Skip to main content

Hyperspectral Imaging

  • Chapter
  • First Online:
Process Analytical Technology for the Food Industry

Part of the book series: Food Engineering Series ((FSES))

  • 2176 Accesses

Abstract

The advantages of hyperspectral imaging (HSI) which is an emerging platform technology that integrates conventional imaging and spectroscopy to attain both spatial and spectral information from an object will be discussed. Although HSI was originally developed for remote sensing, it has recently emerged as a powerful process analytical tool for non-destructive food analysis. This chapter will provide an introduction to HSI: HSI equipment, image acquisition and processing; current limitations and likely future applications are discussed. In addition, recent advances in the application of HSI to food safety and quality assessment will be reviewed, such as contaminant detection, defect identification, constituent analysis and quality evaluation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  • Ariana D, Lu R (2006). Visible/near-infrared hyperspectral transmittance imaging for detection of internal mechanical injury in pickling cucumbers. In: ASABE Annual International Meeting, Paper No. 063039, July 2006.

    Google Scholar 

  • Ariana D, Lu R, Guyer DE (2006) Hyperspectral reflectance imaging for detection of bruises on pickling cucumbers. Comput Electron Agric 53:60–70

    Article  Google Scholar 

  • Burger J, Geladi P (2006) Hyperspectral NIR image regression part II: Dataset preprocessing diagnostics. J Chemom 20:106–119

    Article  CAS  Google Scholar 

  • Chao K, Yang C, Kim M (2010) Spectral line-scan imaging system for high-speed non-destructive Wholesomeness inspection of broilers. Trends Food Sci Technol 21:129–137

    Article  CAS  Google Scholar 

  • Cheng X, Chen YR, Tao Y, Wang CY, Kim MS, Lefcourt AM (2004) A novel integrated PCA and FLD method on hyperspectral image feature extraction for cucumber chilling damage inspection. Trans ASAE 47:1313–1320

    Article  Google Scholar 

  • Cogdill R, Hurburgh C, Rippke G (2004) Single-kernel maize analysis by near-infrared hyperspectral imaging. Trans ASAE 47:311–320

    Article  Google Scholar 

  • Dubois J, Lewis E, Fry F, Calvey E (2005) Bacterial identification by near-infrared chemical imaging of food-specific cards. Food Microbiol 22:577–583

    Article  CAS  Google Scholar 

  • ElMasry G, Wold JP (2008) High-speed assessment of fat and water content distribution in fish fillets using online imaging spectroscopy. J Agric Food Chem 56(17):7672–7677

    Article  CAS  Google Scholar 

  • ElMasry G, Wang N, El Sayed A, Ngadi M (2007) Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry. J Food Eng 81:98–107

    Article  CAS  Google Scholar 

  • ElMasry G, Wang N, Vigneault C, Qiao J, ElSayed A (2008). Early detection of apple bruises on different background colors using hyperspectral imaging. LWT Food Sci Technol 41(2):337–345

    Article  CAS  Google Scholar 

  • ElMasry G, Wang N, Vigneault C (2009). Detecting chilling injury in Red Delicious apple using hyperspectral imaging and neural networks. Postharvest Biol Technol 52(1):1–8

    Article  Google Scholar 

  • Escoriza M, VanBriesen J, Stewart S, Maier J, Treado P (2006) Raman spectroscopy and chemical imaging for quantification of filtered waterborne bacteria. J Microbiol Methods 66:63–72

    Article  CAS  Google Scholar 

  • Fernandez Pierna JA, Baeten V, Michotte Renier A, Cogdill RP, Dardenne P (2005) Combination of SVM and NIR imaging spectroscopy for the detection of MBM in compound feeds. J Chemom 18(7–8):341–349

    Google Scholar 

  • Fernandez Pierna JA, Baeten V, Dardenne P (2006) Screening of compound feeds using NIR hyperspectral data. Chemom Intell Lab Syst 84:114–118

    Article  CAS  Google Scholar 

  • Fernandez Pierna JA, Dardenne P, Baeten V (2010) In-house validation of a near infrared hyperspectral imaging method for detecting processed animal proteins (PAP) in compound feed. J Near Infrared Spectrosc 18:121–133

    Article  Google Scholar 

  • Gaston E, Frías JM, Cullen PJ, O’Donnell CP, Gowen AA (2010a) Prediction of polyphenol oxidase activity using visible and near-infrared hyperspectral imaging on mushroom (Agaricus bisporus) caps. J Agric Food Chem 58:6226–6233

    Google Scholar 

  • Gaston E, Frías JM, Cullen PJ, O’Donnell CP, Gowen AA (2010b) Visible near infrared hyperspectral imaging for the identification and discrimination of brown blotch disease on mushroom (Agaricus bisporus) caps. J Near Infrared Spectrosc 18:341–353

    Article  CAS  Google Scholar 

  • Gómez-Sanchis J, Gómez-Chova L, Aleixos N, Camps-Valls G, Montesinos-Herrero C, Moltó E, Blasco J (2008) Hyperspectral system for early detection of rottenness caused by Penicillium digitatum in mandarins. J Food Eng 89(1):80–86

    Article  Google Scholar 

  • Gowen AA, O’Donnell CP, Taghizadeh M, Cullen PJ, Frias JM, Downey G (2008a) Hyperspectral imaging combined with principal component analysis for bruise damage detection on white mushrooms (Agaricus bisporus). J Chemom 22:259–267

    Google Scholar 

  • Gowen AA, O’Donnell CP, Taghizadeh M, Gaston E, O’Gorman A, Cullen PJ, Frias JM, Esquerre C, Downey G (2008b) Hyperspectral imaging for the investigation of quality deterioration in sliced mushrooms (Agaricus bisporus) during storage. Sens Instrum Food Qual 2:133–143

    Google Scholar 

  • Gowen A, Taghizadeh M, O’Donnell CP (2009) Identification of mushrooms subjected to freeze damage using hyperspectral imaging. J Food Eng 93:7–12

    Article  Google Scholar 

  • Grahn HF, Geladi P (2007) Techniques and applications of hyperspectral image analysis. Wiley, Chichester

    Book  Google Scholar 

  • Heia K, Sivertsen A, Stormo S, Elvevoll E, Wold J, Nilsen H (2007) Detection of nematodes in cod (Gadus morhua) fillets by imaging spectroscopy. J Food Sci 72:E011–E015

    Article  Google Scholar 

  • Jiang L, Zhu B, Rao X, Berney G, Tao Y (2007) Discrimination of black walnut shell and pulp in hyperspectral fluorescence imagery using Gaussian kernel function approach. J Food Eng 81:108–117

    Article  Google Scholar 

  • Jun W, Kim M, Cho B, Millner P, Chao K, Chan D (2010) Microbial biofilm detection on food contact surfaces by macro-scale fluorescence imaging. J Food Eng 99:314–322

    Article  Google Scholar 

  • Kim MS, Chen YR, Mehl PM (2001) Hyperspectral reflectance and fluorescence imaging system for food quality and safety. Trans ASAE 44:721–729

    Google Scholar 

  • Kim MS, Lefcourt AM, Chao K, Chen YR, Kim I, Chan DE (2002) Multispectral detection of fecal contamination on apples based on hyperspectral imagery: Part I. Application of visible and near-infrared reflectance imaging. Trans ASAE 45:2027–2037

    Google Scholar 

  • Kim MS, Lefcourt AM, Chen YR (2003) Multispectral laser-induced fluorescence imaging system for large biological samples. Appl Opt 42:3927–3933

    Article  Google Scholar 

  • Kim I, Kim MS, Chen YR, Kong SG (2004) Detection of skin tumors on chicken carcasses using hyperspectral fluorescence imaging. Trans ASAE 47:1785–1792

    Article  Google Scholar 

  • Kirkhus T, Fismen B, Skotheim Ø, Tschudi J (2009) A DMD (Digital Micro-Mirror Device) based multi-object quasi-imaging spectrometer, ProCams at CVPR 2009 Miami FL. http://graphics.cis.udel.edu/2009/poster/posters.pdf

  • Liu Y, Chen YR, Wang CY, Chan DE, Kim MS (2005) Development of simple algorithm for the detection of chilling injury in cucumbers from visible/near-infrared hyperspectral imaging. Appl Spectrosc 59:78–85

    Article  CAS  Google Scholar 

  • Liu Y, Chen YR, Kim MS, Chan DE, Lefcourt AM (2007) Development of simple algorithms for the detection of fecal contaminants on apples from visible/near infrared hyperspectral reflectance imaging. J Food Eng 81:412–418

    Article  CAS  Google Scholar 

  • Lu RF, Peng YK (2006) Hyperspectral scattering for assessing peach fruit firmness. Biosyst Eng 93:161–171

    Article  Google Scholar 

  • Margalith E (2007) US patent 7,233,392

    Google Scholar 

  • Mehl PM, Chen YR, Kim MS, Chan DE (2004) Development of hyperspectral imaging technique for the detection of apple surface defects and contaminations. J Food Eng 61:67–81

    Article  Google Scholar 

  • Menesatti P, D’Andrea S, Bucarelli A (2004). Non-destructive spectrometric qualification of Italian wheat durum pasta produced by traditional or industrial technology approaches. In: 2004 CIGR International Conference, Beijing, China, 11–14 Oct 2004

    Google Scholar 

  • Menesatti P, Urbani G, Lanza G (2005). Spectral imaging Vis-NIR system to forecast the chilling injury onset on citrus fruits. In: Mencarelli F, Tonutti P (eds) ISHS Acta Horticulturae 682: V international postharvest symposium, pp 1347–1354

    Google Scholar 

  • Menesatti P, Zanella A, D’Andrea S, Costa C, Plagia G, Pallottino F (2008). Supervised multivariate analysis of hyper-spectral NIR images to evaluate the starch index of apples. Food Bioprocess Technol 2:308–314

    Article  Google Scholar 

  • Nicolaï B, Lötze E, Peirs A, Scheerlinck N, Theron K (2006) Non-destructive measurement of bitter pit in apple fruit using NIR hyperspectral imaging. Postharvest Biol Technol 40:1–6

    Article  Google Scholar 

  • Noh H, Lu R (2007) Hyperspectral laser-induced fluorescence imaging for assessing apple fruit quality. Postharvest Biol Technol 43:193–201

    Article  Google Scholar 

  • O´Farrell M, Kirkhus T, Fismen B, Skotheim O, Tschudi J (2010a) Quasi-imaging spectrometer with programmable field of view and field of illumination. NIR News 21:8–10

    Google Scholar 

  • O´Farrell M, Wold JP, Høy M, Tschudi J, Schulerud H (2010b) On-line fat content classification of inhomogeneous pork trimmings using multispectral near infrared interactance imaging. J Near Infrared Spectrosc 18:135–146

    Google Scholar 

  • Oertel DC, Grothaus JT, Marcott C (2009) Applications of spectral imaging using a tunable laser source, Proceedings of SPIE 7319: 731906-1

    Google Scholar 

  • Ottestad S, Høy M, Stevik A, Wold JP (2009) Prediction of ice fraction and fat content in super-chilled salmon by non-contact interactance near infrared imaging. J Near Infrared Spectrosc 17:77–87

    Article  CAS  Google Scholar 

  • Park B, Lawrence KC, Windham WR, Smith D (2006) Performance of hyperspectral imaging system for poultry surface fecal contaminant detection. J Food Eng 75:340–348

    Article  Google Scholar 

  • Park B, Windham WR, Lawrence KC, Smith D (2007) Contaminant classification of poultry hyperspectral imagery using a spectral angle mapper algorithm. Biosyst Eng 96:323–333

    Article  Google Scholar 

  • Polder G, Heijden G, Young I (2002) Spectral image analysis for measuring ripeness of tomatoes. Trans ASAE 45:1155–1161

    Article  Google Scholar 

  • Qiao J, Wang N, Ngadi M, Baljinder S (2005). Water Content and Weight Estimation for Potatoes Using Hyperspectral Imaging. Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org. Paper number 053126, 2005 ASAE Annual Meeting

  • Qiao J, Ngadi M, Wang N, Gariépy C, Prashe S (2007) Pork quality and marbling level assessment using a hyperspectral imaging system. J Food Eng 83:10–16

    Article  Google Scholar 

  • Qin J, Lu R (2005) Detection of pits in tart cherries by hyperspectral transmission imaging. Trans ASAE 48:1963–1970

    Article  Google Scholar 

  • Qin J, Burks TF, Ritenour MA, Bonn WG (2009) Detection of citrus canker using hyperspectral reflectance umaging with spectral information divergence. J Food Eng 93(2):183–191

    Article  Google Scholar 

  • Schmilovitch Z, Shenderey C, Shmulevich I, Alchanatis V, Egozi H, Hoffman A, Ostrovsky V, Lurie S, Arie R (2004). NIRS detection of mouldy core in apples. In 2004 CIGR international conference, Beijing, China, 11–14 Oct 2004

    Google Scholar 

  • Segtnan VH, Høy M, Sørheim O, Kohler A, Lundby F, Wold JP, Ofstad R (2008) Noncontact salt and fat distributional analysis in salted and smoked salmon fillets using X-ray computed tomography and NIR interactance imaging. J Agric Food Chem 57:1705–1710

    Article  Google Scholar 

  • Vargas AM, Kim MS, Tao Y, Lefcourt A (2005) Detection of fecal contamination on cantaloupes using hyperspectral fluorescence imagery. J Food Sci 70:E471–E476

    Article  CAS  Google Scholar 

  • Weinstock BA, Janni J, Hagen L, 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

    Article  CAS  Google Scholar 

  • Wold JP, Kermit M, Woll A (2010) Rapid nondestructive determination of edible meat content in crabs (Cancer Pagurus) by near-infrared imaging spectroscopy. Appl Spectrosc 64:691–699

    Article  CAS  Google Scholar 

  • Xing J, Bravo C, Jancsók P, Ramon H, De Baerdemaeker J (2005) Detecting bruises on ‘Golden Delicious’ apples using hyperspectral imaging with multiple wavebands. Biosyst Eng 90:27–36

    Article  Google Scholar 

  • Xing J, Jancsók P, De Baerdemaeker J (2006) Stem-end/calyx identification on apples using contour analysis in multispectral images. Biosyst Eng 96:231–237

    Article  Google Scholar 

  • Xing J, Saeys W, De Baerdemaeker J (2007) Combination of chemometric tools and image processing for bruise detection on apples. Comput Electron Agric 56:1–13

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to A. A. Gowen .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media, New York

About this chapter

Cite this chapter

Gowen, A., Gaston, E., Burger, J. (2014). Hyperspectral Imaging. In: O'Donnell, C., Fagan, C., Cullen, P. (eds) Process Analytical Technology for the Food Industry. Food Engineering Series. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-0311-5_9

Download citation

Publish with us

Policies and ethics