Near infrared hyperspectral imaging in quality and safety evaluation of cereals

Critical Reviews in Food Science and Nutrition

Volumn 58, Issue 4, 2018, Pages 575-590

  Sendin, Kate ^a   Williams, Paul J ^a   Manley, Marena ^a  

^a STELLENBOSCH UNIVERSITY   (South Africa)

Author keywords

barley; chemical imaging; chemometrics; maize; NIR spectroscopy; rice; wheat

Indexed keywords

CEREAL PRODUCTS; CHEMICAL ANALYSIS; FOOD PRODUCTS; GRADING; GRAIN (AGRICULTURAL PRODUCT); IMAGE PROCESSING; IMAGING TECHNIQUES; INDUSTRIAL RESEARCH; INFRARED DEVICES; NEAR INFRARED SPECTROSCOPY; QUALITY CONTROL; SPECTROSCOPY;

BARLEY; CHEMICAL IMAGING; CHEMOMETRICS; MAIZE; NIR SPECTROSCOPY; RICE; WHEAT;

HYPERSPECTRAL IMAGING;

ANALYTIC METHOD; BAKERY PRODUCT; BARLEY; CHEMOMETRICS; CLASSIFICATION; DIFFERENTIATION; FUNGAL DEVELOPMENT; IMAGING; INFRARED RADIATION; MAIZE; NONHUMAN; QUANTITATIVE STUDY; RICE; SAFETY; SPECTROSCOPY; WHEAT; FOOD ANALYSIS; FOOD GRAIN; NEAR INFRARED SPECTROSCOPY; PROCEDURES; QUALITY CONTROL;

EDIBLE GRAIN; FOOD ANALYSIS; QUALITY CONTROL; SPECTROSCOPY, NEAR-INFRARED;

EID: 85027119077     PISSN: 10408398     EISSN: 15497852     Source Type: Journal    
DOI: 10.1080/10408398.2016.1205548     Document Type: Article

References (76)

1. AACC International. Approved Methods of Analysis, 11th Ed. (2000). AACC International, St. Paul, MN, U.S.A.
2. Amigo, J. M., Martí, I., and Gowen, A., (2013). Hyperspectral imaging and chemometrics: a perfect combination for the analysis of food structure, composition and quality. In: Data Handling in Science and Technology, pp. 343–370. Elsevier Science. Amsterdam, Netherlands.
3. Archibald, D. D., Thai, C. N., and Dowell, F. E., (1998). Development of short-wavelength near-infrared spectral imaging for grain color classification. In: Photonics East (ISAM, VVDC, IEMB), pp. 189–198. International Society for Optics and Photonics.
4. Arngren, M., Hansen, P. W., Eriksen, B., Larsen, J., and Larsen, R., (2011). Analysis of pregerminated barley using hyperspectral image analysis. J. Agric. Food Chem. 59:11385–11394.
5. Baeten, V., Pierna, J. F., and Dardenne, P., (2007). Hyperspectrali maging techniques: An attractive solution for the analysis of biological and agricultural materials. In: Techniques and Applications of Hyperspectral Image Analysis, pp. 289–312. Grahn, H., and Geladi, P., Eds., John Wiley & Sons, Chichester, UK.
6. Barnes, R., Dhanoa, M., and Lister, S. J., (1989). Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Appl. Spectrosc. 43:772–777.
7. Bauriegel, E., Giebel, A., Geyer, M., Schmidt, U., and Herppich, W., (2011). Early detection of Fusarium infection in wheat using hyper-spectral imaging. Comput. Electron. Agric. 75:304–312.
8. Berman, M., Connor, P., Whitbourn, L., Coward, D., Osborne, B., and Southan, M., (2007). Classification of sound and stained wheat grains using visible and near infrared hyperspectral image analysis. J. Near Infrared Spectrosc. 15:351–358.
9. Blandino, M., Mancini, M. C., Peila, A., Rolle, L., Vanara, F., and Reyneri, A., (2010). Determination of maize kernel hardness: comparison of different laboratory tests to predict dry-milling performance. J. Sci. Food Agric. 90:1870–1878.
10. Burger, J., and Geladi, P., (2006). Hyperspectral NIR imaging for calibration and prediction: a comparison between image and spectrometer data for studying organic and biological samples. Analyst 131:1152–1160.
11. Chevallier, S., Bertrand, D., Kohler, A., and Courcoux, P., (2006). Application of PLS-DA in multivariate image analysis. J. Chemometr. 20:221.
12. Choudhary, R., Mahesh, S., Paliwal, J., and Jayas, D., (2009). Identification of wheat classes using wavelet features from near infrared hyperspectral images of bulk samples. Biosystems Eng. 102:115–127.
13. Cogdill, R. P., Hurburgh, C., Rippke, G. R., Bajic, S. J., Jones, R. W., McClelland, J. F., Jensen, T. C., and Liu, J., (2004). Single-kernel maize analysis by near-infrared hyperspectral imaging. Trans. ASAE. 47:311.
14. Cowe, I. A., and McNicol, J. W., (1985). The use of principal components in the analysis of near-infrared spectra. Appl. Spectrosc. 39:257–266.
15. Dale, L. M., Thewis, A., Boudry, C., Rotar, I., Dardenne, P., Baeten, V., and Pierna, J. A. F., (2013). Hyperspectral imaging applications in agriculture and agro-food product quality and safety control: A review. Appl. Spectrosc. Rev. 48:142–159.
16. Del Fiore, A., Reverberi, M., Ricelli, A., Pinzari, F., Serranti, S., Fabbri, A., Bonifazi, G., and Fanelli, C., (2010). Early detection of toxigenic fungi on maize by hyperspectral imaging analysis. Int. J. Food Microbiology. 144:64–71.
17. Delwiche, S. R., and Hruschka, W. R., (2000). Protein content of bulk wheat from near-infrared reflectance of individual kernels. Cereal Chem. 77:86–88.
18. Elmasry, G., Kamruzzaman, M., Sun, D.-W., and Allen, P., (2012). Principles and applications of hyperspectral imaging in quality evaluation of agro-food products: a review. Crit. Rev. Food Sci.Nutrition. 52:999–1023.
19. Elmasry, G., and Sun, D.-W., (2010). Principles of hyperspectral imaging technology. In: Hyperspectral Imaging for Food Quality Analysis and Control, pp. 3–44. Sun, D.-W., Ed., Elsevier. London, UK.
20. Elmasry, G., Sun, D.-W., and Allen, P., (2011). Non-destructive determination of water-holding capacity in fresh beef by using NIR hyperspectral imaging. Food Res. Int. 44:2624–2633.
21. Esbensen, K., and Geladi, P., (1989). Strategy of multivariate image analysis (MIA). Chemometr. Intell. Lab.Syst. 7:67–86.
22. Esbensen, K. H., Guyot, D., Westad, F., and Houmoller, L. P., (2002). Principal component analysis (PCA)—introduction. In: Multivariate data analysis—In practice: An introduction to multivariate data analysis and experimental design, pp. 19–74. Camo ASA. Oslo, Norway
23. Feng, Y.-Z., and Sun, D.-W., (2012). Application of hyperspectral imaging in food safety inspection and control: a review. Crit. Rev. Food Sci. Nutr. 52:1039–1058.
24. Firrao, G., Torelli, E., Gobbi, E., Raranciuc, S., Bianchi, G., and Locci, R., (2010). Prediction of milled maize fumonisin contamination by multispectral image analysis. J. Cereal Sci. 52:327–330.
25. Fisher, R. A., (1936). The use of multiple measurements in taxonomic problems. Ann. Eugenics 7:179–188.
26. Fox, G., and Manley, M., (2009). Hardness methods for testing maize kernels. J. Agric. Food Chem. 57:5647–5657.
27. Fox, G., and Manley, M., (2014). Applications of single kernel conventional and hyperspectral imaging near infrared spectroscopy in cereals. J. Sci. Food Agric. 94:174–179.
28. Giroux, M., and Morris, C., (1997). A glycine to serine change in puroindoline b is associated with wheat grain hardness and low levels of starch-surface friabilin. Theor.Appl.Genetics. 95:857–864.
29. Goetz, A. F., (2009). Three decades of hyperspectral remote sensing of the Earth: a personal view. Remote Sens. Environ. 113:5–16.
30. Goetz, A. F., Vane, G., Solomon, J. E., and Rock, B. N., (1985). Imaging spectrometry for earth remote sensing. Science. 228:1147–1153.
31. Gorretta, N., Roger, J., Aubert, M., Bellon-Maurel, V., Campan, F., and Roumet, P., (2006). Determining vitreousness of durum wheat kernels using near infrared hyperspectral imaging. J. Near Infrared Spectrosc. 14:231–239.
32. Gowen, A., O'Donnell, C., Cullen, P., Downey, G., and Frias, J., (2007). Hyperspectral imaging—an emerging process analytical tool for food quality and safety control. Trends Food Sci. Technol. 18:590–598.
33. Hege, E. K., O'Connell, D., Johnson, W., Basty, S., and Dereniak, E. L., (2004). Hyperspectral imaging for astronomy and space surviellance. In: Optical Science and Technology, SPIE's 48th Annual Meeting, pp. 380–391. International Society for Optics and Photonics, Denver, USA.
34. Jay, S. C., Lawrence, R. L., Repasky, K. S., and Rew, L. J., (2010). Detection of leafy spurge using hyper-spectral-spatial-temporal imagery. In: Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International, pp. 4374–4376. IEEE, Honolulu, USA.
35. Jovanov, P. T., Beljkaš, B., Matić, J., Milovanović, I., Kabić, D., and Sakač, M., (2008). Determination of total oil content in cereal products verification of the method and estimation of measurement uncertainty. Food Process., Qual. Saf. 35:189–195.
36. Keith, C. J., Repasky, K. S., Lawrence, R. L., Jay, S. C., and Carlsten, J. L., (2009). Monitoring effects of a controlled subsurface carbon dioxide release on vegetation using a hyperspectral imager. Int. J. Greenhouse Gas Control. 3:626–632.
37. Kim, K. S., Park, S. H., Choung, M. G., and Jang, Y. S., (2007). Use of near-infrared spectroscopy for estimating fatty acid composition in intact seeds of rapeseed. J. Crop Sci. Biotechnol. 10:13–18.
38. Kucheryavskiy, S., (2013). A new approach for discrimination of objects on hyperspectral images. Chemometr. Intell. Lab. Syst. 120:126–135.
39. Lin, L.H., Lu, F.M., and Chang, Y.C., (2006). Development of a near-infrared imaging system for determination of rice moisture. Cereal Chem. 83:498–504.
40. Lorente, D., Aleixos, N., Gómez-Sanchis, J., Cubero, S., García-Navarrete, O. L., and Blasco, J., (2012). Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment. Food Bioprocess Technol. 5:1121–1142.
41. Mahesh, S., Jayas, D., Paliwal, J., and White, N., (2015). Comparison of partial least squares regression (PLSR) and principal components regression (PCR) methods for protein and hardness predictions using the near-infrared (NIR) hyperspectral images of bulk samples of Canadian wheat. Food Bioprocess Technol. 8:31–40.
42. Mahesh, S., Manickavasagan, A., Jayas, D., Paliwal, J., and White, N., (2008). Feasibility of near-infrared hyperspectral imaging to differentiate Canadian wheat classes. Biosystems Eng. 101:50–57.
43. Manley, M., (2014). Near-infrared spectroscopy and hyperspectral imaging: non-destructive analysis of biological materials. Chem. Soc. Rev. 43:8200–8214.
44. Manley, M., Du Toit, G., and Geladi, P., (2011). Tracking diffusion of conditioning water in single wheat kernels of different hardnesses by near infrared hyperspectral imaging. Analytica Chimica Acta. 686:64–75.
45. Manley, M., Williams, P., Nilsson, D., and Geladi, P., (2009). Near infrared hyperspectral imaging for the evaluation of endosperm texture in whole yellow maize (Zea maize L.) kernels. J. Agric. Food Chem. 57:8761–8769.
46. McGoverin, C. M., Engelbrecht, P., Geladi, P., and Manley, M., (2011). Characterisation of non-viable whole barley, wheat and sorghum grains using near-infrared hyperspectral data and chemometrics. Anal. Bioanalytical Chem. 401:2283–2289.
47. McGoverin, C., and Manley, M., (2012). Classification of maize kernel hardness using near infrared hyperspectral imaging. J. Near Infrared Spectrosc. 20:529–535.
48. Munkvold, G. P., and Desjardins, A. E., (1997). Fumonisins in maize: Can we reduce their occurrence?. Plant Dis. 81:556–565.
49. Murphy, D., (2011). Plants and their exploitation by people. In: Plants, Biotechnology and Agriculture, pp. 1–20, CABI, Oxfordshire, U.K.
50. Næs, T., Isaksson, T., Fearn, T., and Davies, T., (2002). Data compression by PCR and PLS. In: A User-friendly Guide to Multivariate Calibration and Classification, pp. 27–38. NIR Publications, Chichester, UK.
51. Nansen, C., Kolomiets, M., and Gao, X., (2008). Considerations regarding the use of hyperspectral imaging data in classifications of food products, exemplified by analysis of maize kernels. J. Agric. Food Chem. 56:2933–2938.
52. Nansen, C., Zhao, G., Dakin, N., Zhao, C., and Turner, S. R., (2015). Using hyperspectral imaging to determine germination of native Australian plant seeds. J. Photochem. Photobiol. B: Biol. 145:19–24.
53. Patterson, M., and Brescia, A., (2010). Operation of small sensor payloads on tactical sized unmanned air vehicles. Aeronaut. J. 114:427–436.
54. Polerecky, L., Bissett, A., Al-Najjar, M., Faerber, P., Osmers, H., Suci, P. A., Stoodley, P., and de Beer, D., (2009). Modular spectral imaging system for discrimination of pigments in cells and microbial communities. Appl. Environ.Microbiol. 75:758–771.
55. Ravn, C., Skibsted, E., and Bro, R., (2008). Near-infrared chemical imaging (NIR-CI) on pharmaceutical solid dosage forms—comparing common calibration approaches. J. Pharmaceutical Biomed. Anal. 48:554–561.
56. Resmini, R., Kappus, M., Aldrich, W., Harsanyi, J., and Anderson, M., (1997). Mineral mapping with hyperspectral digital imagery collection experiment (HYDICE) sensor data at Cuprite, Nevada, USA. Int. J. Remote Sens. 18:1553–1570.
57. Singh, C. B., Jayas, D., Paliwal, J., and White, N., (2009a). Detection of insect-damaged wheat kernels using near-infrared hyperspectral imaging. J. Stored Prod. Res. 45:151–158.
58. Singh, C. B., Jayas, D. S., Paliwal, J., and White, N. D., (2009b). Detection of sprouted and midge-damaged wheat kernels using near-infrared hyperspectral imaging. Cereal Chem. 86:256–260.
59. Singh, C. B., Jayas, D. S., Paliwal, J., and White, N. D., (2010). Identification of insect-damaged wheat kernels using short-wave near-infrared hyperspectral and digital colour imaging. Comput. Electron. Agric. 73:118–125.
60. Siripatrawan, U., and Makino, Y., (2015). Monitoring fungal growth on brown rice grains using rapid and non-destructive hyperspectral imaging. Int. J. Food Microbiol. 199:93–100.
61. Spangler, L. H., Dobeck, L. M., Repasky, K. S., Nehrir, A. R., Humphries, S. D., Barr, J. L., Keith, C. J., Shaw, J. A., Rouse, J. H., and Cunningham, A. B., (2010). A shallow subsurface controlled release facility in Bozeman, Montana, USA, for testing near surface CO2 detection techniques and transport models. Environ. Earth Sci. 60:227–239.
62. Tallada, J. G., Palacios-Rojas, N., and Armstrong, P. R., (2009). Prediction of maize seed attributes using a rapid single kernel near infrared instrument. J. Cereal Sci. 50:381–387.
63. Thenkabail, P. S., (2012). Foreword. In: Hyperspectral Remote Sensing of Vegetation, pp. xi–xii. Thenkabail, P. S., Lyon, J. G., and Huete, A., Eds., CRC Press, Boca Raton, FL, USA.
64. Van Der Maarel, M. J., Van Der Veen, B., Uitdehaag, J. C., Leemhuis, H., and Dijkhuizen, L., (2002). Properties and applications of starch-converting enzymes of the α-amylase family. J. Biotechnol. 94:137–155.
65. Vermeulen, P., Pierna, J. F., Egmond, H. V., Dardenne, P., and Baeten, V., (2012). Online detection and quantification of ergot bodies in cereals using near infrared hyperspectral imaging. Food Additives Contam.: Part A. 29:232–240.
66. Vidal, M., and Amigo, J. M., (2012). Pre-processing of hyperspectral images. Essential steps before image analysis. Chemometr. Intell. Lab. Syst. 117:138–148.
67. Wallays, C., Missotten, B., De Baerdemaeker, J., and Saeys, W., (2009). Hyperspectral waveband selection for on-line measurement of grain cleanness. Biosyst. Eng. 104:1–7.
68. Wang, L., Liu, D., Pu, H., Sun, D.-W., Gao, W., and Xiong, Z., (2014a). Use of hyperspectral imaging to discriminate the variety and quality of rice. Food Anal. Methods. 8:515–523.
69. Wang, L., Sun, D.-W., Pu, H., and Zhu, Z., (2015). Application of hyperspectral imaging to discriminate the variety of maize seeds. Food Anal. Methods. 9:1–10.
70. Wang, W., Heitschmidt, G. W., Ni, X., Windham, W. R., Hawkins, S., and Chu, X., (2014b). Identification of aflatoxin B1 on maize kernel surfaces using hyperspectral imaging. Food Control 42:78–86.
71. Weinstock, B. A., Janni, J., Hagen, L., and Wright, S., (2006). 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. 60:9–16.
72. Williams, P. C., (1975). Application of near infrared reflectance spectroscopy to analysis of cereal grains and oilseeds. Cereal Chem. 52:561–567.
73. Williams, P. J., Geladi, P., Britz, T. J., and Manley, M., (2012). Investigation of fungal development in maize kernels using NIR hyperspectral imaging and multivariate data analysis. J. Cereal Sci. 55:272–278.
74. Williams, P.J., Geladi, P., Fox, G., and Manley, M., (2009). Maize kernel hardness classification by near infrared (NIR) hyperspectral imaging and multivariate data analysis. Analytica Chim. Acta. 653:121–130.
75. Yang, Y., Chai, R., and He, Y., (2012). Early detection of rice blast (pyricularia) at seedling stage in nipponbare rice variety using near-infrared hyper-spectral image. Afr. J. Biotechnol. 11:6809–6817.
76. Zhu, F., Zhang, D., He, Y., Liu, F., and Sun, D.-W., (2013). Application of visible and near infrared hyperspectral imaging to differentiate between fresh and frozen–thawed fish fillets. Food Bioprocess. Technol. 6:2931–2937.