Abstract
In order to apply the bionic electronic nose in classifying the litchi into different classes, there were five different litchi varieties tested by the proposed methods in this study. Firstly, Physical differences of the 5 litchi varieties were compared in this study. Secondly, the response curves from the electronic nose (PEN3) were recorded for all the samples of the five litchi varieties. Variance Analysis (VA) was used for best characteristic value selection. Finally, via different pattern recognition techniques, including the Principal Component Analysis (PCA), the Linear Discrimination Analysis (LDA), the Probabilistic Neural Network (PNN), the Support Vector Machine (SVM) and the loading analysis (Loadings), it is found that PCA and LDA have a poor performance in classifying litchi varieties. The classification accuracy of the PNN model with training set and test set were 100 and 84%, respectively. As to the SVM model, the classification accuracy of training set and test set were 100 and 92%, respectively. According to the Loadings results, the sensors R3, R5, R8 and R1 can be chosen for developing special and simple instruments for the detection of litchi volatiles. The test results has demonstrated the feasibility and effectiveness of using bionic electronic nose for discriminating and classifying litchi varieties, which provides a new method for rapid and nondestructive classification of litchi varieties.
Highlights
Litchi, a typical subtropical fruit, is rich in nutrient elements, taste delicious and has high officinal value (Zan et al, 2009)
This study explores the feasibility of using electronic nose for litchi varieties classification and recognition through an experiment, in which 5 varieties of litchi were chosen for testing
Experimental materials: Five varieties of ripe litchi were chosen for sampling in this experiment, including Baili, Guiwei, Xiabuli, Jidi, Lingfengnuo, which were planted on the orchard of South China Agricultural University
Summary
A typical subtropical fruit, is rich in nutrient elements, taste delicious and has high officinal value (Zan et al, 2009). There are several existing classification and recognition methods of litchi varieties, such as the sensory evaluation method (Chen et al, 2013; Falasconi et al, 2005), the electronic tongue detection method (Qiao et al, 2012a, 2012b), the gas chromatographic method (Hou et al, 1987) and the liquid chromatography method (Xu and Yang, 2004). The machine detection methods such as the electronic tongue detection method, the gas chromatographic method and the liquid chromatography method, overcome the disadvantages of sensory evaluation method to some extent These machine detection methods have a higher requirement for the measured samples and more complicated operations, which usually need extract the juice of litchi fruit for testing. The Principal Component Analysis (PCA), the Linear Discrimination Analysis (LDA), the Probabilistic Neural Network (PNN), the Support Vector Machine (SVM) and the loading analysis (Loadings) are used for pattern recognition, aiming to explore the feasibility of an electronic nose on the use of litchi varieties classification and recognition
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
