Towards a Feasible and Robust Technique for Non-invasive Food Analysis

Growing requirements for food safety have driven the development of non-invasive monitoring technologies. Among these, electrical bioimpedance (BIA) has emerged as a method for assessing freshness, composition, and adulteration while preserving sample integrity. This work presents a systematic analysis of 259 Scopus-indexed publications from the last 10 years in order to strength and encourage new agricultural applications on food analysis. It was found 21 works on food quality using bioimpedance spectroscopy (BIA), which is the most non-destructive analysis. Due to measurement variability caused by product heterogeneity and environmental factors, standardizing protocols, accurate hardware designs and machine learning (ML) technique are the keys factors towards a feasible BIA system. It was also observed that portability will improve the supply chain management by enabling real-time quality control. It can be concluded that BIA data with ML is a robust and safe tool for the daily basis of consumers, farmers and industry, while supporting industry requirements for sustainable and efficient operations.