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.

Keywords:

Artificial olfactory, bonic electronic nose, classification and recognition, litchi, variety,

References

1. Anonymous, 2009. Electronic noses and tongues for the food and beverage industries. Food Australia, 61: 316.
   
2. Baby, R.E., M. Cabezas and E.N. Walsöe de Reca, 2000. Electronic nose: A useful tool for monitoring environmental contamination. Sensor. Actuat. B-Chem., 69(3): 214-218.
   
3. Bourgeois, W., A.C. Romain, J. Nicolas and R.M. Stuetz, 2003. The use of sensor arrays for environmental monitoring: Interests and limitations. J. Environ. Monit., 5(6): 852-860.
   CrossRef    PMid:14710922    Direct Link
4. Chen, H.J., Y. Cao, H.Y. Gao, F. Tao and Y. Fu, 2013. Quality properties and cluster analysis of different litchi cultivars. J. Chinese Inst. Food Sci. Technol., 5: 198-210. http://en.cnki.com.cn/Article_en/CJFDTOTAL-ZGSP201305046.htm.
   Direct Link
5. Falasconi, M., E. Gobbi, M. Pardo, M.D. Torre, A. Bresciani and G. Sberveglieri, 2005. Detection of toxigenic strains of Fusarium verticillioides in corn by electronic olfactory system. Sensor. Actuat. B-Chem., 108(1-2): 250-257.
   
6. Gupta, S., P.S. Variyar and A. Sharma, 2015. Application of mass spectrometry based electronic nose and chemometrics for fingerprinting radiation treatment. Radiat. Phys. Chem., 106: 348-354.
   CrossRef    Direct Link
7. Hou, X.Y., L.F. Liang, Z.L. Ji, P.W. Lee and M.Q. Li, 1987. Studies on the assay method for endogenous abscisic acid (ABA) and the fluctuation of its contents in the buds of litchi during floral initiation. Acta Hortic. Sin., 14(1): 12-16.
   Direct Link
8. Kim, D., D.H. Kim and S. Chang, 2008. Application of probabilistic neural network to design breakwater armor blocks. Ocean Eng., 35(3-4): 294-300. http://www.sciencedirect.com/science/article/pii/S002980180700251X.
   CrossRef    Direct Link
9. Lee, L.H.Y., W. Tsang, J. Lee, L. Qiao-Ming, R. Tsang, X. Zhi-Quan and Y. Ling, 2007. Investigation on pesticide residue of Litchi Chicness. Natural Enem. Insects, 29(2): 92-95.
   
10. Li, J., C.T. Wang, G.R. Liu, L. Zhao and P.Q. Yang, 2013. Fast detection of fried oil quality by electronic nose. Food Sci., 34(8): 236-239.
    Direct Link
11. Liu, M., L.Q. Pan, K. Tu and P. Liu, 2010. Determination of egg freshness during shelf life with electronic nose. Trans. Chinese Soc. Agric. Eng., 26(4): 317-321.
    
12. Olsson, J., T. Börjesson, T. Lundstedt and J. Schnürer, 2002. Detection and quantification of ochratoxin A and deoxynivalenol in barley grains by GC-MS and electronic nose. Int. J. Food Microbiol., 72(3): 203-214.
    CrossRef    Direct Link
13. Qiao, F., L.L. Huang, C.F. Fang, Y.P. Gu and S.F. Zhang, 2012a. Application of electronic tongue on various lyhcees discrimination. Food Ind., 33(10): 154-156.
    Direct Link
14. Qiao, F., L.L. Huang, C.F. Fang, Y.P. Gu and S.F. Zhang, 2012b. Comparison of taste-related compounds and analysis using electronic tongue of feizixiao and huaizhi lychee fruits from different planting area. J. Food Sci. Biotechnol., 31(9): 984-990.
    Direct Link
15. Saevels, S., J. Lammertyn, A.Z. Berna, E.A. Veraverbeke, C. Di Natale and B.M. Nicolai, 2004. An electronic nose and a mass spectrometry-based electronic nose for assessing apple quality during shelf life. Postharvest Biol. Tec., 31(1): 9-19.
    
16. Shi, Z.B., Y.Y. Tong, D.H. Chen and Y. Li, 2009. Identification of beef freshness with electronic nose. Trans. Chinese Soc. Agric. Mach., 40(11): 184-188.
    
17. Shnayder, E.P., M.P. Moshkin, D.V. Petrovskii, A.I. Shevela, A.N. Babko and V.G. Kulikov, 2009. Detection of helicobacter pylori infection by examination of human breath odor using electronic nose Bloodhound-214ST. AIP Conf. Proc., 1137(1): 523-524.
    
18. Wu, L.H., S.F. Shen and B. Li, 2012. Study on the correlation between sweetness and sugar of sweet potato before and after steaming. J. Chin. Cereal Oil Assoc., 27(9): 25-29. http://en.cnki.com.cn/Article_en/CJFDTOTAL-ZLYX201209009.htm.
    Direct Link
19. Xu, B.Q. and J. Yang, 2004. Study on Free Amino Acids in Litchi Varieties by RP-HPLC. Food Sci., 25(12): 156-159.
    Direct Link
20. Yu, H.C., J. Wang, H.M. Zhang and Y. Yu, 2007. Measurement of the LongJing tea quality by using an electronic nose. Trans. Chinese Soc. Agric. Mach., 38(7): 103-106.
    
21. Zan, F.G., Z.D. Wu, Q. Zeng, H.Y. Zhang, M.F. Li and X.Q. Zheng, 2009. Genetic diversity analysis of litchi germplasm by SRAP markers. Mol. Plant Breed., 7: 562-568.
    
22. Zhang, S.L., D. Pan, Q.F. Wang, G.H. Wang, R.C. Yang, J. Huang and X.Q. Li, 2012. Feasibility of measuring pericarp tenderness of fresh corn with fruit hardness meter. Crops, 3: 62-65.
    
23. Zheng, X.Z., Y.B. Lan, J.M. Zhu, J. Westbrook, W.C. Hoffmann and R.E. Lacey, 2009. Rapid identification of rice samples using an electronic nose. J. Bionic Eng., 6(3): 290-297.
    CrossRef    Direct Link

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