Differentiation of deciduous-calyx and persistent-calyx pears using hyperspectral reflectance imaging and multivariate analysis

Computers and Electronics in Agriculture

Volumn 137, Issue , 2017, Pages 150-156

  Hu, Haijiang ^a   Pan, Leiqing ^a   Sun, Ke ^a   Tu, Sicong ^b   Sun, Ye ^a   Wei, Yingying ^a   Tu, Kang ^a  

^a NANJING AGRICULTURAL UNIVERSITY   (China)

^b UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

Deciduous calyx pear (DCF); Hyperspectral imaging; Persistent calyx pear (PCF); Successive projection algorithm; Support vector machine

Indexed keywords

FRUITS; IMAGING TECHNIQUES; MULTIVARIANT ANALYSIS; NONDESTRUCTIVE EXAMINATION; REFLECTION; SPECTROSCOPY; SUPPORT VECTOR MACHINES;

DECIDUOUS-CALYX PEAR (DCF); HYPERSPECTRAL IMAGING TECHNOLOGIES; HYPERSPECTRAL REFLECTANCE; MORPHOLOGICAL CHARACTERISTIC; PERSISTENT-CALYX PEAR (PCF); SUCCESSIVE PROJECTION ALGORITHMS; SUCCESSIVE PROJECTIONS ALGORITHMS (SPA); XINJIANG UYGUR AUTONOMOUS REGION;

HYPERSPECTRAL IMAGING;

AGRICULTURAL MODELING; ALGORITHM; DECIDUOUS TREE; IMAGING METHOD; MORPHOLOGY; MULTIVARIATE ANALYSIS; SPECTRAL REFLECTANCE; SUPPORT VECTOR MACHINE;

CHINA; XINJIANG UYGUR;

CITRUS MAXIMA; PYRUS;

EID: 85017428485     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2017.04.002     Document Type: Article

References (30)

1. Andersen, C.M., Bro, R., Variable selection in regression – a tutorial. J. Chemometr. 24:11–12SI (2010), 728–737.
2. Araujo, M., Saldanha, T., Galvao, R., Yoneyama, T., Chame, H.C., Visani, V., The successive projections algorithm for variable selection in spectroscopic multicomponent analysis. Chemometr. Intell. Lab. 57:2 (2001), 65–73.
3. Ariana, D.P., Lu, R., Guyer, D.E., Near-infrared hyperspectral. Reflectance imaging for detection of bruises on pickling cucumbers. Comput Electron. AGR 53:1 (2006), 60–70.
4. Balabin, R.M., Safieva, R.Z., Lomakina, E.I., Near-infrared (NIR) spectroscopy for motor oil classification: from discriminant analysis to support vector machines. Microchem. J. 98:1 (2011), 121–128.
5. Baranowski, P., Mazurek, W., Wozniak, J., Majewska, U., Detection of early bruises in apples using hyperspectral data and thermal imaging. J. Food Eng. 110:3 (2012), 345–355.
6. Barbin, D.F., ElMasry, G., Sun, D., Allen, P., Predicting quality and sensory attributes of pork using near-infrared hyperspectral imaging. Anal. Chim. Acta 719 (2012), 30–42.
7. Burges, C., A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Disc. 2:2 (1998), 121–167.
8. Cheng, X., Chen, Y.R., Tao, Y., Wang, C.Y., Kim, M.S., Lefcourt, A.M., A novel integrated PCA and FLD method on hyperspectral image feature extraction for cucumber chllling damage inspection. Trans. ASAE 47:4 (2004), 1313–1320.
9. Cubeddu, R., D'Andrea, C., Pifferi, A., Taroni, P., Torricelli, A., Valentini, G., Dover, C., Johnson, D., Ruiz-Altisent, M., Valero, C., Nondestructive quantification of chemical and physical properties of fruits by time-resolved reflectance spectroscopy in the wavelength range 650–1000 nm. Appl. Opt. 40:4 (2001), 538–543.
10. Galvao, R., Pimentel, M.F., Araujo, M., Yoneyama, T., Visani, V., Aspects of the successive projections algorithm for variable selection in multivariate calibration applied to plasma emission spectrometry. Anal. Chim. Acta 443:1 (2001), 107–115.
11. Galvao, R.K.H., Ugulino Araujo, M.C., Fragoso, W.D., Silva, E.C., Jose, G.E., Carreiro Soares, S.F., Paiva, H.M., A variable elimination method to improve the parsimony of MLR models using the successive projections algorithm. Chemometr. Intell. Lab. 92:1 (2008), 83–91.
12. Gowen, A.A., O'Donnell, C.P., Cullen, P.J., Downey, G., Frias, J.M., Hyperspectral imaging - an emerging process analytical tool for food quality and safety control. Trends Food Sci. Technol. 18:12 (2007), 590–598.
13. Kim, C.E., Digital disks. IEEE Trans. Pattern Anal. Mach. Intell. 6 (1984), 372–374.
14. Kim, M.S., 19 - Online screening of fruits and vegetables using hyperspectral line-scan imaging techniques. Bhunia, A.K., Kim, M.S., Taitt, C.R., (eds.) High Throughput Screening for Food Safety Assessment, 2015, Woodhead Publishing, 467–490.
15. Kurban, Y., Yunus, Q., Jiang, L., Weiming, Q., Comparison of fruit quality of normal and roughbark pear in persistent calyx and dropping calyx of korla fragrant pear. Xinjiang Agric. Sci. 6:49 (2012), 1028–1034.
16. Leiva-Valenzuela, G.A., Lu, R., Aguilera, J.M., Prediction of firmness and soluble solids content of blueberries using hyperspectral reflectance imaging. J. Food Eng. 115:1 (2013), 91–98.
17. Li, J., Huang, W., Zhao, C., Zhang, B., A comparative study for the quantitative determination of soluble solids content, pH and firmness of pears by Vis/NIR spectroscopy. J. Food Eng. 116:2 (2013), 324–332.
18. Liu, D., Qu, J., Sun, D., Pu, H., Zeng, X., Non-destructive prediction of salt contents and water activity of porcine meat slices by hyperspectral imaging in a salting process. Innov. Food Sci. Emerg. 20 (2013), 316–323.
19. Lu, R., Multispectral imaging for predicting firmness and soluble solids content of apple fruit. Postharvest Biol. Technol. 31:2 (2004), 147–157.
20. Lu, R., Peng, Y., Hyperspectral scattering for assessing Peach Fruit Firmness. Biosyst. Eng. 93:2 (2006), 161–171.
21. Ma, B.X., Ying, Y.B., Fragrant pear sexuality recognition with machine vision. Chen, Y.R., Meyer, G.E., Tu, S.I., (eds.) Proceedings of the Society of Photo-Optical Instrumentation Engineers (SPIE), 2006, SPIE-Int Soc Optical Engineering, Bellingham, A3811.
22. Mehl, P.M., Chen, Y.R., Kim, M.S., Chan, D.E., Development of hyperspectral imaging technique for the detection of apple surface defects and contaminations. J. Food Eng. 61:1 (2004), 67–81.
23. Merzlyak, M.N., Solovchenko, A.E., Gitelson, A.A., Reflectance spectral features and non-destructive estimation of chlorophyll, carotenoid and anthocyanin content in apple fruit. Postharvest Biol. Technol. 27:2 (2003), 197–211.
24. Noh, H.K., Lu, R., Hyperspectral laser-induced fluorescence imaging for assessing apple fruit quality. Postharvest Biol. Technol. 43:2 (2007), 193–201.
25. Pan, L., Zhang, Q., Zhang, W., Sun, Y., Hu, P., Tu, K., Detection of cold injury in peaches by hyperspectral reflectance imaging and artificial neural network. Food Chem. 192 (2016), 134–141.
26. Peng, Y., Lu, R., Prediction of apple fruit firmness and soluble solids content using characteristics of multispectral scattering images. J. Food Eng. 82:2 (2007), 142–152.
27. Pontes, M., Galvao, R., Araujo, M., Nogueira, P., Moreira, T., Neto, O., Jose, G.E., Saldanha, T., The successive projections algorithm for spectral variable selection in classification problems. Chemometr. Intell. Lab. 78:1–2 (2005), 11–18.
28. Qin, J., Burks, T.F., Ritenour, M.A., Bonn, W.G., Detection of citrus canker using hyperspectral reflectance imaging with spectral information divergence. J. Food Eng. 93:2 (2009), 183–191.
29. Rao, X., Yang, C.C., Ying, Y., Kim, M.S., Chan, D.E., Chao, K., Differentiation of deciduous-calyx and persistent-calyx pears using nir hyperspectral imaging analysis. Trans. ASABE 57:6 (2014), 1875–1883.
30. Xing, J., Jancsok, P., De Baerdemaeker, J., Stem-end/calyx identification on apples using contour analysis in multispectral images. Biosyst. Eng. 96:2 (2007), 231–237.