RAPID DISCRIMINATION OF HIGH-QUALITY WATERMELON SEEDS BY MULTISPECTRAL IMAGING COMBINED WITH CHEMOMETRIC METHODS
Abstract
This study focuses on the feasibility of nondestructive discrimination of high-quality watermelon seeds with a multispectral imaging system combined with chemometrics. Principal component analysis (PCA), least squares-support vector machines (LS-SVM), back propagation neural network (BPNN), and random forest (RF) were applied to determine the seed quality. The results demonstrate that both the spectral and the morphological features are essential for discrimination of the quality of watermelon seeds. Clear differences between high-quality watermelon seeds and other watermelon seeds including dead seeds and low-vigor seeds were visualized, and an excellent classification (with accuracies of 92% in the LS-SVM model for Julong and 91% in the RF model for Xiali, respectively) was achieved. These results indicate that multispectral imaging could be used for rapid and efficient non-destructive quality control of watermelon seeds.
About the Authors
W. . Liu
Hefei University
Russian Federation
Russian Federation
X. . Xu
Rice Research Institute, Anhui Academy of Agricultural Sciences
Russian Federation
Russian Federation
Ch. . Liu
School of Food Science and Engineering, Hefei University of Technology
Russian Federation
Russian Federation
L. . Zheng
School of Food Science and Engineering, Hefei University of Technology
Russian Federation
Russian Federation
References
1. A. Guo, J. Zhang, H. Sun, J. Salse, W. J. Lucas, Nat. Genet., 45, 51-58 (2012).
2. Perkins-Veazie, P. Collins, J. K. Davis, A. R. Roberts, J. Agric. Food Chem., 54, 2593-2597 (2006).
3. J. K. Collins, G. Y. Wu, P. Perkinsveazie, K. Spears, P. L. Claypool, R. A. Baker, Nutr., 23, 261-266 (2007).
4. D. S. Egel, C. Gunter, R. Martyn, Hortscience, 43, 1410-1414 (2008).
5. K. K. Moorthy, P. Babu, M. Sreedhar, V. S. A. K. Sama, P. N. Kumar, S. M. Balachandran, R. M. Sundaram, Seed Sci. Technol., 39, 282-292 (2011).
6. S. Ye, Y. Wang, D. Huang, J. Li, Y. Gong, L. Xu, L. Liu, Sci. Hort-Amsterdam, 155, 92-96 (2013).
7. M. D. Sánchez-Yélamo, Seed Sci. Technol., 34, 297-306 (2006).
8. V. N. Soares, S. G. Elias, G. I. Gadotti, A. E. Garay, F. A. Villela, Crop Sci., 56, 707-715 (2016).
9. E. Noli, S. Conti, M. Maccaferri, M. C. Sanguineti, Seed Sci. Technol., 27, 1-10 (1999).
10. N. Shetty, T. G. Min, R. Gislum, M. Olesen, B. Boelt, J. Near Infrared Spectrosc., 19, 451 (2012).
11. S. S. Shin, E. J. Shin, D. P. Sang, K. S. Lee, Can. J. Civil. Eng., 16, 538-546 (2012).
12. C. Mo, J. Lim, K. Lee, S. Kang, M. S. Kim, G. Kim, J. Biosyst. Eng., 38, 318-326 (2013).
13. A. Ambrose, S. Lohumi, W. H. Lee, B. K. Cho, Sensor Actuator B: Chem., 224, 500-506 (2016).
14. C. Mo, M. S. Kim, J. Lim, K. Lee, G. Kim, B. K. Cho, Trans. ASABE, 58, 959-968 (2015)
15. Y. Z. Feng, D. W. Sun, Crc. Crit. Rev. Food Sci. Nutr., 52, 1039-1058 (2012).
16. J. Qin, K. Chao, M. S. Kim, R. Lu, T. F. Burks, J. Food Eng., 118, 157-171 (2013).
17. F. A. E. Hahn, Biosyst. Eng., 81, 147-155 (2002).
18. M. M. Løkke, H. F. Seefeldt, T. Skov, M. Edelenbos, Posthavrvest Biol. Technol., 75, 86-95 (2013).
19. C. Liu, W. Liu, W. Chen, J. Yang, L. Zheng, Food Chem., 173, 482-488 (2015).
20. T. C. Pearson, D. T. Wicklow, Trans. ASABE, 49, 1235-1245 (2006).
21. A. I. Ropodi, D. E. Pavlidis, F. Mohareb, E. Z. Panagou, G. J. Nychas, Food Res. Int., 67, 12-18 (2015).
22. A. I. Ropodi, E. Z. Panagou, G. J. E. Nychas, Food Control, 73, 57-63 (2016)
23. C. Liu, W. Liu, X. Lu, W. Chen, J. Yang, L. Zheng, Food Chem., 153, 87-93 (2014).
24. C. Cortes, V. Vapnik, Mach. Learn., 20, 273-297 (1995).
25. O. Devos, C. Ruckebusch, A. Durand, L. Duponchel, J. P. Huvenne, Chemometer Intell. Lab. Syst., 96, 27-33 (2009).
26. U. Bradter, T. J. Thom, J. Appl. Ecol., 48, 1057-1065 (2011).
27. M. Liu, M. Wang, J. Wang, D. Li, Sensor Actuator B: Chem., 177, 970-980 (2013).
Review
For citations:
Liu W., Xu X., Liu Ch., Zheng L. RAPID DISCRIMINATION OF HIGH-QUALITY WATERMELON SEEDS BY MULTISPECTRAL IMAGING COMBINED WITH CHEMOMETRIC METHODS. Zhurnal Prikladnoii Spektroskopii. 2018;85(6):919-925. (In Russ.)
Views: 232
Email this article 