APPLICATION OF ELECTRONIC NOSE AND MACHINE LEARNING IN DETERMINING FRUITS QUALITY: A REVIEW

Abstract

Fruits are an essential part of our diet, providing necessary nutrients that promote good health and proper functioning of our bodies. However, determining fruit quality can be complex due to numerous factors such as harmful insects, fungal diseases and damage caused during the harvesting and transport processes. Current methods employed by industries, such as sensory panels for categorising damage from healthy produce; are not as precise as needed. Therefore, there is a pressing need for a more simple and accurate way to assess the quality of fresh produce. An emerging technology, the electronic nose, presents a cost-efficient and precise solution to this problem. The electronic nose identifies various aromas which helps to evaluate fruit quality. In correlation with this, machine learning models classify fruits into their respective grades using the data collected by the electronic nose. In this review, we delve into the practicalities of using the electronic nose technology and machine learning algorithms to identify the quality of various fruits such as apples, bananas, peaches, litchis, strawberries, and pomegranates. In conclusion, the integration of the electronic nose technology and machine learning models could revolutionise the fruit industry by providing an efficient, precise, and cost-effective method for determining fruit quality.