Food Analytical Methods

, Volume 12, Issue 10, pp 2205–2215 | Cite as

Hyperspectral imaging for a rapid detection and visualization of duck meat adulteration in beef

  • Hongzhe Jiang
  • Wei WangEmail author
  • Hong Zhuang
  • Seung-Chul Yoon
  • Yi Yang
  • Xin Zhao


Hyperspectral imaging (HSI) was investigated to detect and visualize beef adulteration with duck meat. Minced beef was adulterated with duck meat in range of 0–100% (w/w) at 10% increments. Visible and near-infrared (VNIR) hyperspectral images were acquired, and extracted spectra were analyzed with preprocessing methods. Partial least squares regression (PLSR) and principal component regression (PCR) modeling methods were individually utilized, and the PLSR model based on full raw spectra performed best. To make the preferred model practical, optimal wavelengths were individually selected using two-dimensional correlation spectroscopy (2D-COS) and PC loadings. The model based on competent wavelengths selected from PC loadings resulted in good performance of Rp2 = 0.96, RMSEP = 6.58%, and RPD = 4.86 with limit of detection (LOD) of 7.59%. Finally, spatially distributed visualization was achieved using the simplified model, and adulteration levels were readily discernible. Known distributed samples were also successfully visualized, and the validity of visualization was thus proved. Results demonstrated the potential of the HSI to detect minced beef adulteration with duck meat.


Hyperspectral imaging Near-infrared Beef adulteration Duck meat Two-dimensional correlation spectroscopy Visualization 


Funding Information

This work was supported by the National Natural Science Foundation of China (Grant NO. 31772062).

Compliance with Ethical Standards

Conflict of Interest

Hongzhe Jiang declares that he has no conflict of interest. Wei Wang declares that he has no conflict of interest. Hong Zhuang declares that he has no conflict of interest. Seung-Chul Yoon declares that he has no conflict of interest. Yi Yang declares that he has no conflict of interest. Xin Zhao declares that she has no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed Consent

Not applicable.


  1. Abdulmawjood A, Grabowski N, Fohler S, Kittler S, Nagengast H, Klein G (2014) Development of loop-mediated isothermal amplification (lamp) assay for rapid and sensitive identification of ostrich meat. Plos One 9:e100717CrossRefPubMedPubMedCentralGoogle Scholar
  2. Alamprese C, Casale M, Sinelli N, Lanteri S, Casiraghi E (2013) Detection of minced beef adulteration with turkey meat by UV–Vis, NIR and MIR spectroscopy. LWT-Food Sci Technol 53:225–232CrossRefGoogle Scholar
  3. Andrés S, Murray I, Navajas EA, Fisher AV, Lambe NR, Bünger L (2007) Prediction of sensory characteristics of lamb meat samples by near infrared reflectance spectroscopy. Meat Sci 76:509–516CrossRefPubMedGoogle Scholar
  4. Ballin NZ (2010) Authentication of meat and meat products. Meat Sci 86:577–587CrossRefPubMedGoogle Scholar
  5. Barbin D, ElMasry G, Sun DW, Allen P (2012) Near-infrared hyperspectral imaging for grading and classification of pork. Meat Sci 90:259–268CrossRefPubMedGoogle Scholar
  6. Barbin DF, ElMasry G, Sun DW, Allen P (2013) Non-destructive determination of chemical composition in intact and minced pork using near-infrared hyperspectral imaging. Food Chem 138:1162–1171CrossRefPubMedGoogle Scholar
  7. Bilge G, Velioglu HM, Sezer B, Eseller KE, Boyaci IH (2016) Identification of meat species by using laser-induced breakdown spectroscopy. Meat Sci 119:118–122CrossRefPubMedGoogle Scholar
  8. Bowker B, Hawkins S, Zhuang H (2014) Measurement of water-holding capacity in raw and freeze-dried broiler breast meat with visible and near-infrared spectroscopy. Poult Sci 93:1834–1841CrossRefPubMedGoogle Scholar
  9. Chou CC, Lin SP, Lee KM, Hsu CT, Vickroy TW, Zen JM (2007) Fast differentiation of meats from fifteen animal species by liquid chromatography with electrochemical detection using copper nanoparticle plated electrodes. J Chromatogr B Analyt Technol Biomed Life Sci 846:230–239CrossRefPubMedGoogle Scholar
  10. Chu X, Wang W, Ni X, Zheng H, Zhao X, Zhang R, Li Y (2018) Growth Identification of Aspergillus flavus and Aspergillus parasiticus by visible/near-infrared hyperspectral imaging. Appli Sci 8:513CrossRefGoogle Scholar
  11. ElMasry G, Sun DW, Allen P (2011) Non-destructive determination of water-holding capacity in fresh beef by using NIR hyperspectral imaging. Food Res Int 44:2624–2633CrossRefGoogle Scholar
  12. ElMasry G, Sun DW, Allen P (2013) Chemical-free assessment and mapping of major constituents in beef using hyperspectral imaging. J Food Eng 117:235–246CrossRefGoogle Scholar
  13. He H, Hong X, Feng Y, Wang Y, Ying J, Liu Q, Qin Y, Zhou X, Wang D (2015) Application of quadruple multiplex PCR detection for beef, duck, mutton and pork in mixed meat. J Food Nutr Res 3:392–398CrossRefGoogle Scholar
  14. Jiang H, Zhuang H, Sohn M, Wang W (2017) Measurement of soy contents in ground beef using near-infrared spectroscopy. Appli Sci 7:97CrossRefGoogle Scholar
  15. Jiang H, Wang W, Zhuang H, Yoon SC, Li Y, Yang Y (2018a) Visible and near-infrared hyperspectral imaging for cooking loss classification of fresh broiler breast fillets. Appli Sci 8:256CrossRefGoogle Scholar
  16. Jiang H, Yoon SC, Zhuang H, Wang W, Lawrence KC, Yang Y (2018b) Tenderness classification of fresh broiler breast fillets using visible and near-infrared hyperspectral imaging. Meat Sci 139:82–90CrossRefPubMedGoogle Scholar
  17. Kamruzzaman M, ElMasry G, Sun DW, Allen P (2013a) Non-destructive assessment of instrumental and sensory tenderness of lamb meat using NIR hyperspectral imaging. Food Chem 141:389–396CrossRefPubMedGoogle Scholar
  18. Kamruzzaman M, Sun DW, Elmasry G, Allen P (2013b) Fast detection and visualization of minced lamb meat adulteration using NIR hyperspectral imaging and multivariate image analysis. Talanta 103:130–136CrossRefPubMedGoogle Scholar
  19. Kamruzzaman M, Makino Y, Oshita S, Liu S (2015a) Assessment of visible near-infrared hyperspectral imaging as a tool for detection of horsemeat adulteration in minced beef. Food Bioprocess Technol 8:1054–1062CrossRefGoogle Scholar
  20. Kamruzzaman M, Makino Y, Oshita S (2015b) Hyperspectral imaging in tandem with multivariate analysis and image processing for non-invasive detection and visualization of pork adulteration in minced beef. Anal Methods 7:7496–7502CrossRefGoogle Scholar
  21. Kamruzzaman M, Makino Y, Oshita S (2016) Rapid and non-destructive detection of chicken adulteration in minced beef using visible near-infrared hyperspectral imaging and machine learning. J Food Eng 170:8–15CrossRefGoogle Scholar
  22. Kapper C, Klont RE, Verdonk JMAJ, Urlings HAP (2012) Prediction of pork quality with near infrared spectroscopy (NIRS): 1. Feasibility and robustness of NIRS measurements at laboratory scale. Meat Sci 91:294–299CrossRefPubMedGoogle Scholar
  23. Keithley RB, Wightman RM, Heien ML (2009) Multivariate concentration determination using principal component regression with residual analysis. TrAC Trends Anal Chem 28:1127–1136CrossRefGoogle Scholar
  24. Liu Y, Chen YR (2000) Two-dimensional correlation spectroscopy study of visible and near-infrared spectral variations of chicken meats in cold storage. Appl Spectrosc 54:1458–1470CrossRefGoogle Scholar
  25. Liu D, Zeng XA, Sun DW (2013) NIR spectroscopy and imaging techniques for evaluation of fish quality—a review. Appl Spectrosc Rev 48:609–628CrossRefGoogle Scholar
  26. Macedo-Silva A, Barbosa SFC, Alkmin MGA, Vaz AJ, Shimokomaki M, Tenuta-Filho A (2000) Hamburger meat identification by dot-ELISA. Meat Sci 56:189–192CrossRefPubMedGoogle Scholar
  27. Mahesh S, Jayas DS, Paliwal J, White NDG (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 8:31–40CrossRefGoogle Scholar
  28. 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 Bioprocess Technol 2:308–314CrossRefGoogle Scholar
  29. Noda I (1993) Generalized two-dimensional correlation method applicable to infrared, Raman, and other types of spectroscopy. Appl Spectrosc 47:1329–1336CrossRefGoogle Scholar
  30. Premanandh J (2013) Horse meat scandal–A wake-up call for regulatory authorities. Food Control 34:568–569CrossRefGoogle Scholar
  31. Qin J, Burks TF, Kim MS, Chao K, Ritenour MA (2008) Citrus canker detection using hyperspectral reflectance imaging and PCA-based image classification method. Sens Instrum Food Qual Saf 2:168–177CrossRefGoogle Scholar
  32. Rahmati S, Julkapli NM, Yehye WA, Basirun WJ (2016) Identification of meat origin in food products–a review. Food Control 68:379–390CrossRefGoogle Scholar
  33. Ropodi AI, Pavlidis DE, Mohareb F, Panagou EZ, Nychas GJ (2015) Multispectral image analysis approach to detect adulteration of beef and pork in raw meats. Food Res Int 67:12–18CrossRefGoogle Scholar
  34. Ropodi AI, Panagou EZ, Nychas GJE (2017) Multispectral imaging (MSI): a promising method for the detection of minced beef adulteration with horsemeat. Food Control 73:57–63CrossRefGoogle Scholar
  35. Tang Z, Zhou Y, Zhou Y, Zheng P (2015) Brief analysis on harm and sensory identification of five kinds of questionable meat. Meat Ind 416:47–54 (in Chinese)Google Scholar
  36. Wu D, Sun DW (2013) Application of visible and near infrared hyperspectral imaging for non-invasively measuring distribution of water-holding capacity in salmon flesh. Talanta 116:266–276CrossRefPubMedGoogle Scholar
  37. Wu D, Shi H, He Y, Yu X, Bao Y (2013) Potential of hyperspectral imaging and multivariate analysis for rapid and non-invasive detection of gelatin adulteration in prawn. J Food Eng 119:680–686CrossRefGoogle Scholar
  38. You Z, Zhuo L, Yang X, Hong H, Liu Z, Gong Z, Cheng F (2015) Food research applications of two-dimensional correlation spectroscopy. Appl Spectrosc Rev 50:840–858CrossRefGoogle Scholar
  39. Zhong K, Han F, Yao K, Ren X, Chen S, Luo X, Guo L (2012) Current situation, problems, challenges and counter measures of food safety risk communication in China. Chin J Food Hyg 24:578–586 (in Chinese)Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of EngineeringChina Agricultural UniversityBeijingChina
  2. 2.Quality & Safety Assessment Research UnitU. S. National Poultry Research Center, USDA-ARSAthensUSA

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