Intellectual Information System for Recognition and Food Product Composition Analysis

Abstract

Taking into account the long-term COVID-19 effects, doctors have suggested a specific diet, as quality foods are important for a healthy lifestyle, as well as fighting with various chronic diseases. Advances in technology encourage manufacturers to increase profits and reduce production costs by using flavorings and specific food additives (E-additives), as well as their synthetic combinations, each of which has a number of features. The international collection of food standards Codex Alimentarius includes a list of about 500 types of original E-additives, which can be natural, identical to natural or synthetic. A number of such additives have a negative effect on the human organism, some of them have not been fully researched, that potentially creates the risk of genetic mutations and, accordingly, autoimmune and carcinogenic effects in the future. That is why it is important for the buyer of a certain food product to quickly identify the product composition, analyze the relevant additives in it, their dangers “online” at the storefront using mobile devices and the Internet.

Recently, modern developments have been considered through the prism of artificial intelligence, accordingly, such models, which will be oriented to work with food products, will help support people’s desire for healthy food. This article briefly describes the developed intelligent information system, which, to achieve the goal, applies machine learning models on a database with food products, a barcode scanner with an additional correction procedure, if it is highly damaged, a regular expression apparatus, a text similarity metric, and also a product rating system. The corresponding software application works in three modes on the iOS and Android platforms: product recognition, barcodes recognition, composition analysis and product evaluation. The program allows buyer to get information about the product, its composition, an additives list, scientific information on them, the rating of the product “usefulness”, its comparison with similar products, etc.