Near-Infrared spectroscopy and hyperspectral imaging for non-destructive quality assessment of cereal grains

Applied Spectroscopy Reviews

Volumn 53, Issue 8, 2018, Pages 667-687

  Caporaso, Nicola ^a,b   Whitworth, Martin B ^b   Fisk, Ian D ^a  

^a UNIVERSITY OF NOTTINGHAM   (United Kingdom)

^b CAMPDEN BRI   (United Kingdom)

Author keywords

cereal quality; Hyperspectral chemical imaging; NIR spectral imaging; non destructive analysis; single grains analysis; wheat

Indexed keywords

CHEMICAL ANALYSIS; DAMAGE DETECTION; GRAIN (AGRICULTURAL PRODUCT); INFRARED DEVICES; NEAR INFRARED SPECTROSCOPY; SPECTRUM ANALYSIS;

CEREAL QUALITY; CHEMICAL IMAGING; NON-DESTRUCTIVE ANALYSIS; SINGLE GRAINS; SPECTRAL IMAGING; WHEAT;

HYPERSPECTRAL IMAGING;

EID: 85049695598     PISSN: 05704928     EISSN: 1520569X     Source Type: Journal    
DOI: 10.1080/05704928.2018.1425214     Document Type: Review

References (90)

1. Osborne, B. G., Fearn, T., and Hindle, P. H., (1993) Practical NIR spectroscopy with applications in food and beverage analysis. Longman Sci. Tech. Harlow, UK, p. 227. ISBN: 0582099463.
2. 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. Tech. 18: 590–598.
3. 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. Nutr. 52: 999–1023.
4. Wu, D., and Sun, D.-W., (2013) Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: A review—Part I: Fundamentals. Innovative Food Sci. Emerg. Technol. 19: 1–14.
5. Huang, H., Liu, L., and Ngadi, M. O., (2014) Recent developments in hyperspectral imaging for assessment of food quality and safety. Sensors 14: 7248–7276.
6. Vidal, M., and Amigo, J. M., (2012) Pre-processing of hyperspectral images. Essential steps before image analysis. Chemom. Intell. Lab. Syst. 117: 138–148.
7. 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.
8. 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 Tech. 5: 1121–1142.
9. Ferrari, C., Foca, G., and Ulrici, A., (2013) Handling large datasets of hyperspectral images: Reducing data size without loss of useful information. Anal. Chim. Acta 802: 29–39.
10. Raychaudhuri, B., (2016) Imaging spectroscopy: Origin and future trends. Appl. Spectrosc. Rev. 51: 23–35.
11. Norris, K., (1982) Use of Near-Infrared Reflectance Spectroscopy and Dye-binding techniques for estimating protein in oat groats. Cereal Chem. 59: 333–335.
12. Downey, G., and Byrne, S., (1983) Determination of protein and moisture in ground wheat by near infra-red reflectance spectroscopy. Irish J. Food Sci. Tech. 135–146.
13. Williams, P. C., Stevenson, S. G., Starkey, P. M., and Hawtin, G. C., (1978) The application of near-infrared reflectance spectroscopy to protein‐testing in pulse breeding programmes. J. Sci. Food Agric. 29: 285–292.
14. Williams, P. C., and Sobering, D., (1993) Comparison of commercial near infrared transmittance and reflectance instruments for analysis of whole grains and seeds. J. Near Infrared Spectro. 1: 25–32.
15. Ozaki, Y., McClure, W. F., and Christy, A. A., (2006) Near-infrared spectroscopy in food science and technology. New York: John Wiley & Sons.
16. Delwiche (1995) Single wheat kernel analysis by near-infrared transmittance: Protein content. Cereal Chem. 72: 11–16.
17. Nielsen, J. P., Pedersen, D. K., and Munck, L., (2003) Development of nondestructive screening methods for single kernel characterization of wheat. Cereal Chem. 80: 274–280.
18. Williams, P., Geladi, P., Fox, G., and Manley, M., (2009) Maize kernel hardness classification by near-infrared (NIR) hyperspectral imaging and multivariate data analysis. Anal. Chim. Acta 653: 121–130.
19. Delwiche, S. R., (1998) Protein content of single kernels of wheat by near-infrared reflectance spectroscopy. J. Cereal Sci. 27: 241–254.
20. Delwiche, S., and Hruschka, W., (2000) Protein content of bulk wheat from near-infrared reflectance of individual kernels. Cereal Chem. 77: 86–88.
21. Halsey, S. A., (1987) Analysis of whole barley kernels using near-infrared reflectance spectroscopy. J. Inst. Brew. 93: 461–464.
22. Fox, G., Onley‐Watson, K., and Osman, A., (2002) Multiple linear regression calibrations for barley and malt protein based on the spectra of hordein. J. Inst. Brew. 108: 155–159.
23. Fox, G., Kelly, A., Sweeney, N., and Hocroft, D., (2011) Development of a single kernel NIR barley protein calibration and assessment of variation in protein on grain quality. J. Inst. Brew. 117: 582–586.
24. Tarr, A., Diepeveen, D., and Appels, R., (2012) Spectroscopic and chemical fingerprints in malted barley. J. Cereal Sci. 56: 268–275.
25. Han, Z., Cai, S., Zhang, X., Qian, Q., Huang, Y., Dai, F., and Zhang, G., (2017) Development of predictive models for total phenolics and free p-coumaric acid contents in barley grain by near-infrared spectroscopy. Food Chem. 227: 342–348.
26. Fontaine, J., Schirmer, B., and Hörr, J., (2002) Near-infrared reflectance spectroscopy (NIRS) enables the fast and accurate prediction of essential amino acid contents. 2. Results for wheat, barley, corn, triticale, wheat bran/middlings, rice bran, and sorghum. J. Agric. Food Chem. 50: 3902–3911.
27. Manley, M., McGoverin, C. M., Snyders, F., Muller, N., Botes, W. C., and Fox, G. P., (2013) Prediction of triticale grain quality properties, based on both chemical and indirectly measured reference methods, using near-infrared spectroscopy. Cereal Chem. 90: 540–545.
28. Igne, B., Gibson, L., Rippke, G., Schwarte, A., and Hurburgh, C., (2007) Triticale moisture and protein content prediction by near-infrared spectroscopy (NIRS). Cereal Chem. 84: 328–330.
29. Serranti, S., Cesare, D., Marini, F., and Bonifazi, G., (2013) Classification of oat and groat kernels using NIR hyperspectral imaging. Talanta 103: 276–284.
30. Manley, M., McGoverin, C. M., Engelbrecht, P., and Geladi, P., (2012) Influence of grain topography on near-infrared hyperspectral images. Talanta 89: 223–230.
31. Pedersen, D. K., Martens, H., Nielsen, J. P., and Engelsen, S. B., (2002) Near-infrared absorption and scattering separated by extended inverted signal correction (EISC): Analysis of near-infrared transmittance spectra of single wheat seeds. Appl. Spectrosc. 56: 1206–1214.
32. Rinnan, Å., van den Berg, F., and Engelsen, S. B., (2009) Review of the most common pre-processing techniques for near-infrared spectra. Trends Anal. Chem. 28: 1201–1222.
33. Ravikanth, L., Singh, C. B., Jayas, D. S., and White, N. D., (2015) Classification of contaminants from wheat using near-infrared hyperspectral imaging. Biosystems Eng. 135: 73–86.
34. 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.
35. 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 Spectro. 14: 231.
36. Shahin, M. A., and Symons, S. J., (2008) Detection of hard vitreous and starchy kernels in amber durum wheat samples using hyperspectral imaging (GRL Number M306). NIR News 19: 16–18.
37. Caporaso, N., Whitworth, M. B., and Fisk, I. D., (2017) Protein content prediction in single wheat kernels using hyperspectral imaging. Food Chem. 240: 32–42.
38. Mares, D., and Mrva, K., (2014) Wheat grain preharvest sprouting and late maturity alpha-amylase. Planta 240: 1167–1178.
39. Shafqat, S., (2013) Effect of different sprouting conditions on alpha amylase activity, functional properties of wheat flour and on shelf-life of bread supplemented with sprouted wheat. in, University of Guelph, Guelph, Ontario, Canada, p. 120.
40. Koç, H., Smail, V. W., and Wetzel, D. L., (2008) Reliability of InGaAs focal plane array imaging of wheat germination at early stages. J. Cereal Sci. 48: 394–400.
41. Xing, J., Van Hung, P., Symons, S., Shahin, M., and Hatcher, D., (2009) Using a short wavelength infrared (SWIR) hyperspectral imaging system to predict alpha amylase activity in individual Canadian western wheat kernels. Sensing Instrum. Food Qual. Saf. 3: 211–218.
42. Xing, J., Symons, S., Shahin, M., and Hatcher, D., (2010) Detection of sprout damage in Canada Western Red Spring wheat with multiple wavebands using visible/near-infrared hyperspectral imaging. Biosystems Eng. 106: 188–194.
43. Xing, J., Symons, S., Hatcher, D., and Shahin, M., (2011) Comparison of short-wavelength infrared (SWIR) hyperspectral imaging system with an FT-NIR spectrophotometer for predicting alpha-amylase activities in individual Canadian Western Red Spring (CWRS) wheat kernels. Biosystems Eng. 108: 303–310.
44. Caporaso, N., Whitworth, M. B., and Fisk, I. D., (2017) Application of calibrations to hyperspectral images of food grains: Example for wheat falling number. J. Spectral Imaging 6: 1–15.
45. 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.
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. Sá, R. M., and Palmer, G., (2006) Analysis of β‐Glucan in single grains of barley and malt using NIR‐spectroscopy. J. Inst. Brew. 112: 9–16.
48. Schmidt, J., Gergely, S., Schönlechner, R., Grausgruber, H., Tömösközi, S., Salgó, A., and Berghofer, E., (2009) Comparison of different types of NIR instruments in ability to measure β-glucan content in naked barley. Cereal Chem. 86: 398–404.
49. Bellato, S., Frate, V. D., Redaelli, R., Sgrulletta, D., Bucci, R., Magrì, A. D., and Marini, F., (2011) Use of near infrared reflectance and transmittance coupled to robust calibration for the evaluation of nutritional value in naked oats. J. Agric. Food Chem. 59: 4349–4360.
50. Sohn, M., Himmelsbach, D. S., Barton, F. E., Griffey, C. A., Brooks, W., and Hicks, K. B., (2008) Near-infrared analysis of whole kernel barley: Comparison of three spectrometers. Appl. Spectrosc. 62: 427–432.
51. Vermeulen, P., Flémal, P., Pigeon, O., Dardenne, P., Fernández Pierna, J., and Baeten, V., (2017) Assessment of pesticide coating on cereal seeds by near infrared hyperspectral imaging. J. Spectral Imaging 6: 1–7.
52. Kawamura, S., Natsuga, M., Takekura, K., and Itoh, K., (2003) Development of an automatic rice: Quality inspection system. Comput. Electron. Agric. 40: 115–126.
53. 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.
54. Osborne, B., Mertens, B., Thompson, M., and Fearn, T., (1993) The authentication of Basmati rice using near infrared spectroscopy. J. Near Infrared Spectro. 1: 77–83.
55. Deng, X., Zhu, Q., and Huang, M., Semi-supervised classification of rice seed based on hyperspectral imaging technology. Am. Soc. Agric. Biolo. Eng. 141912601.
56. Sun, J., Jin, X., Mao, H., Wu, X., and Yang, N., (2014) Application of hyperspectral imaging technology for detecting adulterate rice. Trans. Chin. Soc. Agric. Eng. 30: 301–307.
57. Zhao, M., (2014) Hyperspectral visible-near infrared imaging for the detection of waxed rice, in: SPIE/COS Photonics Asia. Int. Soc. Opt. Photonics 927622-927622-927613.
58. Li, B., Zhao, M., Zhou, Y., Hou, B., and Zhang, D., (2016) Detection of waxed rice using Visible-near infrared hyperspectral imaging. J. Food Nutr. Res. 4: 267–275.
59. Sumriddetchkajorn, S., Suwansukho, K., and Buranasiri, P., (2010) Two-wavelength spectral imaging-based Thai rice breed identification, in: SPIE Photonics Europe. Int. Soc. Opt. Photonics 77150I-77150I-77110.
60. Chen, W.-T., and Kuo, Y.-F., (2014) Observation and measurement of residual bran on milled rice using hyperspectral imaging. Cereal Chem. 91: 566–571.
61. Wang, L., Liu, D., Pu, H., Sun, D.-W., Gao, W., and Xiong, Z., (2015) Use of hyperspectral imaging to discriminate the variety and quality of rice. Food Anal. Methods 8: 515–523.
62. Kong, W., Zhang, C., Liu, F., Nie, P., and He, Y., (2013) Rice seed cultivar identification using near-infrared hyperspectral imaging and multivariate data analysis. Sensors 13: 8916–8927.
63. Juliano, B. O., and Perez, C. M., (1983) Major factors affecting cooked milled rice hardness and cooking time. J. Texture Stud. 14: 235–243.
64. Sohn, M., Barton, F. E., McClung, A. M., and Champagne, E. T., (2004) Near-infrared spectroscopy for determination of protein and amylose in rice flour through use of derivatives. Cereal Chem. 81: 341–344.
65. Bao, J., Shen, Y., and Jin, L., (2007) Determination of thermal and retrogradation properties of rice starch using near-infrared spectroscopy. J. Cereal Sci. 46: 75–81.
66. Wu, J., and Shi, C., (2007) Calibration model optimization for rice cooking characteristics by near infrared reflectance spectroscopy (NIRS). Food Chem. 103: 1054–1061.
67. Xie, L., Tang, S., Chen, N., Luo, J., Jiao, G., Shao, G., Wei, X., and Hu, P., (2014) Optimisation of near-infrared reflectance model in measuring protein and amylose content of rice flour. Food Chem. 142: 92–100.
68. Xie, L., He, X., Duan, B., Tang, S., Luo, J., Jiao, G., Shao, G., Wei, X., Sheng, Z., and Hu, P., (2015) Optimization of Near-infrared reflectance model in measuring gelatinization characteristics of rice flour with a rapid viscosity analyzer (RVA) and differential scanning calorimeter (DSC). Cereal Chem. 92: 522–528.
69. Cogdill, R. P., Hurburgh, C., Rippke, G. R., Bajic, S. J., Jones, R. W., McClelland, J. F., Jensen, T. C., and Liu, J., (2001) Single-kernel maize analysis by near-infrared hyperspectral imaging. Iowa: Iowa State University.
70. 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.
71. 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: 225–234.
72. Zhou, X., Yang, Z., Haughey, S. A., Galvin-King, P., Han, L., and Elliott, C. T., (2014) Classification the geographical origin of corn distillers' dried grains with solubles by near infrared reflectance spectroscopy combined with chemometrics: A feasibility study. Food Chem. 189: 13–18.
73. 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.
74. Fox, G., and Manley, M., (2009) Hardness methods for testing maize kernels. J. Agric. Food Chem. 57: 5647–5657.
75. 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.
76. Berardo, N., Pisacane, V., Battilani, P., Scandolara, A., Pietri, A., and Marocco, A., (2005) Rapid detection of kernel rots and mycotoxins in maize by near-infrared reflectance spectroscopy. J. Agric. Food Chem. 53: 8128–8134.
77. Williams, P., Geladi, P., Britz, T., 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.
78. Kautzman, M. E., Wickstrom, M. L., and Scott, T. A., (2015) The use of near infrared transmittance kernel sorting technology to salvage high quality grain from grain downgraded due to Fusarium damage. Anim. Nutr. 1: 41–46.
79. Wang, W., Lawrence, K., Ni, X., Yoon, S.-C., Heitschmidt, G., and Feldner, P., (2015) Near-infrared hyperspectral imaging for detecting Aflatoxin B 1 of maize kernels. Food Control 51: 347–355.
80. Shahin, M. A., Hatcher, D. W., and Symons, S. J., (2010) Assessment of mildew levels in wheat samples based on spectral characteristics of bulk grains. Qual. Assur. Saf. Crops Foods 2: 133–140.
81. Shahin, M. A., Symons, S. J., and Hatcher, D. W., (2014) Quantification of mildew damage in soft red winter wheat based on spectral characteristics of bulk samples: A comparison of visible-near-infrared imaging and near-infrared spectroscopy. Food Bioprocess Tech. 7: 224–234.
82. Shahin, M. A., and Symons, S. J., (2012) Detection of fusarium damage in Canadian wheat using visible/near-infrared hyperspectral imaging. J. Food Meas. Char. 6: 3–11.
83. 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 Microbiol. 144: 64–71.
84. Sirisomboon, C. D., Putthang, R., and Sirisomboon, P., (2013) Application of near infrared spectroscopy to detect aflatoxigenic fungal contamination in rice. Food Control 33: 207–214.
85. Vejarano, R., Siche, R., and Tesfaye, W., (2017) Evaluation of biological contaminants in foods by hyperspectral imaging (HSI): A review. Int. J. Food Prop.
86. Cozzolino, D., Degner, S., and Eglinton, J., (2015) In situ study of water uptake by the seeds, endosperm and husk of barley using infrared spectroscopy. Spectrochim. Acta Part A: Mol. Biomol. Spectrosc.
87. 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. Anal. Chim. Acta 686: 64–75.
88. Lancelot, E., Bertrand, D., Hanafi, M., and Jaillais, B., (2017) Near-infrared hyperspectral imaging for following imbibition of single wheat kernel sections. Vib. Spectrosc. 92: 46–53.
89. Wang, D., Dowell, F., and Chung, D., (2001) Assessment of Heat-damaged wheat kernels using near-infrared spectroscopy. 1. Cereal Chem. 78: 625–628.
90. Pierna, J. F., Baeten, V., and Dardenne, P., (2006) Screening of compound feeds using NIR hyperspectral data. Chemometrics and Intelligent Laboratory Systems 84: 114–118.