Advances in Electronic Nose Development for Application to Agricultural Products

Abstract

High agricultural product quality is a fundamental requirement for consumers and odor is an important indicator that reflects product quality. Conventional analysis methods are based on sensory evaluation or on physic-chemical methods (e.g., high-performance liquid chromatography, liquid chromatography with tandem mass spectrometry). Analysis methods should be simple, quick, nondestructive, inexpensive, and specific, with good reproducibility and repeatability. Electronic noses can meet many of these requirements. Electronic nose development for agricultural product quality analysis has been increasing since the 1980s. This review summarizes the extensive achievements to date in electronic nose development for quality analysis/evaluation of agricultural products. First, we briefly introduce electronic noses and describe commonly used data analysis methods (e.g., artificial neural networks (ANNs), principal component analysis (PCA), linear discriminant analysis (LDA)). We then discuss the application of electronic noses to analysis of agricultural products (e.g., fruit, vegetables, tea, grain, meat from livestock and poultry, fish), including freshness evaluation, quality classification, and authenticity assessment variety identification, geographical origin identification, and disease detection. Finally, the problems, prospects, and likely future development of electronic noses for agricultural product quality analysis are highlighted.