Optimized Multivariate Analysis for the Discrimination of Cucumber Green Mosaic Mottle Virus-Infected Watermelon Seeds Based on Spectral Imaging
This study proposes a nondestructive sorting method based on the short-wave infrared hyperspectral imaging technique (SWIR-HIT) to detect and classify watermelon seeds infected with the cucumber green mosaic mottle virus (CGMMV). Virus-infected watermelon seeds were collected from virus-infected watermelon plants. Five plates each with 81 seeds were scanned. A total of 304 mean reflectance spectra were used to develop and evaluate virus-infected seed classification models with multivariate analysis methods such as partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), and least squares support vector machine (LS-SVM). To determine the optimal preprocessing method, three preprocessing methods were employed: multivariate scatter correct (MSC) as well as first- and second-derivative preprocessing with the Savitzky–Golay algorithm. Among these methods, second-derivative preprocessing with the LS-SVM method showed an approximately 75% accuracy with a 0.57 kappa coefficient for all three classification classes (infected, infection suspected, and sound seeds). Binary classification between infected and sound seeds by LS-SVM with second-derivative preprocessing showed an approximately 92% accuracy with a 0.75 kappa coefficient. To improve the classification accuracy, the genetic algorithm was implemented, and 9 bands were selected. The selected wavelengths were applied to develop and compare classification models with full wavelengths. The three-class classification with the selected bands showed an approximately 80% accuracy, whereas binary classification in infected and sound seeds showed a more than 93% accuracy with a 0.78 kappa coefficient. These results indicate that SWIR-HIT is a valuable nondestructive tool for rapidly classifying CGMMV-infected watermelon seeds using LS-SVM with raw spectra.
KeywordsShort-wave infrared hyperspectral imaging Watermelon seed Cucumber green mottle mosaic virus Partial least squares discriminant analysis Support vector machine
This research was supported by the Export Strategy Technology Development Program, Ministry of Agriculture, Food and Rural Affairs (MAFRA), Republic of Korea.
Compliance with Ethical Standards
Conflict of Interest
The authors declare that they have no conflict of interest.
- Ainsworth, G. C. (1935). Mosaic diseases of the cucumber. Annals of Applied Biology, 22(1), 55–67. https://doi.org/10.1111/j.1744-7348.1935.tb07708.x.CrossRefGoogle Scholar
- Bangalore, A. S., Shaffer, R. E., Small, G. W., & Arnold, M. A. (1996). Genetic algorithm-based method for selecting wavelengths and model size for use with partial least-squares regression: application to near-infrared spectroscopy. Analytical Chemistry, 68(23), 4200–4212. https://doi.org/10.1021/ac9607121.CrossRefGoogle Scholar
- Borin, A., Ferrao, M. F., Mello, C., Maretto, D. A., & Poppi, R. J. (2006). Least-squares support vector machines and near infrared spectroscopy for quantification of common adulterants in powdered milk. Analytica Chimica Acta, 579(1), 25–32 https://www.sciencedirect.com/science/article/pii/S0003267006014954.CrossRefGoogle Scholar
- Chevallier, S., Bertrand, D., Kohler, A., & Courcoux, P. (2006). Application of PLS-DA in multivariate image analysis. Journal of Chemometrics: A Journal of the Chemometrics Society, 20(5), 221–229 https://onlinelibrary.wiley.com/doi/abs/10.1002/cem.994.CrossRefGoogle Scholar
- Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37–46. https://doi.org/10.1177/001316446002000104?journalCode=epma.CrossRefGoogle Scholar
- Devos, O., & Duponchel, L. (2011). Parallel genetic algorithm co-optimization of spectral pre-processing and wavelength selection for PLS regression. Chemometrics and Intelligent Laboratory Systems, 107(1), 50–58 https://www.sciencedirect.com/science/article/abs/pii/S0169743911000116.CrossRefGoogle Scholar
- Feng, Y. Z., & Sun, D. W. (2013). Near-infrared hyperspectral imaging in tandem with partial least squares regression and genetic algorithm for non-destructive determination and visualization of Pseudomonas loads in chicken fillets. Talanta, 109, 74–83 https://www.sciencedirect.com/science/article/pii/S0039914013000672.CrossRefGoogle Scholar
- Foody, G. M. (2002). Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80(1), 185–201 https://www.sciencedirect.com/science/article/abs/pii/S0034425701002954.CrossRefGoogle Scholar
- Gao, J., Li, X., Zhu, F., & He, Y. (2013). Application of hyperspectral imaging technology to discriminate different geographical origins of Jatropha curcas L. seeds. Computers and Electronics in Agriculture, 99, 186–193 https://www.sciencedirect.com/science/article/pii/S0168169913002287.CrossRefGoogle Scholar
- Gowen, A. A., O’Donnell, C., Cullen, P. J., Downey, G., & Frias, J. M. (2007). Hyperspectral imaging–an emerging process analytical tool for food quality and safety control. Trends in Food Science & Technology, 18(12), 590–598 https://www.sciencedirect.com/science/article/pii/S0924224407002026.CrossRefGoogle Scholar
- Gowen, A. A., Feng, Y., Gaston, E., & Valdramidis, V. (2015). Recent applications of hyperspectral imaging in microbiology. Talanta, 137, 43–54 https://www.sciencedirect.com/science/article/pii/S0039914015000260.CrossRefGoogle Scholar
- Jiao, L., & Wang, L. (2000). A novel genetic algorithm based on immunity. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 30(5), 552–561 https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=867862.CrossRefGoogle Scholar
- Lee, K. W., Lee, B. C., Park, H. C., & Lee, Y. S. (1990). Occurrence of cucumber green mottle mosaic virus disease of watermelon in Korea. Korean Journal of Plant Pathology, 6(2), 250–255 https://www.cabdirect.org/cabdirect/abstract/19922320396.Google Scholar
- Lee, H., Kim, M. S., Lim, H. S., Park, E., Lee, W. H., & Cho, B. K. (2016). Detection of cucumber green mottle mosaic virus-infected watermelon seeds using a near-infrared (NIR) hyperspectral imaging system: application to seeds of the “Sambok Honey” cultivar. Biosystems Engineering, 148, 138–147 https://www.sciencedirect.com/science/article/pii/S1537511015303111.CrossRefGoogle Scholar
- Lee, H., Kim, M., Qin, J., Park, E., Song, Y. R., Oh, C. S., et al. (2017a). Raman hyperspectral imaging for detection of watermelon seeds infected with acidovorax citrulli. Sensors, 17(10), 2188 https://www.mdpi.com/1424-8220/17/10/2188.
- Lee, H., Kim, M. S., Song, Y. R., Oh, C. S., Lim, H. S., Lee, W. H., et al. (2017b). Non-destructive evaluation of bacteria-infected watermelon seeds using visible/near-infrared hyperspectral imaging. Journal of the Science of Food and Agriculture, 97(4), 1084–1092. https://doi.org/10.1002/jsfa.7832.
- Osborne, W. W., & Stokes, J. L. (1955). A modified selenite brilliant-green medium for the isolation of Salmonella from egg products. Applied Microbiology, 3(5), 295. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1057123/–299.Google Scholar
- Schikora, M., Neupane, B., Madhogaria, S., Koch, W., Cremers, D., Hirt, H., Kogel, K. H., & Schikora, A. (2012). An image classification approach to analyze the suppression of plant immunity by the human pathogen Salmonella typhimurium. BMC Bioinformatics, 13(1), 171 https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-13-171.CrossRefGoogle Scholar
- Siripatrawan, U., Makino, Y., Kawagoe, Y., & Oshita, S. (2011). Rapid detection of Escherichia coli contamination in packaged fresh spinach using hyperspectral imaging. Talanta, 85(1), 276–281 https://www.sciencedirect.com/science/article/pii/S0039914011002669.CrossRefGoogle Scholar
- Westerhuis, J. A., Kourti, T., & MacGregor, J. F. (1998). Analysis of multiblock and hierarchical PCA and PLS models. Journal of Chemometrics: A Journal of the Chemometrics Society, 12(5), 301–321. https://doi.org/10.1002/(SICI)1099-128X(199809/10)12:5%3C301::AID-CEM515%3E3.0.CO;2-S.CrossRefGoogle Scholar
- Zhang, C., Shen, Y., Chen, J., Xiao, P., & Bao, J. (2008). Nondestructive prediction of total phenolics, flavonoid contents, and antioxidant capacity of rice grain using near-infrared spectroscopy. Journal of Agricultural and Food Chemistry, 56(18), 8268–8272. https://doi.org/10.1021/jf801830z.CrossRefGoogle Scholar