Rheology-Based Classification of Foods for the Elderly by Machine Learning Analysis

Abstract

A new research framework for the rheological measurements of foods for the elderly was proposed by combining experiments with machine learning. Universal design food (UDF), the conventional rheological test for foods for the elderly, was compared with three different rheological methods in terms of stress, clearly showing a great linear correlation (R2 = 0.9885) with the puncture test. A binary logistic classification with the tensorflow library was successfully applied to predict the elderly’s foods based on the rheological stress values from the UDF and puncture tests. The gradient descent algorithm demonstrated that the cost functions became minimized, and the model parameters were optimally estimated with an increasing number of machine learning iterations. From the testing dataset, the predictive model with a threshold value of 0.7 successfully classified the food samples into two groups (belong to the elderly’s foods or not) with an accuracy of 98%. The research framework proposed in this study can be applied to a wide variety of classification and estimation-related studies in the field of food science.