Applications of hyperspectral imaging in grains and nuts quality and safety assessment: A review

Journal of Food Measurement and Characterization

Volumn 7, Issue 3, 2013, Pages 129-140

  Mohammadi Moghaddam, Toktam ^a   Razavi, Seyed M A ^a   Taghizadeh, Masoud ^a  

^a FERDOWSI UNIVERSITY OF MASHHAD   (Iran)

Author keywords

Grain; Hyperspectral imaging; Nut; Quality; Spectrum

Indexed keywords

CARDIO-VASCULAR DISEASE; FUNCTIONAL INFORMATION; HYPERSPECTRAL IMAGING; NUT; NUTRIENT-DENSE FOODS; QUALITY AND SAFETIES; QUALITY PARAMETERS; SPECTRUM;

CHEMICAL ANALYSIS; DAMAGE DETECTION; DISEASES; GRAIN (AGRICULTURAL PRODUCT); IMAGE QUALITY; SAFETY ENGINEERING; SPECTROSCOPY; UNSATURATED FATTY ACIDS;

QUALITY CONTROL;

EID: 84883654776     PISSN: 21934126     EISSN: 21934134     Source Type: Journal    
DOI: 10.1007/s11694-013-9148-1     Document Type: Article

References (63)

1. ICC: International association for cereal science and technology (2013). http://www. icc. or. at/working_groups/whole_grain.
2. J. Slavin, Whole grains and human health. Nutr. Res. Rev. 17, 99-110 (2004).
3. S. B. Eaton, M. Konner, Paleolithic nutrition. A consideration of its nature and current implications. N. Engl. J. Med. 312, 283-289 (1985).
4. M. L. Dreher, C. V. Maher, P. Kearney, The traditional and emerging role of nuts in healthful diets. Nutr. Rev. 54, 241-245 (1996).
5. American Heart Association, American Heart Association Cookbook, 5th edn. (Times Books, New York, 1991).
6. K. Alexiadou, N. Katsilambros, Nuts: Anti-atherogenic food? Eur. J. Intern. Med. 22, 141-146 (2011).
7. Dole Food Company Inc., The Mayo Clinic, UCLA Center for Human Nutrition, High-protein foods: includes: poultry, eggs, meat, fish, shellfish, legumes, nuts & seeds, in Encyclopedia of Foods: A Guide to Healthy Nutrition (Elsevier, 2002), pp. 290-342.
8. J. Sabate, E. Ros, J. Salas-Salvado, Nuts: nutrition and health outcomes. Br. J. Nutr. 96(2), S1-S2 (2006).
9. H. Cen, Y. He, Theory and application of near infrared reflectance spectroscopy in determination of food quality. Trends Food Sci. Tech. 18, 72-83 (2007).
10. R. Lu, Y. Chen, Hyperspectral imaging for safety inspection of food and agricultural products. SPIE 3544, 121-133 (1998).
11. Y. R. Chen, Classifying diseased poultry carcasses by visible and near-IR reflectance spectroscopy. Optic in Agriculture and Forestry. SPIE 1835, 46-55 (1992).
12. Y. R. Chen, B. Park, R. W. Huffman, M. Nguyen, Classification of on-line poultry carcasses with backpropagation neural networks. J. Food Process Eng. 21(1), 33-48 (1998).
13. B. G. Osborne, T. Fearn, P. H. Hindle, Practical NIR Spectroscopy with Applications in Food and Beverage Analysis (Longman Scientific & Technical, Harlow, 1993).
14. P. C. Williams, K. H. Norris (eds.), Near-infrared Technology in the Agricultural and Food Industries (AACC, St. Paul, 1987).
15. A. A. Gowen, C. P. O'Donnell, P. J. Cullen, G. Downey, J. M. Frias, Hyperspectral imaging-an emerging process analytical tool for food quality and safety control. Trends Food Sci. Tech. 18, 590-598 (2007).
16. A. Shwetank, J. Kamal, K. J. Bhatia, Review of rice crop identification and classification using hyper-spectral image processing system. Int. J. Comput. Sci. Commun. 1(1), 253-258 (2010).
17. G. Elmasry, D. W. Sun, Principles of hyperspectral imaging technology, in Hyperspectral Imaging for Food Quality Analysis and Control, ed. by D.-W. Sun (Academic Press, San Diego, 2010), pp. 3-43.
18. N. Gat, S. Subramanian, Spectral Image: Technology and Applications, Hyperspectrum News Letter (3, 1, Opto-Knowledge stems, Inc., Torrance, 1997).
19. A. F. H. Goetz, G. Vane, T. E. Solomon, B. N. Rock, Imaging spectrometry for earth remote sensing. Science 228, 1147-1153 (1985).
20. B. Bezard, C. A. Griffith, D. M. Kelly, J. H. Lacy, T. Greatthouse, G. Orton, Thermal infrared image spectroscopy of Shoemaker-Levy 9 impact sites: temperature and HCN retrieval. ICARUS 125(1), 94-120 (1997).
21. G. Bellucci, V. Formisano, Regional mapping of planetaiy surfaces with imaging spectroscopy. Planet. Space Sci. 45(11), 1371-1381 (1997).
22. E. Hege, D. O'Connell, W. Johnson, S. Basty, E. Dereniak, Hyperspectral imaging for astronomy and space surveillance. Proc. SPIE 5159, 380-391 (2003).
23. K. S. Wood, A. M. Gulian, G. G. Fritz, D. van Vechten, A QVD detector for focal plane hyperspectral imaging in astronomy. Bull. Am. Astron. Soc. 34, 1241 (2002).
24. J. W. Boardman, F. A. Kruse, Automated spectral analysis: a geologic example using AVIRIS data, north Grapevine Moutains, Nevada, in Proceedings of the Thematic Conference on Geologic Remote Sensing, 1-407-1-418 (Environmental Research Institute of Michigan, Ann Arbor 1994).
25. S. Monteiro, Y. Minekawa, Y. Kosugi, T. Akazawa, K. Oda, Prediction of sweetness and amino acid content in soybean crops from hyperspectral imagery. ISPRS J. Photogramm. Remote Sens. 62(1), 2-12 (2007).
26. V. SmailV, A. Fritz, D. Wetzel, Chemical imaging of intact seeds with NIR focal plane array assists plant breeding. Vib. Spectrosc. 2, 215-221 (2006).
27. Y. Uno, S. Prasher, R. Lacroix, P. Goel, Y. Karimi, A. Viau et al., Artificial neural networks to predict corn yield from compact airborne spectrographic imager data. Comput. Electron. Agric. 47(2), 149-161 (2005).
28. R. C. Lyon, D. S. Lester, E. N. Lewis, E. Lee, L. X. Yu, E. H. Jefferson et al., Near-infrared spectral imaging for quality assurance of pharmaceutical products: analysis of tablets to assess powder blend homogeneity. AAPS Pharm. Sci. Tech. 3(3), 17 (2002).
29. O. Rodionova, L. Houmøller, A. Pomerantsev, P. Geladi, J. Burger, V. Dorofeyev et al., NIR spectrometry for counterfeit drug detection: a feasibility study. Anal. Chim. Acta 549(1-2), 151-158 (2005).
30. Y. Roggo, A. Edmond, P. Chalus, M. Ulmschneider, Infrared hyperspectral imaging for qualitative analysis of pharmaceutical solid forms. Anal. Chim. Acta 535(1-2), 79-87 (2005).
31. D. Ferris, R. Lawhead, E. Dickman, N. Holtzapple, J. Miller, S. Grogan et al., Multimodal hyperspectral imaging for the noninvasive diagnosis of cervical neoplasia. J. Low. Genit. Tract Dis. 5(2), 65-72 (2001).
32. D. Kellicut, J. Weiswasser, S. Arora, J. Freeman, R. Lew, C. Shuman et al., Emerging technology: hyperspectral imaging. Perspect. Vasc. Surg. Endovasc. Ther. Perspect. 16(1), 53-57 (2004).
33. G. Zheng, Y. Chen, X. Intes, B. Chance, J. D. Glickson, Contrast-enhanced near-infrared (NIR) optical imaging for subsurface cancer detection. J. Porphyrins Phthalocyanines 8(9), 1106-1117 (2004).
34. C. D. Arvanitopoulos, J. L. Koenig, Infrared spectral imaging of the interphase of epoxy-glass flber-reinforced composites under wet conditions. Appl. Spectrosc. 50(1), 11-18 (1996).
35. V. B. Broek, W. D. Wienke, W. J. Melssen, C. W. A. D. Crom, L. Buydens, Identification of plastics among nonplastics in mixed wase by remote sensing near-infrared imaging spectroscopy. 1. Image improvement and analysis by singular value decomposition. Anal. Chem. 67(20), 3753-3759 (1995).
36. W. D. Wienke, V. B. Broek, L. Buydens, Identification of plastics among nonplastics in mixed waste by remote sensing near-infrared imaging spectroscopy, 2. Multivariate image rank analysis for rapid classification. Anal. Chem. 67(20), 3760-3766 (1995).
37. W. D. Wienke, V. B. Broek, W. J. Melssen, L. Buydens, R. Feldhoff, T. Huth-Fehre, T. Kantimm, F. Winter, K. Camman, Near-infrared imaging spectroscopy and image rank analysis for remote identification of plastics in mixed waste. Fresenius J. Anal. Chem. 354(7-8), 823-828 (1996).
38. J. Qin, Hyperspectral imaging instruments, in Hyperspectral Imaging for Food Quality Analysis and Control, ed. by D.-W. Sun (Academic Press, San Diego, 2010), pp. 129-172.
39. E. Estanga, Visible and hyperspectral imaging systems for the detection and discrimination of mechanical and microbiological damage of mushrooms. Ph. D. thesis, School of Food Science and Environmental Health, College of Science, Dublin Institute of Technology, Ireland, 2010.
40. D. D. Archibald, C. N. Thai, F. E. Dowell, Development of short wavelength near-infrared spectral imaging for grain color classification. in Precision Agriculture and Biological Quality, Proceedings of SPIE, vol 3543, 1998, ed. by G-E Meyer, J-A DeShazer, pp. 189-198.
41. C. B. Singh, D. S. Jayas, J. Paliwal, N. D. G. White, Fungal detection in wheat using near infrared hyperspectral imaging. Trans. ASABE 50(6), 2171-2176 (2007).
42. S. Mahesh, A. Manickavasagan, D. S. Jayas, J. Paliwal, N. D. G. White, Feasibility of near-infrared hyperspectral imaging to differentiate Canadian wheat classes. Biosyst. Eng. 101, 50-57 (2008).
43. J. Xing, S. Symons, M. Shahin, D. Hatcher, Detection of sprout damage in Canada Western Red Spring wheat with multiple wavebands using visible/near-infrared hyperspectral imaging. Biosyst. Eng. 106, 188-194 (2010).
44. S. R. Delwiche, M. S. Kim, Hyperspectral imaging for detection of scab in wheat. in Proceedings of SPIE. Biological Quality and Precision Agriculture II, vol 4203, 2000, ed. by J-A DeShazer, G-E Meyer, pp. 13-20, Boston, Massachusetts, 6-8 November 2000.
45. S. R. Delwiche, I. C. Yang, M. S. Kim, Hyperspectral imaging for detection of black tip damage in wheat kernels. Proc. SPIE 7315, 731508 (2009).
46. N. Gorretta, J. M. Roger, M. Aubert, V. Bellon-Maurel, F. Campan, P. Roumet, Determining vitreousness of durum wheat kernels using near infrared hyperspectral imaging. J. Near Infrared Spectrosc. 14(4), 231-239 (2006).
47. C. B. Singh, D. S. Jayas, J. Paliwal, N. D. G. White, Identification of insect-damaged wheat kernels using short-wave near-infrared hyperspectral and digital colour imaging. Comput. Electron. Agric. 73, 118-125 (2010).
48. A. Berman, P. M. Connor, L. B. Whitbourn, D. A. Coward, B. G. Osborne, M. D. Southan, Classification of sound and stained wheat grains using visible and near infrared hyperspectral image analysis. J. Near Infrared Spectrosc. 15, 351-358 (2007).
49. M. A. Shahin, S. J. Symons, Detection of hard vitreous and starchy kernels in amber durum wheat samples using hyperspectral imaging. NIR News 19(5), 16-18 (2008).
50. M. A. Shahin, S. J. Symons, Detection of fusarium damaged kernels in Canada Western Red Spring wheat using visible/nearinfrared hyperspectral imaging and principal component analysis. Comput. Electron. Agric. 75(1), 107-112 (2011).
51. M. A. Shahin, S. J. Symons, Detection of fusarium damage in Canadian wheat using visible/near-infrared hyperspectral imaging. Food Meas. 6, 3-11 (2012).
52. R. P. Cogdill, C. R. Hurburgh, G. R. Rippke, Single-kernel maize analysis by near-infrared hyperspectral imaging. Am. Soc. Agric. Eng. 47(1), 311-320 (2004).
53. B. A. Weinstock, J. Janni, L. Hagen, S. Wright, Prediction of oil and oleic acid concentrations in individual corn (Zea mays L.) kernels using near-infrared reflectance hyperspectral imaging and multivariate analysis. Appl. Spectrosc. 60(1), 9-16 (2006).
54. P. Williams, P. Geladi, G. Fox, M. Manley, Maize kernel hardness classification by near infrared (NIR) hyperspectral imaging and multivariate data analysis. Anal. Chim. Acta 653, 121-130 (2009).
55. X. Zhang, F. Liu, Y. He, X. Li, Application of hyperspectral imaging and chemometric calibrations for variety discrimination of maize seeds. Sensors 12, 17234-17246 (2012).
56. L. Zhan-yu, H. Jing-feng, T. Rong-xiang, Characterizing and estimating fungal disease severity of rice brown spot with hyperspectral reflectance data. Rice Sci. 15(3), 232-242 (2008).
57. H. Wang, J. Chen, H. Lin, D. Yuan, Research on effectiveness of hyperspectral data on identifying rice of different genotypes. Remote Sens. Lett. 1(4), 223-229 (2011).
58. M. Arngren, P. W. Hansen, B. Eriksen, J. Larsen, R. Larsen, Analysis of pregerminated barley using hyperspectral image analysis. J. Agric. Food Chem. 59, 11385-11394 (2011).
59. B. Zhu, L. Jiang, F. Jin, L. Qin, A. Vogel, Y. Tao, Walnut shell and meat differentiation using fluorescence hyperspectral imagery with ICA-kNN optimal wavelength optimal wavelength selection. Sens. Instrum. Food Qual. Saf. 1, 123-131 (2007).
60. L. Jiang, B. Zhu, X. Rao, G. Berney, Y. Tao, Discrimination of black walnut shell and pulp in hyperspectral fluorescence imagery using Gaussian kernel function approach. J. Food Eng. 81, 108-117 (2007).
61. S. Nakariyakul, D. P. Casasent, Classification of internally damaged almond nuts using hyperspectral imagery. J. Food Eng. 103, 62-67 (2011).
62. H. Kalkan, P. Beriat, Y. Yardimci, T. C. Pearson, Detection of contaminated hazelnuts and ground red chili pepper flakes by multispectral imaging. Comput. Electron. Agric. 77, 28-34 (2011).
63. N. Wang, G. ElMasry, S. Sevakarampalayam, J. Qiao, Spectral imaging techniques for food quality evaluation. Stewart Postharvest Rev. 1, 1 (2007).