Food Analytical Methods

, Volume 12, Issue 3, pp 658–667 | Cite as

A Nondestructive Detection Method for Mixed Veterinary Drugs in Pork Using Line-Scan Raman Chemical Imaging Technology

  • Wenxiu Wang
  • Chen Zhai
  • Yankun PengEmail author
  • Kuanglin Chao


This study reports a nondestructive detection method using Raman chemical imaging (RCI) technology for the simultaneous determination of multiple veterinary drugs, such as ofloxacin, chloramphenicol, and sulfadimidine, in pork. A line-scan Raman imaging system was employed to acquire images of pure veterinary drugs and pork samples containing single and mixed drugs. Raman characteristic peaks at 1623, 1353, and 1147 cm−1 were identified for ofloxacin, chloramphenicol, and sulfadimidine, respectively. An image processing method was proposed to calculate the “pixel-ratio” values in each feature image, and linear regression models for a single veterinary drug were then established between the “pixel-ratio” values and the actual concentrations. By applying the models to samples with mixed veterinary drugs, the concentrations of the three drugs were predicted with correlation coefficients of 0.978, 0.986, and 0.984. The satisfactory results indicated that along with the proposed “pixel-ratio” method, RCI technology enables the nondestructive quantitation and spatial distribution visualization of multiple veterinary drug residues.


Raman chemical imaging Pixel-ratio Veterinary drugs Quantitative analysis Spatial distribution Pork 



This research was supported by the National Key Research and Development Program (Project No. 2016YFD0401205), and Major projects of national agricultural products quality and safety risk assessment (Project No. GJFP201701504).

Compliance with Ethical Standards

Conflict of Interest

Wenxiu Wang declares that she has no conflict of interest. Chen Zhai declares that he has no conflict of interest. Yankun Peng declares that he has no conflict of interest. Kuanglin Chao declares that he 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.


  1. Dhakal S, Chao KL, Qin JW, Kim M, Chan D (2016a) Raman spectral imaging for quantitative contaminant evaluation in skim milk powder. J Food Meas Charact 10:374–386. CrossRefGoogle Scholar
  2. Dhakal S, Chao KL, Qin JW, Schmidt W, Chan DE (2016b) Parameter selection for Raman spectroscopy-based detection of chemical contaminants in food powders. T ASABE 59(2):751–763. CrossRefGoogle Scholar
  3. Do MHN, Yamaguchi T, Okihashi M, Harada K, Konishi Y, Uchida K et al (2016) Screening of antibiotic residues in pork meat in Ho Chi Minh City, Vietnam, using a microbiological test kit and liquid chromatography/tandem mass spectrometry. Food Control 69:262–266. CrossRefGoogle Scholar
  4. Eksi-Kocak H, Mentes-Yilmaz O, Boyaci IH (2016) Detection of green pea adulteration in pistachio nut granules by using Raman hyperspectral imaging. Eur Food Res Technol 242:271–277. CrossRefGoogle Scholar
  5. El-Zahry MR, Lendl B (2017) Structure elucidation and degradation kinetic study of Ofloxacin using surface enhanced Raman spectroscopy. Spectrochim Acta A 193:63–70. CrossRefGoogle Scholar
  6. Hu L, Zuo P, Ye BC (2010) Multicomponent mesofluidic system for the detection of veterinary drug residues based on competitive immunoassay. Anal Biochem 405(1):89–95. CrossRefGoogle Scholar
  7. Ji W, Yao WR (2015) Rapid surface enhanced Raman scattering detection method for chloramphenicol residues. Spectrochim Acta A 144:125–130. CrossRefGoogle Scholar
  8. Kikuchi H, Saka T, Teshima R, Nemot S, Akiyama H (2017) Total determination of chloramphenicol residues in foods by liquid chromatography-tandem mass spectrometry. Food Chem 230:589–593. CrossRefGoogle Scholar
  9. Lai KQ, Zhai FL, Zhang YY, Wang XC, Rasco BA, Huang YQ (2011) Application of surface enhanced Raman spectroscopy for analyses of restricted sulfa drugs. Sens & Instrumen Food Qual 5:91–96. CrossRefGoogle Scholar
  10. Liao W, Lu XN (2016) Determination of chemical hazards in foods using surface-enhanced Raman spectroscopy coupled with advanced separation techniques. Trends Food Sci Tech 54:103–113. CrossRefGoogle Scholar
  11. Nicolaï BM, Beullens K, Bobelyn E, Peirs A, Saeys W, Theron K et al (2007) Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review. Postharvest Biol Tech 46(2):99–118. CrossRefGoogle Scholar
  12. Porep JU, Kammerer DR, Carle R (2015) On-line application of near infrared (NIR) spectroscopy in food production. Trends Food Sci Tech 46(2):211–230. CrossRefGoogle Scholar
  13. Qin JW, Chao KL, Cho BK, Peng YK, Kim MS (2014a) High-throughput Raman chemical imaging for rapid evaluation of food safety and quality. T ASABE 57(6):1783–1792. Google Scholar
  14. Qin JW, Chao KL, Kim MS (2011) Investigation of Raman chemical imaging for detection of lycopene changes in tomatoes during postharvest ripening. J Food Eng 107:277–288. CrossRefGoogle Scholar
  15. Qin JW, Chao KL, Kim MS (2013) Simultaneous detection of multiple adulterants in dry milk using macro-scale Raman chemical imaging. Food Chem 138:998–1007. CrossRefGoogle Scholar
  16. Qin JW, Chao KL, Kim MS, Lee HY, Peng YK (2014b) Development of a Raman chemical imaging detection method for authenticating skim milk powder. J Food Meas Charact 8:122–131. CrossRefGoogle Scholar
  17. Rizzetti TM, Souza MPD, Prestes OD, Adaime MB, Zanella R (2018) Optimization of sample preparation by central composite design for multi-class determination of veterinary drugs in bovine muscle, kidney and liver by ultra-high-performance liquid chromatographic-tandem mass spectrometry. Food Chem 246:404–413. CrossRefGoogle Scholar
  18. Wang GN, Zhang L, Song YP, Liu JX, Wang JP (2017) Application of molecularly imprinted polymer based matrix solid phase dispersion for determination of fluoroquinolones, tetracyclines and sulfonamides in meat. J Chromatogr B 1065-1066:104–111. CrossRefGoogle Scholar
  19. Wang WX, Peng YK, Sun HW, Zheng XC, Wei WS (2018) Spectral detection techniques for non-destructively monitoring the quality, safety, and classification of fresh red meat. Food Anal Method 22(10):2707–2730. CrossRefGoogle Scholar
  20. Wang Y (2013) Analysis on levofloxacin by Raman spectroscopy. Chin Pharm 16:398–400. Google Scholar
  21. Xu Y, Du YP, Li QQ, Wang X, Pan YC, Zhang H et al (2013) Ultrasensitive detection of enrofloxacin in chicken muscles by surface-enhanced Raman spectroscopy using amino-modified glycidyl methacrylate-ethylene dimethacrylate (GMA-EDMA) powdered porous material. Food Anal Method 7:1219–1228. CrossRefGoogle Scholar
  22. Xue LC, Cai QR, Zheng X, Liu L, Ling YH, Li Z et al (2017) Determination of 9 hydroxy veterinary drug residues in fish by QuEChERS-GPS-GC/MS. J Chin Mass Spectrometry Soc 38(6):655–663. Google Scholar
  23. Yang D, Ying YB (2011) Applications of Raman spectroscopy in agricultural products and food analysis: a review. Appl Spec Rev 46(7):539–560. CrossRefGoogle Scholar
  24. Yaseen T, Sun DW, Cheng JH (2017) Raman imaging for food quality and safety evaluation: fundamentals and applications. Trends Food Sci Tech 62:177–189. CrossRefGoogle Scholar
  25. Yoshikawa S, Nagano C, Kanda M, Hayashi H, Matsushima Y, Nakajima T, Tsuruoka Y, Nagata M, Koike H, Sekimura K, Hashimoto T, Takano I, Shindo T (2017) Simultaneous determination of multi-class veterinary drugs in chicken processed foods and muscle using solid-supported liquid extraction clean-up. J Chromatogr B 1057:15–23. CrossRefGoogle Scholar
  26. Zajac A, Hanuza J, Dyminska L (2014) Raman spectroscopy in determination of horse meat content in the mixture with other meats. Food Chem 156:333–338. CrossRefGoogle Scholar
  27. Zhai C, Peng YK, Chao KL, Zhao J, Li YY, Li Y (2016) Design of line-scan Raman imaging system for nondestructive detection of agricultural and livestock products safety. Transactions of the Chinese Society of Agricultural Machinery 47:279–284. Google Scholar
  28. Zhai C, Peng YK, Li YY, Zhao J (2017) Detection of chemical additives in food using Raman chemical imaging system. Chem J Chinese U 38:369–375. Google Scholar
  29. Zhang Y, Zhou WE, Li SH, Ren ZQ, Li WQ, Zhou Y, Feng XS, Wu WJ, Zhang F (2016) A simple, accurate, time-saving and green method for the determination of 15 sulfonamides and metabolites in serum samples by ultra-high performance supercritical fluid chromatography. J Chromatogr A 1432:132–139. CrossRefGoogle Scholar
  30. Zhang ZM, Chen S, Liang YZ (2010) Baseline correction using adaptive iteratively reweighted penalized least squares. Analyst 135:1138–1146. CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Wenxiu Wang
    • 1
    • 2
  • Chen Zhai
    • 3
  • Yankun Peng
    • 1
    Email author
  • Kuanglin Chao
    • 4
  1. 1.National R&D Centre for Agro-Processing EquipmentsChina Agricultural UniversityBeijingChina
  2. 2.Agricultural University of HebeiBaodingChina
  3. 3.COFCO Nutrition and Health Research InstituteBeijing Key Laboratory of Nutrition Health and Food SafetyBeijingChina
  4. 4.Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of AgricultureBeltsvilleUSA

Personalised recommendations