# Rheology-Based Classification of Foods for the Elderly by Machine Learning Analysis
## 1 Introduction  
The elderly population over 65 years of age has been rapidly increasing throughout the world. Based on the 2019 revision of World Population Prospects [1], 16.7% of the population in the world will be over the age of 65 by 2050, compared to 9.1% in 2019. Specifically, Korea is expected to have the greatest percentage of senior citizens at 46.5% in 2067 [2]. In this context, the demands for senior-friendly products are globally increasing, and the food industry is no exception. From a food-scientific point of view, foods for the elderly are required to satisfy several requirements such as being nutritious, digestible, and easy to chew [3]. In particular, dysphagia is prevalent in the elderly [4], and the elderly with dysphagia are known to be at higher risk of malnutrition [5], indicating that the rheology of the elderly foods should be modified for older people who have difficulties in eating foods. Thereby, preceding studies have reported several methods to control the rheological properties of food products for older people by grinding or thickening according to the level of dysphagia [6, 7]. Furthermore, the food industry has been actively researching food-softening or reconstitution technology using enzymes, high-pressure, and 3D-printing techniques while preserving the original form of the food [8]. However, there is limited information on the official methods to effectively and reliably deal with various foods with different rheological properties for the elderly. There are several guidelines to measure their rheological characteristics such as the National Dysphagia Diet guideline and the International Dysphagia Diet Standardization Initiative [9, 10]. Although they provide valuable information to prepare foods for older adults with suitable rheological properties, most of the methods are mainly for nutritionists or dietitians to prepare diets for older adults mainly in convalescent hospitals. On the other hand, the universal design food (UDF) method is useful for the food industry as it provides objective guidelines to measure the rheological properties of the elderly'sdiets. However, due to the limitation of sample geometry, the UDF method [9] may not be universally applied to a wider variety of food products with numerous shapes and sizes. It is therefore necessary to develop scientific and reliable universal measurements that can be favorably made to investigate the rheological properties of foods with various types and sizes for the elderly. Machine learning is an application of artificial intelligence for enhancing the performance of a task directed by available data without relying on explicit programs [11]. It has gained more popularity in recent years with the increasing utilization of big data in a variety of industrial and scientific areas [12]. As state-of-the-art powerful technology, machine learning is as of now utilized to make predictions and suggestions based on mathematically calculated models [13]. Beyond the fields of computer science, a great deal of effort has been made to utilize machine learning in various scientific fields. Specifically, it has been popularly applied to biomedical areas for identifying diseases and improving diagnostic precision [14; 15]. In addition, the applications of machine learning are becoming more diverse and complex by inferring weather forecast uncertainty [16], predicting occupational accidents [17], and discriminating seismic waves for early earthquake warnings [18]. In the field of food science, [PERSON] et al. [19] applied machine learning methods to detect insect pests in food products, and [PERSON] et al. [20] utilized machine learning technology to search for food identity markers by metabolomics. In addition, analytical evaluation of food quality and authenticity by machine learning was recently reviewed [21]. However, the exploration of machine learning in the domain of food application is quite new.