Applications of electronic nose and machine learning models in vegetables quality assessment: A review

Abstract

Vegetables are an integral part of a balanced diet. They are good source of nutrition enriched with vitamins, mineral and antioxidants. They are prone to spoilage after harvesting without proper storage. Their quality can be determined by chemical analysis, high-performance liquid chromatography and near-infrared detection method. These methods are time taking, required a high cost equipment and trained person to perform. This article discussed the basic concept of electronic nose and machine learning. Electronic nose is used to detect gases excreted from vegetables and produced electronic signals. Machine learning is trained on these signals and predict the quality of vegetables. This paper presents the application of an electronic nose system with machine learning models. It is studied that this method is a cost-effective, portable and potential upcoming technique to overcome quality issues in vegetables.