Comparison of ELM, RF, and SVM on E-nose and E-tongue to trace the quality status of mandarin (Citrus unshiu Marc.)

This paper demonstrates a joint way employing both of an electronic nose (E-nose) and an electronic tongue (E-tongue) to discriminate two types of satsuma mandarins from different development stages and to trace the internal quality changes (i.e. ascorbic acid, soluble solids content, total acid, and sugar/acid ratio). Extreme Learning Machine (ELM), Random Forest (RF) and Support Vector Machine (SVM) were applied for qualitative classification and quantitative prediction. The models were compared according to accuracy rate and regression parameters. For classification, the three systems (E-nose, E-tongue, and the fusion system) achieved perfect results respectively. For internal quality prediction, the RF and ELM models obtained better performance than the SVM models. The fusion systems had an advantage when compared with the signal system. This study shows that the E-nose and E-tongue systems combined with RF or ELM could be a fast and objective detection system to trace fruit internal quality changes.