Food safety is the cornerstone of people's health.To achieve rapid and non-destructive detection of apple freshness,a recognition method for detecting the freshness of Red Fuji apples using an electronic nose system is proposed.The electronic nose system consists of two parts:A data acquisition system and a pattern recognition system, which respectively collect and analyze concentration data of apple odor.Using various learning algorithms such as principal component analysis,K-means clustering,back propagation neural network,and whale algorithm optimized back propagation neural network to analyze the collected data.The experimental results show that the recognition accuracy of K-means clustering algorithm based on principal component analysis for dimensionality reduction featuresis 70.67%,which is better than the recognition rate of K-means clustering algorithm based on original features.The recognition accuracy of back propagation neural network optimized by whale optimization algorithm can reach 95%, which can accurately identify samples with different freshness levels.This recognition method is efficient,fast, convenient,and highly feasible.