Navigating nutrients: A scoping review on real-time food nutrition classification and recommendation systems

Abstract

In an era where fast-paced lifestyles often conflict with the pursuit of healthy eating, the demand for innovative solutions to aid nutritional decision-making has never been more pressing. Real-time food nutrition classification and recommendation systems offer an effective solution to this growing issue. By harnessing state-of-the-art technologies such as sensor-based data collection and machine learning algorithms, these systems can conduct a precise analysis of the nutritional composition of foods. This scoping review presents a comprehensive investigation of real-time food nutrition recommendation and classification systems, encompassing their capabilities, effectiveness, and potential ramifications for public health, focusing on identifying and evaluating the technological approaches, nutritional parameters, and applications of these systems. By synthesizing prior research, we can reveal the complex web of methodologies, trends, and obstacles that influence this ever-evolving discipline. We included only peer-reviewed studies and conference proceedings, published within the last decade. A systematic search of Scopus, IEEE Xplore, and PubMed databases yielded 166 papers, of which 36 studies were selected for further evaluation. The findings highlight the importance of technological advancements and the need for further research to improve the effectiveness of these systems in promoting healthy eating habits. The study unveils a landscape filled with possibilities, from machine learning algorithms to sensor-based technologies, each offering unique pathways for users to make smart dietary decisions on the go.