, a first in the field.

Findings

Average top-1 prediction accuracy of 92.2%.

Sensitivity of 83.0%, specificity of 95.0%.

Effective assessment across multiple puree types and dilutions.

Abstract

Dysphagia affects 590 million people worldwide and increases risk for malnutrition. Pureed food may reduce choking, however preparation differences impact nutrient density making quality assurance necessary. This paper is the first study to investigate the feasibility of computational pureed food nutritional density analysis using an imaging system. Motivated by a theoretical optical dilution model, a novel deep neural network (DNN) was evaluated using 390 samples from thirteen types of commercially prepared purees at five dilutions. The DNN predicted relative concentration of the puree sample (20%, 40%, 60%, 80%, 100% initial concentration). Data were captured using same-side reflectance of multispectral imaging data at different polarizations at three exposures. Experimental results yielded an average top-1 prediction accuracy of 92.2+/-0.41% with sensitivity and specificity of 83.0+/-15.0% and 95.0+/-4.8%, respectively. This DNN imaging system for nutrient density analysis of pureed food shows promise as a novel tool for nutrient quality assurance.