The aim of this work was to fulfill the objective and rapid assessment of quality and flavor of fresh orange juice with different storage time. An electronic tongue system that based on virtual instrument technology was developed and used to the qualitative and quantitative analysis of fresh orange juice samples with six kinds of storage time. According to the characteristics of electronic tongue respond signal, it was first preprocessed by the principal component analysis (PCA) method and discrete wavelet transform (DWT) method, respectively. According to the classification result, the DWT was selected as a recommended feature extraction method. Then the linear discriminant analysis (LDA) was used to the qualitative analysis of fresh orange juice samples with different storage time. Moreover, the least squared-support vector machines based on particle swarm optimization method (PSO-LSSVM) was applied to quantitative forecast the different storage time. The results showed that the cumulative contribution rate of LD1 and LD2 was reached 95.7% when the linear discriminant analysis was employed, and the fresh orange juice samples with the six kinds of storage time were effectively discriminated; The PSO-LSSVM prediction model had high prediction precision for different storage time of fresh orange juice, the correlation coefficient (R²) root mean square error (RMSE), mean absolute error (MAE) were 0.999 1, 0.287 7, and 0.232 8, respectively. This study could provide technical reference for quality evaluation and monitoring of fresh fruit juice.