On-site identification of Ophiocordyceps sinensis using multispectral imaging and chemometrics
Keywords:
Ophiocordyceps sinensis, MSI, SAPSO-SVC, On-site distribution mapAbstract
For the reasonable and effective collection of Ophiocordyceps sinensis, a new method of on-site identification was attempted using a portable multispectral imaging (MSI) technique. Three dimensional (3D) data-cubes of representative Ophiocordyceps sinensis and weeds samples were acquired and pre-processed with standard normal variate transformation (SNV). Principal component analysis (PCA) and simulated annealing particle swarm optimisation (SAPSO) algorithms were used to extract characteristic images and develop the support vector classification (SVC) models. Results show that the fused feature model of SAPSO-SVC has the best performance, resulting in a recognition accuracy of the prediction set of 96.30%. Moreover, on-site distribution map of Ophiocordyceps sinensis and weeds was created using the spectral feature model of SAPSO-SVC, and the target could be easily identified from the distribution map. This work demonstrates the potential for on-site identification of Ophiocordyceps sinensis in the Qinghai–Tibet Plateau using a portable MSI technique combined with the SAPSO-SVC algorithm. Keywords: Ophiocordyceps sinensis, MSI, SAPSO-SVC, On-site distribution map DOI: 10.25165/j.ijabe.20201306.5425 Citation: Duan H W, Tong X, Cui R X, Han L J, Huang G Q. On-site identification of Ophiocordyceps sinensis using multispectral imaging and chemometrics. Int J Agric & Biol Eng, 2020; 13(6): 166–170.References
[1] Brosnan T, Sun D W. Inspection and grading of agricultural and food products by computer vision systems—A review. Computers and Electronics in Agriculture, 2002; 36: 193–213.
[2] Ma J, Sun D W, Qu J H, Liu D, Pu H, Gao W H, et al. Applications of computer vision for assessing quality of agri-food products: a Review of recent research advances. Crit Rev Food Sci Nutr, 2016; 56(1): 113–127.
[3] Rong D, Rao X, Ying Y. Computer vision detection of surface defect on oranges by means of a sliding comparison window local segmentation algorithm. Computers and Electronics in Agriculture, 2017; 137: 59–68.
[4] Tellaeche A, Pajares G, Burgos-Artizzu X P, Ribeiro A. A computer vision approach for weeds identification through Support Vector Machines. Applied Soft Computing, 2011; 11(1): 908–915.
[5] Khulal U, Zhao J, Hu W, Chen Q. Nondestructive quantifying total volatile basic nitrogen (TVB-N) content in chicken using hyperspectral imaging (HSI) technique combined with different data dimension reduction algorithms. Food Chem, 2016; 197: 1191–1199.
[6] Liu L, Cozzolino D, Cynkar W U, Gishen M, Colby C B. Geographic classification of Spanish and Australian tempranillo red wines by visible and near-infrared spectroscopy combined with multivariate analysis. J. Agric. Food Chem., 2006; 54: 6754–6759.
[7] Wang D, Dowell F E, Ram M S, Schapaugh W T. Classification of fungal-damaged soybean seeds using near-infrared spectroscopy. International Journal of Food Properties, 2004; 7(1): 75–82.
[8] Dissing B S, Papadopoulou O S, Tassou C, Ersbøll B K, Carstensen J M, Panagou E Z, et al. Using multispectral imaging for spoilage detection of pork meat. Food and Bioprocess Technology, 2012; 6(9): 2268–2279.
[9] Pu H, Kamruzzaman M, Sun D W. Selection of feature wavelengths for developing multispectral imaging systems for quality, safety and authenticity of muscle foods-a review. Trends in Food Science & Technology, 2015; 45(1): 86–104.
[10] Ropodi A I, Pavlidis D E, Mohareb F, Panagou E Z, Nychas G J E. Multispectral image analysis approach to detect adulteration of beef and pork in raw meats. Food Research International, 2015; 67: 12–18.
[11] Sendin K, Manley M, Williams P J. Classification of white maize defects with multispectral imaging. Food Chem, 2018; 243: 311–318.
[12] Tang C, He H, Li E, Li H. Multispectral imaging for predicting sugar content of ‘Fuji’ apples. Optics & Laser Technology, 2018; 106: 280–285.
[13] Li H, Chen Q, Zhao J, Wu M. Nondestructive detection of total volatile basic nitrogen (TVB-N) content in pork meat by integrating hyperspectral imaging and colorimetric sensor combined with a nonlinear data fusion. LWT - Food Science and Technology, 2015; 63(1): 268–274.
[14] Qi C M, Zhou Z B, Sun Y C, Song H B, Hu L S, Wang Q. Feature selection and multiple kernel boosting framework based on PSO with mutation mechanism for hyperspectral classification. Neurocomputing, 2017; 220: 181–190.
[15] Yang Y C, Sun D W, Pu H, Wang N N, Zhu Z. Rapid detection of anthocyanin content in lychee pericarp during storage using hyperspectral imaging coupled with model fusion. Postharvest Biology and Technology, 2015; 103: 55–65.
[16] Duan H W, Zhu R G, Yao X D, Lewis E. Sensitive variables extraction, non-destructive detection and visualization of total viable count (TVC) and pH in vacuum packaged lamb using hyperspectral imaging. Analytical Methods, 2017; 9(21): 3172–3183.
[17] Eberhart R, Kennedy J. A new optimizer using particle swarm theory. Sixth International Symposium on Micro Machine and Human Science. IEEE, 1995; pp.39–43. DOI: 10.1109/MHS.1995.494215
[18] Haralick R M, Shanmugam K. Textural features for image classification. IEEE Trans. Syst. Man Cybern, 1973; 6: 610–621.
[19] Kuo Y C, Tsai W J, Shiao M S, Chen C F, Lin C Y. Cordyceps sinensis as an immunomodulatory agent. Am. J. Chinese Med, 1996; 24: 111–125.
[20] Li S P, Li P, Dong T T, Tsim K W. Anti-oxidation activity of different types of natural Cordyceps sinensis and cultured Cordyceps mycelia. Phytomedicine, 2001; 8(3): 207–212.
[21] Li S P, Zhao K J, Ji Z N, Song Z H, Dong T T X, Lo C K, et al. A polysaccharide isolated from Cordyceps sinensis, a traditional Chinese medicine, protects PC12 cells against hydrogen peroxide-induced injury. Life Sciences, 2003; 73(19): 2503–2513.
[22] Wang Z M, Peng X, Lee K L D, Tang J C-O, Cheung P C-K, Wu J Y. Structural characterisation and immunomodulatory property of an acidic polysaccharide from mycelial culture of Cordyceps sinensis fungus Cs-HK1. Food Chemistry, 2011; 125(2): 637–643.
[23] Dong C H, Yao Y J. In vitro evaluation of antioxidant activities of aqueous extracts from natural and cultured mycelia of Cordyceps sinensis. LWT - Food Science and Technology, 2008; 41(4): 669–677.
[24] Lin H. Development and application of feature selection techniques in protein data Analysis and Prediction. Lett. Org. Chem, 2017; 14: 619–620.
[25] Saini S, Zakaria N, Rambli D R, Sulaiman S. Markerless human motion tracking using hierarchical multi-swarm cooperative particle swarm optimization. PLoS One, 2015; 10(5): e0127833.
[2] Ma J, Sun D W, Qu J H, Liu D, Pu H, Gao W H, et al. Applications of computer vision for assessing quality of agri-food products: a Review of recent research advances. Crit Rev Food Sci Nutr, 2016; 56(1): 113–127.
[3] Rong D, Rao X, Ying Y. Computer vision detection of surface defect on oranges by means of a sliding comparison window local segmentation algorithm. Computers and Electronics in Agriculture, 2017; 137: 59–68.
[4] Tellaeche A, Pajares G, Burgos-Artizzu X P, Ribeiro A. A computer vision approach for weeds identification through Support Vector Machines. Applied Soft Computing, 2011; 11(1): 908–915.
[5] Khulal U, Zhao J, Hu W, Chen Q. Nondestructive quantifying total volatile basic nitrogen (TVB-N) content in chicken using hyperspectral imaging (HSI) technique combined with different data dimension reduction algorithms. Food Chem, 2016; 197: 1191–1199.
[6] Liu L, Cozzolino D, Cynkar W U, Gishen M, Colby C B. Geographic classification of Spanish and Australian tempranillo red wines by visible and near-infrared spectroscopy combined with multivariate analysis. J. Agric. Food Chem., 2006; 54: 6754–6759.
[7] Wang D, Dowell F E, Ram M S, Schapaugh W T. Classification of fungal-damaged soybean seeds using near-infrared spectroscopy. International Journal of Food Properties, 2004; 7(1): 75–82.
[8] Dissing B S, Papadopoulou O S, Tassou C, Ersbøll B K, Carstensen J M, Panagou E Z, et al. Using multispectral imaging for spoilage detection of pork meat. Food and Bioprocess Technology, 2012; 6(9): 2268–2279.
[9] Pu H, Kamruzzaman M, Sun D W. Selection of feature wavelengths for developing multispectral imaging systems for quality, safety and authenticity of muscle foods-a review. Trends in Food Science & Technology, 2015; 45(1): 86–104.
[10] Ropodi A I, Pavlidis D E, Mohareb F, Panagou E Z, Nychas G J E. Multispectral image analysis approach to detect adulteration of beef and pork in raw meats. Food Research International, 2015; 67: 12–18.
[11] Sendin K, Manley M, Williams P J. Classification of white maize defects with multispectral imaging. Food Chem, 2018; 243: 311–318.
[12] Tang C, He H, Li E, Li H. Multispectral imaging for predicting sugar content of ‘Fuji’ apples. Optics & Laser Technology, 2018; 106: 280–285.
[13] Li H, Chen Q, Zhao J, Wu M. Nondestructive detection of total volatile basic nitrogen (TVB-N) content in pork meat by integrating hyperspectral imaging and colorimetric sensor combined with a nonlinear data fusion. LWT - Food Science and Technology, 2015; 63(1): 268–274.
[14] Qi C M, Zhou Z B, Sun Y C, Song H B, Hu L S, Wang Q. Feature selection and multiple kernel boosting framework based on PSO with mutation mechanism for hyperspectral classification. Neurocomputing, 2017; 220: 181–190.
[15] Yang Y C, Sun D W, Pu H, Wang N N, Zhu Z. Rapid detection of anthocyanin content in lychee pericarp during storage using hyperspectral imaging coupled with model fusion. Postharvest Biology and Technology, 2015; 103: 55–65.
[16] Duan H W, Zhu R G, Yao X D, Lewis E. Sensitive variables extraction, non-destructive detection and visualization of total viable count (TVC) and pH in vacuum packaged lamb using hyperspectral imaging. Analytical Methods, 2017; 9(21): 3172–3183.
[17] Eberhart R, Kennedy J. A new optimizer using particle swarm theory. Sixth International Symposium on Micro Machine and Human Science. IEEE, 1995; pp.39–43. DOI: 10.1109/MHS.1995.494215
[18] Haralick R M, Shanmugam K. Textural features for image classification. IEEE Trans. Syst. Man Cybern, 1973; 6: 610–621.
[19] Kuo Y C, Tsai W J, Shiao M S, Chen C F, Lin C Y. Cordyceps sinensis as an immunomodulatory agent. Am. J. Chinese Med, 1996; 24: 111–125.
[20] Li S P, Li P, Dong T T, Tsim K W. Anti-oxidation activity of different types of natural Cordyceps sinensis and cultured Cordyceps mycelia. Phytomedicine, 2001; 8(3): 207–212.
[21] Li S P, Zhao K J, Ji Z N, Song Z H, Dong T T X, Lo C K, et al. A polysaccharide isolated from Cordyceps sinensis, a traditional Chinese medicine, protects PC12 cells against hydrogen peroxide-induced injury. Life Sciences, 2003; 73(19): 2503–2513.
[22] Wang Z M, Peng X, Lee K L D, Tang J C-O, Cheung P C-K, Wu J Y. Structural characterisation and immunomodulatory property of an acidic polysaccharide from mycelial culture of Cordyceps sinensis fungus Cs-HK1. Food Chemistry, 2011; 125(2): 637–643.
[23] Dong C H, Yao Y J. In vitro evaluation of antioxidant activities of aqueous extracts from natural and cultured mycelia of Cordyceps sinensis. LWT - Food Science and Technology, 2008; 41(4): 669–677.
[24] Lin H. Development and application of feature selection techniques in protein data Analysis and Prediction. Lett. Org. Chem, 2017; 14: 619–620.
[25] Saini S, Zakaria N, Rambli D R, Sulaiman S. Markerless human motion tracking using hierarchical multi-swarm cooperative particle swarm optimization. PLoS One, 2015; 10(5): e0127833.
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Published
2020-12-03
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Duan, H., Tong, X., Cui, R., Han, L., & Huang, G. (2020). On-site identification of Ophiocordyceps sinensis using multispectral imaging and chemometrics. International Journal of Agricultural and Biological Engineering, 13(6), 166–170. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/5425
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