Food Safety pp 127-148 | Cite as

Food Adulteration and Authenticity

  • M. KamruzzamanEmail author


Authenticity and detection of adulteration are the increasing challenges for researchers, consumers, industries, and regulatory agencies. Traditional approaches may not be the most effective option to fight against adulteration. Much effort has been spent in both academia and industry to develop rapid and nondestructive optical techniques for detecting adulteration. Among them, hyperspectral imaging is one of the most promising. Hyperspectral imaging is a rapid, reagentless, nondestructive analytical technique that integrates spectroscopic and imaging techniques into one system to attain both spectral and spatial information simultaneously from an object that cannot be achieved with either digital imaging or conventional spectroscopic techniques. Associated with multivariate analyses, the technique has been successfully implemented for rapid and nondestructive inspection of various food products. In this chapter, latest research outcomes for authenticity and detecting adulteration using hyperspectral imaging will be highlighted and described. Additionally, challenges, opportunities, and future trends of hyperspectral imaging will also be discussed.


Partial Little Square Regression Hyperspectral Imaging Partial Little Square Regression Model Successive Projection Algorithm Mince Meat 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Accum F (1820) A treatise on adulterations of food and culinary poisons, and methods of detecting them. Evergreen Review, New YorkGoogle Scholar
  2. Alamprese C, Casale M, Sinelli N et al (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. Al-Jowder O, Defernez M, Kemsley E et al (2002) Detection of adulteration in cooked meat products by mid-infrared spectroscopy. J Agric Food Chem 50:1325–1329CrossRefGoogle Scholar
  4. Ashurst PR, Dennis MJ (eds) (1998) Analytical methods for food authentication. Blackie, LondonGoogle Scholar
  5. Bail J (2014) Trends and solutions in combating global food fraud. Accessed 10 May 2015
  6. Ballin NZ, Lametsch R (2008) Analytical methods for authentication of fresh vs. thawed meat—A review. Meat Sci 80:151–158CrossRefGoogle Scholar
  7. BBC (2010) Timeline: China milk scandal. Accessed 10 May 2015.
  8. Boyaci IH, Temiz HT, Uysal RS et al (2014) A novel method for discrimination of beef and horsemeat using Raman spectroscopy. Food Chem 148:37–41CrossRefGoogle Scholar
  9. Burger J, Gowen A (2011) Data handling in hyperspectral image analysis. Chemometr Intell Lab Syst 108:13–22CrossRefGoogle Scholar
  10. Busta FF, Kennedy SP (2011) Defending the safety of the global food system from intentional contamination in a changing market. In: Hefnawy M (ed) Advances in food protection: focus on food safety and defense. Springer, DordrechtGoogle Scholar
  11. Cen H, He Y (2007) Theory and application of near infrared reflectance spectroscopy in determination of food quality. Trends Food Sci Technol 18:72–83CrossRefGoogle Scholar
  12. Cordella C, Moussa I, Martel AC et al (2002) Recent developments in food characterization and adulteration detection: technique-oriented perspectives. J Agric Food Chem 50:1751–1764CrossRefGoogle Scholar
  13. Ellis DI, Brewster VL, Dunn WB et al (2012) Fingerprinting food: current technologies for the detection of food adulteration and contamination. Chem Soc Rev 41:5706–5727CrossRefGoogle Scholar
  14. ElMasry G, Wang N, ElSayed A et al (2007) Hyperspectral imaging for non-destructive determination of some quality attributes for strawberry. J Food Eng 81:98–107CrossRefGoogle Scholar
  15. ElMasry G, Kamruzzaman M, Sun DW et al (2012) Principles and applications of hyperspectral imaging in quality evaluation of agro-food products, a review. Crit Rev Food Sci Nutr 52:999–1023CrossRefGoogle Scholar
  16. Espiñeira M, Vieites JM, Santaclara FJ (2010) Species authentication of octopus, cuttlefish, bobtail and bottle squids (families Octopodidae, Sepiidae and Sepiolidae) by FINS methodology in seafoods. Food Chem 12:527–532CrossRefGoogle Scholar
  17. FDA (2009) Economically motivated adulteration; Public meeting. Accessed 10 May 2015
  18. Feng YZ, Sun DW (2012) Application of hyperspectral imaging in food safety inspection and control, a review. Crit Rev Food Sci Nutr 52:1039–1058CrossRefGoogle Scholar
  19. Filby FA (1976) A history of food adulteration and analysis. Allen & Unwin, LondonGoogle Scholar
  20. Gossner CM, Schlundt J, Embarek PB et al (2009) The melamine incident: implications for international food and feed safety. Environ Health Perspect 117:1803–1808CrossRefGoogle Scholar
  21. Iqbal A, Sun DW, Allen P (2013) An overview on principle, techniques and application of hyperspectral imaging with special reference to ham quality evaluation and control. Food Control 46:242–245CrossRefGoogle Scholar
  22. Johnson R (2014) Food fraud and “economically motivated adulteration” of food and food ingredients. Congressional research serviceGoogle Scholar
  23. Kamruzzaman M, ElMasry G, Sun DW et al (2011) Application of NIR hyperspectral imaging for discrimination of lamb muscles. J Food Eng 104:332–340CrossRefGoogle Scholar
  24. Kamruzzaman M, Barbin D, ElMasry G et al (2012a) Potential of hyperspectral imaging and pattern recognition for categorization and authentication of red meat. Innovat Food Sci Emerg Technol 16:316–325CrossRefGoogle Scholar
  25. Kamruzzaman M, ElMasry G, Sun DW et al (2012b) Prediction of some quality attributes of lamb meat using near infrared hyperspectral imaging and multivariate analysis. Anal Chim Acta 714:57–67CrossRefGoogle Scholar
  26. Kamruzzaman M, ElMasry G, Sun DW et al (2012c) Non-destructive prediction and visualization of chemical composition in lamb meat using NIR hyperspectral imaging and multivariate regression. Innovat Food Sci Emerg 16:218–226CrossRefGoogle Scholar
  27. Kamruzzaman M, Sun DW, ElMasry G et al (2013a) Fast detection and visualization of minced lamb meat adulteration using NIR hyperspectral imaging and multivariate image analysis. Talanta 103:130–136CrossRefGoogle Scholar
  28. Kamruzzaman M, ElMasry G, Sun DW et al (2013b) Non-destructive assessment of instrumental and sensory tenderness of lamb meat using NIR hyperspectral imaging. Food Chem 141:389–396CrossRefGoogle Scholar
  29. Kamruzzaman M, Makino Y, Oshita S (2014) An appraisal of hyperspectral imaging for non-invasive authentication of geographical origin of beef and pork. In International conference on agricultural engineering, AgEng-2014, 6–10 July, ZurichGoogle Scholar
  30. Kamruzzaman M, Nakauchi S, ElMasry G (2015a) High throughput screening for food safety assessment: biosensor technologies, hyperspectral imaging and practical application. In: Bhunia AK, Kim MS, Taitt CR (eds) Online screening of meat and poultry product quality and safety using hyperspectral imaging. Woodhead, Cambridge, pp 425–466Google Scholar
  31. Kamruzzaman M, Makino Y, Oshita S (2015b) Non-invasive analytical technology for the detection of contamination, adulteration, and authenticity of meat, poultry, and fish: a review. Anal Chim Acta 853:19–29CrossRefGoogle Scholar
  32. Kamruzzaman M, Makino Y, Oshita S et al (2015c) 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
  33. Kelly JFD, Downey G, Fouratier V (2004) Initial study of honey adulteration by sugar solutions using mid-infrared (MIR) spectroscopy and chemometrics. J Agric Food Chem 52:33–39CrossRefGoogle Scholar
  34. Li B, Wang H, Zhao Q et al (2015) Rapid detection of authenticity and adulteration of walnut oil by FTIR and fluorescence spectroscopy: a comparative study. Food Chem 181:25–30CrossRefGoogle Scholar
  35. Longobardi F, Casiello G, Cortese M et al (2015) Discrimination of geographical origin of lentils (Lens culinaris Medik.) using isotope ratio mass spectrometry combined with chemometrics. Food Chem. doi: 10.1016/j.foodchem.2015.05.020 Google Scholar
  36. Lucio-Gutiérrez JR, Coello J, Maspoch S (2011) Application of near infrared spectral fingerprinting and pattern recognition techniques for fast identification of Eleutherococcus senticosus. Food Res Int 4:557–565CrossRefGoogle Scholar
  37. Mamani-Linares LW, Gallo C, Alomar D (2012) Identification of cattle, llama and horse meat by near infrared reflectance or transflectance spectroscopy. Meat Sci 90:378–385CrossRefGoogle Scholar
  38. Manley M, De Bruyn N, Downey G (2003) Classification of three-year old, unblended South African brandy with near-infrared spectroscopy. NIR News 14:8–9CrossRefGoogle Scholar
  39. Meza-Márquez OG, Gallardo-Velázquez T, Osorio-Revilla G (2010) Application of mid-infrared spectroscopy with multivariate analysis and soft independent modeling of class analogies (SIMCA) for the detection of adulterants in minced beef. Meat Sci 86:511–519CrossRefGoogle Scholar
  40. Morsy N, Sun DW (2013) Robust linear and non-linear models of NIR spectroscopy for detection and quantification of adulterants in fresh and frozen- thawed minced beef. Meat Sci 93:292–302CrossRefGoogle Scholar
  41. Peres B, Barlet N, Loiseau G et al (2007) Review of the current methods of analytical traceability allowing determination of the origin of foodstuffs. Food Control 18:228–235CrossRefGoogle Scholar
  42. Picque D, Cattenoz T, Corrieu G et al (2005) Discrimination of red wines according to their geographical origin and vintage year by the use of mid-infrared spectroscopy. Sci Aliments 25:207–220CrossRefGoogle Scholar
  43. Primrose S, Woolfe M, Rpllinson S (2010) Food forensics: methods for determining the authenticity of foodstuffs. Trends Food Sci Technol 21:582–590CrossRefGoogle Scholar
  44. Pu H, Sun DW, Ma J et al (2014a) Hierarchical variable selection for predicting chemical constituents in lamb meats using hyperspectral imaging. J Food Eng 143:44–52CrossRefGoogle Scholar
  45. Pu H, Xie A, Sun DW et al (2014b) Application of wavelet analysis to spectral data for categorization of lamb muscles. Food Bioprocess Technol. doi: 10.1007/s11947-014-1393-8 Google Scholar
  46. Reid LM, O’Donnell CP, Downey G (2006) Recent technological advances for the determination of food authenticity. Trends Food Sci Technol 17:344–353CrossRefGoogle Scholar
  47. Rohman A, Sismindari, Erwanto Y et al (2011) Analysis of pork adulteration in beef meatball using Fourier transform infrared (FTIR) spectroscopy. Meat Sci 88:91–95CrossRefGoogle Scholar
  48. Ruoff K, Luginbuhl W, Kunzli R et al (2006) Authentication of the botanical and geographical origin of honey by mid-infrared spectroscopy. J Food Agric Chem 54:6873–6880CrossRefGoogle Scholar
  49. Spink J, Moyer DC (2011) Defining the public health threat of food fraud. J Food Sci 76:R157–R164CrossRefGoogle Scholar
  50. Sun DW (2008) Modern techniques for food authentication. Academic, San Diego, CAGoogle Scholar
  51. Sun X, Zhang L, Li P et al (2015) Fatty acid profiles based adulteration detection for flaxseed oil by gas chromatography mass spectrometry. LWT Food Sci Technol 63:430–436CrossRefGoogle Scholar
  52. Taghizadeh M, Gowen A, O’Donnell C (2009) Prediction of white button mushroom (Agaricus bisporus) moisture content using hyperspectral imaging. Sens Instrum Food Qual Saf 3:219–226CrossRefGoogle Scholar
  53. Terracini B (2004) Toxic oil syndrome. Ten years of progress. World Health Organization Regional Office, CopenhagenGoogle Scholar
  54. Uríčková V, Sádecká J (2015) Determination of geographical origin of alcoholic beverages using ultraviolet, visible and infrared spectroscopy: a review. Spectrochim Acta A 148:131–137CrossRefGoogle Scholar
  55. Wu D, Shi H, He Y et al (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
  56. Xin H, Stone R (2008) Chinese probe unmask high-tech adulteration with melamine. Science 322:1310–1311CrossRefGoogle Scholar
  57. Xiu C, Klein KK (2010) Melamine in milk products in China: examining the factors that led to deliberate use of the contaminant. Food Policy 35:463–470CrossRefGoogle Scholar
  58. Zhang J, Zhang X, Dediu L et al (2011) Review of the current application of fingerprinting allowing detection of food adulteration and fraud in China. Food Control 22:1126–1135CrossRefGoogle Scholar
  59. Zhao M, Downey G, O’Donnell C (2014) Detection of adulteration in fresh and frozen beef burger products by beef offal using mid-infrared ATR spectroscopy and multivariate data analysis. Meat Sci 96:1003–1011CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Food Technology and Rural Industries, Faculty of Agricultural Engineering and TechnologyBangladesh Agricultural UniversityMymensinghBangladesh

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