Comparison of ELM, RF, and SVM on E-nose and E-tongue to trace the quality status of mandarin (Citrus unshiu Marc.). (December 2015)

• Record Type: Journal Article
• Authors: Qiu, Shanshan; Wang, Jun; Tang, Chen; Du, Dongdong
• Is Part Of: Journal of food engineering. Volume 166(2015:Dec.)
• Page Start: 193
• Page End: 203
• Publication Date: 2015-12
• Subjects: E-nose -- E-tongue -- Citrus -- Extreme Learning Machine -- Random Forest -- Support Vector Machine; Food industry and trade -- Periodicals; Food -- Analysis -- Periodicals; Aliments -- Industrie et commerce -- Périodiques; Aliments -- Analyse -- Périodiques; Aliments -- Recherche -- Périodiques; 664.005
• DOI: 10.1016/j.jfoodeng.2015.06.007
• Languages: English
• ISSNs: 0260-8774

Abstract

Highlights: The quality of Citrus was detected by E-nose and E-tongue simultaneously. ELM as a novel data mining method was first used in fusion of E-nose and E-tongue. SVM, RF, and ELM were compared with accuracy rate and regression parameters. E-nose and E-tongue with RF or ELM could be an alternative to monitor fruit quality. Abstract: 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.