Application of Visible and Near-Infrared Hyperspectral Imaging for Detection of Defective Features in Loquat

Food and Bioprocess Technology

Volumn 7, Issue 11, 2014, Pages 3077-3087

  Yu, Ke Qiang ^a   Zhao, Yan Ru ^a   Liu, Zi Yi ^a   Li, Xiao Li ^a,b   Liu, Fei ^a,b   He, Yong ^a,b  

^a ZHEJIANG UNIVERSITY   (China)

^b BEIJING HOSPITAL   (China)

Author keywords

Defective features; Hyperspectral imaging; Loquat; Minimum noise fraction rotation; Sensitive wavelengths

Indexed keywords

COHERENT SCATTERING; DEFECTS; DISCRIMINANT ANALYSIS; FEATURE EXTRACTION; FRUITS; IMPORTANCE SAMPLING; INFRARED DEVICES; LEAST SQUARES APPROXIMATIONS; SPECTROSCOPY;

CLASSIFICATION SYSTEM; DEFECTIVE FEATURES; DISCRIMINATION ACCURACY; LOQUAT; MINIMUM NOISE FRACTION; PARTIAL LEAST SQUARES DISCRIMINANT ANALYSES (PLSDA); SENSITIVE WAVELENGTHS; VISIBLE AND NEAR INFRARED;

HYPERSPECTRAL IMAGING;

EID: 84911005103     PISSN: 19355130     EISSN: 19355149     Source Type: Journal    
DOI: 10.1007/s11947-014-1357-z     Document Type: Article

References (55)

1. Abbott, J. A., Lu, R., Upchurch, B. L., & Stroshine, R. L. (2010). Technologies for nondestructive quality evaluation of fruits and vegetables. Horticultural Reviews, 20, 1–120.
2. Abdel-Nour, N., Ngadi, M., Prasher, S., & Karimi, Y. (2009). Prediction of egg freshness and albumen quality using visible/near infrared spectroscopy. Food and Bioprocess Technology, 4(5), 731–736.
3. Alexandrakis, D., Downey, G., & Scannell, A. G. M. (2009). Rapid non-destructive detection of spoilage of intact chicken breast muscle using near-infrared and Fourier transform mid-infrared spectroscopy and multivariate statistics. Food and Bioprocess Technology, 5(1), 338–347.
4. Antonucci, F., Pallottino, F., Paglia, G., Palma, A., D’Aquino, S., & Menesatti, P. (2010). Non-destructive estimation of mandarin maturity status through portable VIS-NIR spectrophotometer. Food and Bioprocess Technology, 4(5), 809–813.
5. Ballabio, D., & Consonni, V. (2013). Classification tools in chemistry. Part 1: linear models. PLS-DA. Analytical Methods, 5, 3790–3798.
6. Bulanon, D. M., Burks, T. F., Kim, D. G., & Ritenour, M. A. (2013). Citrus black spot detection using hyperspectral image analysis. Agricultural Engineering International: CIGR Journal, 15(3), 171–180.
7. Cao, S., Zheng, Y., Wang, K., Jin, P., & Rui, H. (2009). Methyl jasmonate reduces chilling injury and enhances antioxidant enzyme activity in postharvest loquat fruit. Food Chemistry, 115(4), 1458–1463.
8. Cao, F., Wu, D., & He, Y. (2010). Soluble solids content and pH prediction and varieties discrimination of grapes based on visible–near infrared spectroscopy. Computers and Electronics in Agriculture, 71, S15–S18.
9. Cen, H., Bao, Y., He, Y., & Sun, D. W. (2007). Visible and near infrared spectroscopy for rapid detection of citric and tartaric acids in orange juice. Journal of Food Engineering, 82(2), 253–260.
10. Chauchard, F., Cogdill, R., Roussel, S., Roger, J. M., & Bellon-Maurel, V. (2004). Application of LS-SVM to non-linear phenomena in NIR spectroscopy: development of a robust and portable sensor for acidity prediction in grapes. Chemometrics and Intelligent Laboratory Systems, 71(2), 141–150.
11. Chen, L., Wang, J., Ye, Z., Zhao, J., Xue, X., Vander Heyden, Y., et al. (2012). Classification of Chinese honeys according to their floral origin by near infrared spectroscopy. Food Chemistry, 135(2), 338–342.
12. Cho, B.-K., Kim, M. S., Baek, I.-S., Kim, D.-Y., Lee, W.-H., Kim, J., et al. (2013). Detection of cuticle defects on cherry tomatoes using hyperspectral fluorescence imagery. Postharvest Biology and Technology, 76, 40–49.
13. Darwin, C., & Beer, G. (1998). The origin of species. Oxford: Oxford University Press.
14. ElMasry, G., Wang, N., ElSayed, A., & Ngadi, M. (2007). Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry. Journal of Food Engineering, 81(1), 98–107.
15. ElMasry, G., Wang, N., & Vigneault, C. (2009). Detecting chilling injury in Red Delicious apple using hyperspectral imaging and neural networks. Postharvest Biology and Technology, 52(1), 1–8.
16. ElMasry, G., Sun, D.-W., & Allen, P. (2011). Non-destructive determination of water-holding capacity in fresh beef by using NIR hyperspectral imaging. Food Research International, 44(9), 2624–2633.
17. ElMasry, G., Sun, D. W., & Allen, P. (2013). Chemical-free assessment and mapping of major constituents in beef using hyperspectral imaging. Journal of Food Engineering, 117, 235–246.
18. Fernández Pierna, J. A., Vermeulen, P., Amand, O., Tossens, A., Dardenne, P., & Baeten, V. (2012). NIR hyperspectral imaging spectroscopy and chemometrics for the detection of undesirable substances in food and feed. Chemometrics and Intelligent Laboratory Systems, 117, 233–239.
19. Gonzalez, R. C., Woods, R. E., & Eddins, S. L. (2010). Digital image processing using MATLAB (3rd ed.). Upper Saddle River: Prentice Hall.
20. Green, A., Berman, M., Switzer, P., & Craig, M. D. (1998). A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Transactions on Geoscience and Remote Sensing, 26(1), 65–74.
21. Guo, Z., Huang, W., Chen, L., Zhao, C., & Peng, Y. (2013). Geographical classification of apple based on hyperspectral imaging. Proceedings of SPIE, 8721, 87211–87218.
22. Haff, R. P., Saranwong, S., Thanapase, W., Janhiran, A., Kasemsumran, S., & Kawano, S. (2013). Automatic image analysis and spot classification for detection of fruit fly infestation in hyperspectral images of mangoes. Postharvest Biology and Technology, 86, 23–28.
23. He, H.-J., Wu, D., & Sun, D.-W. (2013). Non-destructive and rapid analysis of moisture distribution in farmed Atlantic salmon (Salmo salar) fillets using visible and near-infrared hyperspectral imaging. Innovative Food Science and Emerging Technologies, 18, 237–245.
24. Huang, M., Wan, X., Zhang, M., & Zhu, Q. (2013). Detection of insect-damaged vegetable soybeans using hyperspectral transmittance image. Journal of Food Engineering, 116(1), 45–49.
25. Kennard, R. W., & Stone, L. A. (1969). Computer aided design of experiments. Technometrics, 11(1), 137–148.
26. Kong, W., Zhang, C., Liu, F., Nie, P., & He, Y. (2013). Rice seed cultivar identification using near-infrared hyperspectral imaging and multivariate data analysis. Sensors, 13(7), 8916–8927.
27. Li, H., Liang, Y., Xu, Q., & Cao, D. (2009). Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration. Analytica Chimica Acta, 648(1), 77–84.
28. Li, J., Rao, X., & Ying, Y. (2011). Detection of common defects on oranges using hyperspectral reflectance imaging. Computers and Electronics in Agriculture, 78(1), 38–48.
29. Li, H., Lee, W. S., Wang, K., Ehsani, R., & Yang, C. (2013a). Extended spectral angle mapping (ESAM)’ for citrus greening disease detection using airborne hyperspectral imaging. Precision Agriculture. doi:10.1007/s11119-11013-19325-11116.
30. Li, P. P., Cui, Y. J., Tian, Y. F., Zhang, F. N., Wang, X. X., & Su, S. (2013b). Automatic detection of kiwifruit defects based on near-infrared light source. International Federation for Information Processing, 2013(392), 189–198.
31. Lin, H., Zhao, J., Sun, L., Chen, Q., & Zhou, F. (2011). Freshness measurement of eggs using near infrared (NIR) spectroscopy and multivariate data analysis. Innovative Food Science and Emerging Technologies, 12(2), 182–186.
32. Liu, F., & He, Y. (2009). Discrimination of producing areas of Auricularia auricula using visible/near infrared spectroscopy. Food and Bioprocess Technology, 4(3), 387–394.
33. Liu, F., Jin, Z. L., Naeem, M. S., Tian, T., Zhang, F., He, Y., et al. (2010a). Applying near-infrared spectroscopy and chemometrics to determine total amino acids in herbicide-stressed oilseed rape leaves. Food and Bioprocess Technology, 4(7), 1314–1321.
34. Liu, F. J., Tu, K., Shao, X. F., Zhao, Y., Tu, S. C., Su, J., et al. (2010b). Effect of hot air treatment in combination with Pichia guilliermondii on postharvest anthracnose rot of loquat fruit. Postharvest Biological and Technology, 58(1), 65–71.
35. Liu, D., Sun, D.-W., & Zeng, X. A. (2014). Recent advances in wavelength selection techniques for hyperspectral image processing in the food industry. Food and Bioprocess Technology, 7(2), 307–323.
36. Lü, Q., & Tang, M. (2012). Detection of hidden bruise on kiwi fruit using hyperspectral imaging and parallelepiped classification. Procedia Environmental Sciences, 12, 1172–1179.
37. Magwaza, L. S., Opara, U. L., Nieuwoudt, H., Cronje, P. J. R., Saeys, W., & Nicolaï, B. (2011). NIR spectroscopy applications for internal and external quality analysis of citrus fruit—a review. Food and Bioprocess Technology, 5(2), 425–444.
38. Mendoza, F., Lu, R., Ariana, D., Cen, H., & Bailey, B. (2011). Integrated spectral and image analysis of hyperspectral scattering data for prediction of apple fruit firmness and soluble solids content. Postharvest Biology and Technology, 62, 149–160.
39. Menesatti, P., Zanella, A., D’Andrea, S., Costa, C., Paglia, G., & Pallottino, F. (2009). Supervised multivariate analysis of hyper-spectral NIR images to evaluate the starch index of apples. Food and Bioprocess Technology, 2(3), 308–314.
40. Moghaddam, M. T., Razavi, S. M. A., & Taghizadeh, M. (2013). Applications of hyperspectral imaging in grains and nuts quality and safety assessment: a review. Journal of Food Measurement and Characterization, 7(3), 129–140.
41. Nguyen Do Trong, N., Tsuta, M., Nicolaï, B. M., De Baerdemaeker, J., & Saeys, W. (2011). Prediction of optimal cooking time for boiled potatoes by hyperspectral imaging. Journal of Food Engineering, 105(4), 617–624.
42. Niphadkar, N. P., Burks, T. F., Qin, J., & Ritenour, M. A. (2013). Estimation of citrus canker lesion size using hyperspectral reflectance imaging. International Agricultural Engineering Journal, 22, 41–51.
43. Polder, G., Van Der Heijden, G. W. A. M., Keizer, L. C. P., & Young, I. T. (2003). Calibration and characterization of imaging spectrographs. Near Infrared Spectroscopy, 11(3), 193–210.
44. Shao, Y., Bao, Y., & He, Y. (2009). Visible/near-infrared spectra for linear and nonlinear calibrations: a case to predict soluble solids contents and pH Value in peach. Food and Bioprocess Technology, 4(8), 1376–1383.
45. Shao, Y., Cen, Y., He, Y., & Liu, F. (2011). Infrared spectroscopy and chemometrics for the starch and protein prediction in irradiated rice. Food Chemistry, 126(4), 1856–1861.
46. Wei, X., Liu, F., Qiu, Z., Shao, Y., & He, Y. (2013). Ripeness classification of astringent persimmon using hyperspectral imaging technique. Food and Bioprocess Technology. doi:10.1007/s11947-11013-11164-y.
47. Wu, D., & Sun, D.-W. (2013a). Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: a review—Part II: applications. Innovative Food Science & Emerging Technologies, 19, 15–28.
48. Wu, D., & Sun, D.-W. (2013b). Potential of time series-hyperspectral imaging (TS-HSI) for non-invasive determination of microbial spoilage of salmon flesh. Talanta, 111, 39–46.
49. Wu, D., Nie, P., He, Y., & Bao, Y. (2011). Determination of calcium content in powdered milk using near and mid-infrared spectroscopy with variable selection and chemometrics. Food and Bioprocess Technology, 5(4), 1402–1410.
50. Wu, D., Shi, H., Wang, S., He, Y., Bao, Y., & Liu, K. (2012). Rapid prediction of moisture content of dehydrated prawns using online hyperspectral imaging system. Analytica Chimica Acta, 726, 57–66.
51. Xing, J., & De Baerdemaeker, J. (2005). Bruise detection on ‘Jonagold’ apples using hyperspectral imaging. Postharvest Biology and Technology, 37(2), 152–162.
52. Xu, L., Wei, X., Zhou, X., Yu, D., & Zhang, J. (2013). Study on identification method of foreign fibers of seed cotton in hyper-spectral images based on minimum noise fraction. International Federation for Information Processing, 392, 166–176.
53. Yu, K., Zhao, Y., Li, X., Shao, Y., Zhu, F., & He, Y. (2014). Identification of crack features in fresh jujube using Vis/NIR hyperspectral imaging combined with image processing. Computer and Electronics Agriculture, 103, 1–10.
54. Zhang, X. L., & He, Y. (2013). Rapid estimation of seed yield using hyperspectral images of oilseed rape leaves. Industrial Crops and Products, 42, 416–420.
55. Zhang, R., Ying, Y., Rao, X., & Li, J. (2012). Quality and safety assessment of food and agricultural products by hyperspectral fluorescence imaging. Journal of the Science of Food and Agriculture, 92(12), 2397–2408.