Beyond nutrient-based food indices: a data mining approach to search for a quantitative holistic index reflecting the degree of food processing and including physicochemical properties

Abstract

Processing has major impacts on both the structure and composition of food and hence on nutritional value. In particular, high consumption of ultra-processed foods (UPFs) is associated with increased risks of obesity and diabetes. Unfortunately, existing food indices only focus on food nutritional content while failing to consider either food structure or the degree of processing. The objectives of this study were thus to link non-nutrient food characteristics (texture, water activity (a_w), glycemic and satiety potentials (FF), and shelf life) to the degree of processing; search for associations between these characteristics with nutritional composition; search for a holistic quantitative technological index; and determine quantitative rules for a food to be defined as UPF using data mining. Among the 280 most widely consumed foods by the elderly in France, 139 solid/semi-solid foods were selected for textural and a_w measurements, and classified according to three degrees of processing. Our results showed that minimally-processed foods were less hyperglycemic, more satiating, had better nutrient profile, higher a_w, shorter shelf life, lower maximum stress, and higher energy at break than UPFs. Based on 72 food variables, multivariate analyses differentiated foods according to their degree of processing. Then technological indices including food nutritional composition, a_w, FF and textural parameters were tested against technological groups. Finally, a LIM score (nutrients to limit) ≥8 per 100 kcal and a number of ingredients/additives >4 are relevant, but not sufficient, rules to define UPFs. We therefore suggest that food health potential should be first defined by its degree of processing.