Analytical and Bioanalytical Chemistry

, Volume 410, Issue 22, pp 5663–5673 | Cite as

Through-packaging analysis of butter adulteration using line-scan spatially offset Raman spectroscopy

  • Santosh Lohumi
  • Hoonsoo Lee
  • Moon S. Kim
  • Jianwei Qin
  • Byoung-Kwan ChoEmail author
Research Paper
Part of the following topical collections:
  1. Food Safety Analysis


Spectroscopic techniques for food quality analysis are limited to surface inspections and are highly affected by the superficial layers (skin or packaging material) of the food samples. The ability of spatially offset Raman spectroscopy (SORS) to obtain chemical information from below the surface of a sample makes it a promising candidate for the non-destructive analysis of the quality of packaged food. In the present study, we developed a line-scan SORS technique for obtaining the Raman spectra of packaged-food samples. This technique was used to quantify butter adulteration with margarine through two different types of packaging. Further, the significant commercial potential of the developed technique was demonstrated by its being able to discriminate between ten commercial varieties of butter and margarine whilst still in their original, unopened packaging. The results revealed that, while conventional backscattering Raman spectroscopy cannot penetrate the packaging, thus preventing its application to the quality analysis of packaged food, SORS analysis yielded excellent qualitative and quantitative analyses of butter samples. The partial least-square regression analysis predictive values for the SORS data exhibit correlation coefficient values of 0.95 and 0.92, associated with the prediction error 3.2 % and 3.9 % for cover-1 & 2, respectively. The developed system utilizes a laser line (ca. 14-cm wide) that enables the simultaneous collection of a large number of spectra from a sample. Thus, by averaging the spectra collected for a given sample, the signal-to-noise ratio of the final spectrum can be enhanced, which will then have a significant effect on the multivariate data analysis methods used for qualitative and/or qualitative analyses. This recently presented line-scan SORS technique could be applied to the development of high-throughput and real-time analysis techniques for determining the quality and authenticity various packaged agricultural products.


Food safety Food authenticity Through-packaging analysis Raman imaging SORS 



This research was supported by research fund of Chungnam National University.

Compliance with ethical standards

The samples used in this study were purchased from a supermarket, and no commercial or financial relationship with the products’ brand used. Informed consent was provided by all individuals involved in this study.

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

216_2018_1189_MOESM1_ESM.pdf (616 kb)
ESM 1 (PDF 616 kb)


  1. 1.
    Reid LM, O’donnell CP, Downey G. Recent technological advances for the determination of food authenticity. Trends Food Sci Technol. 2006;17:344–53.CrossRefGoogle Scholar
  2. 2.
    Ellis DI, Brewster VL, Dunn WB, Allwood JW, Golovanov AP, Goodacre R. Fingerprinting food: current technologies for the detection of food adulteration and contamination. Chem Soc Rev. 2012;41:5706–27.CrossRefPubMedGoogle Scholar
  3. 3.
    Li-Chan ECY. The application of Raman spectroscopy in food science. Trends Food Sci Technol. 1996;7:361–70.CrossRefGoogle Scholar
  4. 4.
    Lohumi S, Kim MS, Qin J, Cho B-K. Raman imaging from microscopy to macroscopy: quality and safety control of biological materials. Trends Anal Chem. 2017;93:183–98.CrossRefGoogle Scholar
  5. 5.
    Qin J, Kim MS, Chao KCB-K. Raman chemical imaging technology for food and agricultural applications. J Biosyst Eng. 2017;42:170–89.Google Scholar
  6. 6.
    Matousek P. Deep non-invasive Raman spectroscopy of living tissue and powders. Chem Soc Rev. 2007;36:1292–304.CrossRefPubMedGoogle Scholar
  7. 7.
    Matousek P, Clark IP, Draper ERC, Morris MD, Everall N, Towrie M, et al. Subsurface probing in diffusely scattering media using spatially offset Raman spectroscopy. Appl Spectrosc. 2005;59:393–400.CrossRefPubMedGoogle Scholar
  8. 8.
    Conti C, Colombo C, Realini M, Zerbi G, Matousek P. Subsurface Raman analysis of thin painted layers. Appl Spectrosc. 2014;68:686–91. Scholar
  9. 9.
    Afseth NK, Bloomfield M, Wold JP, Matousek P. A novel approach for subsurface through-skin analysis of salmon using spatially offset Raman spectroscopy (SORS). Appl Spectrosc. 2014;68:255–62.CrossRefPubMedGoogle Scholar
  10. 10.
    Qin J, Chao K, Kim MS. Nondestructive evaluation of internal maturity of tomatoes using spatially offset Raman spectroscopy. Postharvest Biol Technol. 2012;71:21–31.CrossRefGoogle Scholar
  11. 11.
    Chao K, Dhakal S, Qin J, Peng Y, Schmidt W, Kim M, et al. A spatially offset Raman spectroscopy method for non-destructive detection of gelatin-encapsulated powders. Sensors. 2017;17:1–12.CrossRefGoogle Scholar
  12. 12.
    Matousek P, Stone N. Development of deep subsurface Raman spectroscopy for medical diagnosis and disease monitoring. Chem Soc Rev. 2016;45:1794–802.CrossRefPubMedGoogle Scholar
  13. 13.
    Botteon A, Conti C, Realini M, Colombo C, Matousek P. Discovering hidden painted images: subsurface imaging using microscale spatially offset Raman spectroscopy. Anal Chem. 2017;89:792–8.CrossRefPubMedGoogle Scholar
  14. 14.
    Liu W, Ong YH, Yu XJ, Ju J, Perlaki CM, Liu LB, et al. Snapshot depth sensitive Raman spectroscopy in layered tissues. Opt Express. 2016;24:28312–25.CrossRefPubMedGoogle Scholar
  15. 15.
    Qin J, Chao K, Kim MS. Investigation of Raman chemical imaging for detection of lycopene changes in tomatoes during postharvest ripening. J Food Eng. 2011;107:277–88.CrossRefGoogle Scholar
  16. 16.
    Fadzlillah NA, Rohman A, Ismail A, Mustafa S, Khatib A. Application of FTIR-ATR spectroscopy coupled with multivariate analysis for rapid estimation of butter adulteration. J Oleo Sci. 2013;62:555–62.CrossRefPubMedGoogle Scholar
  17. 17.
    Nedeljković A, Rösch P, Popp J, Miočinović J, Radovanović M, Pudja P. Raman spectroscopy as a rapid tool for quantitative analysis of butter adulterated with margarine. Food Anal Methods. 2016;9:1315–20.CrossRefGoogle Scholar
  18. 18.
    Koca N, Kocaoglu-Vurma NA, Harper WJ, Rodriguez-Saona LE. Application of temperature-controlled attenuated total reflectance-mid-infrared (ATR-MIR) spectroscopy for rapid estimation of butter adulteration. Food Chem. 2010;121:778–82.CrossRefGoogle Scholar
  19. 19.
    Gat N. Imaging spectroscopy using tunable filters: a review. Proc SPIE. 2000;4056:50–64. Scholar
  20. 20.
    Zhang Z-M, Chen S, Liang Y-Z. Baseline correction using adaptive iteratively reweighted penalized least squares. Analyst. 2010;135:1138–46.CrossRefPubMedGoogle Scholar
  21. 21.
    Qin J, Kim MS, Schmidt WF, Cho B-K, Peng Y, Chao K. A line-scan hyperspectral Raman system for spatially offset Raman spectroscopy. J Raman Spectrosc. 2016;47:437–43.CrossRefGoogle Scholar
  22. 22.
    El-Abassy RM, Eravuchira PJ, Donfack P, Von Der Kammer B, Materny A. Fast determination of milk fat content using Raman spectroscopy. Vib Spectrosc. 2010;56:3–8.CrossRefGoogle Scholar
  23. 23.
    Gallier S, Gordon KC, Jiménez-Flores R, Everett DW. Composition of bovine milk fat globules by confocal Raman microscopy. Int Dairy J. 2011;21:402–12.CrossRefGoogle Scholar
  24. 24.
    Seo Y-W, Ahn CK, Lee H, Park E, Mo C, Cho B-K. Non-destructive sorting techniques for viable pepper (Capsicum annuum L.) seeds using Fourier transform near-infrared and Raman spectroscopy. J Biosyst Eng. 2016;41:51–9.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Santosh Lohumi
    • 1
  • Hoonsoo Lee
    • 2
  • Moon S. Kim
    • 2
  • Jianwei Qin
    • 2
  • Byoung-Kwan Cho
    • 1
    Email author
  1. 1.Department of Biosystems Machinery Engineering, College of Agricultural and Life ScienceChungnam National UniversityDaejeonSouth Korea
  2. 2.Environmental Microbial and Food Safety Laboratory, Agricultural Research ServiceU.S. Department of AgricultureBeltsvilleUSA

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