Determination of damaged wheat kernels with hyperspectral imaging analysis
Keywords:
hyperspectral image, damaged wheat kernels, determination, PCA, SPA, LS-SVMAbstract
Hyperspectral imaging was applied to classify the damaged wheat kernels and healthy kernels. The spectral information was extracted from damaged wheat kernels and healthy kernels samples. The effective wavelengths were obtained from spectral of 865-1711 nm by X-loadings of principal component analysis (PCA) and successive projection algorithm (SPA) method, respectively. Partial least square method (PLS) and least square-support vector machine (LS-SVM) were then used to build classification models on full spectral data and effective wavelengths dataset, respectively. The results showed that the classification accuracy of every LS-SVM model was the best, being 100%. While the accuracy of the PLS model was slightly lower, still over 97%. The confusion matrix showed that several damaged wheat kernels samples were misclassified as healthy samples, while all healthy samples were correctly classified. The overall results indicated that hyperspectral imaging could be used for discriminating the damaged wheat kernels and could provide a reference for detecting other grain kernels grading degrees. Further, this study can provide a research basis for the development of online or portable detectors on grain damaged kernels recognition, which will be beneficial for grain grading or post-harvest quality processing of other grains. Keywords: hyperspectral image, damaged wheat kernels, determination, PCA, SPA, LS-SVM DOI: 10.25165/j.ijabe.20201305.4413 Citation: Shao Y Y, Gao C, Xuan G T, Gao X M, Chen Y Q, Hu Z C. Determination of damaged wheat kernels with hyperspectral imaging analysis. Int J Agric & Biol Eng, 2020; 13(5): 194–198.References
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[4] Caporaso N, Whitworth M B, Fisk I D. Protein content prediction in single wheat kernels using hyperspectral imaging. Food Chemistry, 2018; 240: 32–42.
[5] Senthilkumar T, Jayas D S, White N D G, Fields P G, Gräfenhan T. Detection of Ochratoxin A contamination in stored wheat using near-infrared hyperspectral imaging. Infrared Physics & Technology, 2017; 81: 228–235.
[6] Lancelot E, Bertrand D, Hanafi M, Jaillais B. Near-infrared hyperspectral imaging for following imbibition of single wheat kernel sections. Vibrational Spectroscopy, 2017; 9: 46–53.
[7] Senthilkumar T, Jayas D S, White N D G, Fields P G, Gräfenhan T. Detection of fungal infection and Ochratoxin A contamination in stored wheat using near-infrared hyperspectral imaging. Journal of Stored Products Research, 2016; 65: 30–39.
[8] Chu X L. Near-infrared spectroscopy analytical technology practical handbook. Beijing: Mechanical Industry Press, 2016; 493p.
[9] Williams P J, Kucheryavskiy S. Classification of maize kernels using NIR hyperspectral imaging. Food Chemistry, 2016; 209: 131–138.
[10] Barreto A, Cruz-Tirado J P, Siche R, Quevedo R. Determination of starch content in adulterated fresh cheese using hyperspectral imaging. Food Bioscience, 2018; 21: 14–19.
[11] Su W H, Sun D W. Evaluation of spectral imaging for inspection of adulterants in terms of common wheat flour, cassava flour and corn flour in organic Avatar wheat (Triticum spp.) flour. Journal of Food Engineering, 2017; 200: 59–69.
[12] Esteki M, Vander Heyden Y, Farajmand B, Kolahderazi Y. Qualitative and quantitative analysis of peanut adulteration in almond powder samples using multi-elemental fingerprinting combined with multivariate data analysis methods. Food Control, 2017; 82: 31–41.
[13] Lohumi S, Lee S, Lee H, Cho B K. A review of vibrational spectroscopic techniques for the detection of food authenticity and adulteration. Trends in Food Science & Technology, 2015; 46(1): 85–98.
[14] Verdú S, Vásquez F, Grau R, Ivorra E, Sánchez A. J, Barat J M. Detection of adulterations with different grains in wheat products based on the hyperspectral image technique: The specific cases of flour and bread.
Food Control, 2016; 62: 373–380.
[15] Guo D, Zhu Q, Huang M, Guo Y, Qin J. Model updating for the classification of different varieties of maize seeds from different years by hyperspectral imaging coupled with a pre-labeling method. Computers and Electronics in Agriculture, 2017; 142: 1–8.
[16] Singh C B, Jayas D S, Paliwal J, White N D G. Detection of insect-damaged wheat kernels using near-infrared hyperspectral imaging. Journal of Stored Products Research, 2009; 45(3): 151–158.
[17] Singh C B, Jayas D S, Paliwal J, White N D G. Identification of insect-damaged wheat kernels using short-wave near-infrared hyperspectral and digital colour imaging. Computers and Electronics in Agriculture, 2010; 73(2): 118–125.
[18] Dong J J, Wu J Z, Liu Q. Research on hyperspectral image detection method of wheat unsound kernel. Journal of Electronic Measurement and Instrumentation, 2017; 31(7): 1074–1080. (in Chinese)
[19] Luo X, Jayas D S, Symonst S J. Identification of damaged kernels in wheat using a colour machine vision system. Journal of Cereal Science, 1999; 30: 49–59.
[20] Narvankar D S, Singh C B, Jayas D S, White N D G. Assessment of soft X-ray imaging for detection of fungal infection in wheat. Biosystems Engineering, 2009; 103(1): 49–56.
[21] Shao Y Y, Xuan G T, Hu Z C, Gao Z M, Liu L. Determination of the bruise degree for cherry using Vis-NIR reflection spectroscopy coupled with multivariate analysis. PloS One, 2019; 14(9): 1–13.
[22] Gao Z M, Zhao Y R, Khot L R, Hoheisel G A, Zhang Q. Optical sensing for early spring freeze related blueberry bud damage detection: Hyperspectral imaging for salient spectral wavelengths identification. Computers and Electronics in Agriculture, 2019; 167: 105025. doi: 10.1016/j.compag.2019.105025.
[23] Gao Z M, Shao Y Y, Xuan G T, Wang Y X, Liu Y, Han X. Real-time hyperspectral imaging for the in-field estimation of strawberry ripeness with deep learning. Artificial Intelligence in Agriculture, 2020; 4: 31–38.
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Published
2020-10-13
How to Cite
Shao, Y., Gao, C., Xuan, G., Gao, X., Chen, Y., & Hu, Z. (2020). Determination of damaged wheat kernels with hyperspectral imaging analysis. International Journal of Agricultural and Biological Engineering, 13(5), 194–198. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/4413
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Information Technology, Sensors and Control Systems
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