Journal of Biosystems Engineering

  • 제42권3호
  • /
  • Pages.170-189
  • /
  • 2017
  • /
  • 1738-1266(pISSN)
  • /
  • 2234-1862(eISSN)
한국농업기계학회 (Korean Society for Agricultural Machinery)
권호 표지
DOI QR코드

DOI QR Code

Raman Chemical Imaging Technology for Food and Agricultural Applications

  • Qin, Jianwei (USDA/ARS Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center) ;
  • Kim, Moon S. (USDA/ARS Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center) ;
  • Chao, Kuanglin (USDA/ARS Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center) ;
  • Cho, Byoung-Kwan (Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University)
  • 투고 : 2017.06.23
  • 심사 : 2017.08.14
  • 발행 : 2017.09.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Purpose: This paper presents Raman chemical imaging technology for inspecting food and agricultural products. Methods The paper puts emphasis on introducing and demonstrating Raman imaging techniques for practical uses in food analysis. Results & Conclusions: The main topics include Raman scattering principles, Raman spectroscopy measurement techniques (e.g., backscattering Raman spectroscopy, transmission Raman spectroscopy, and spatially offset Raman spectroscopy), Raman image acquisition methods (i.e., point-scan, line-scan, and area-scan methods), Raman imaging instruments (e.g., excitation sources, wavelength separation devices, detectors, imaging systems, and calibration methods), and Raman image processing and analysis techniques (e.g., fluorescence correction, mixture analysis, target identification, spatial mapping, and quantitative analysis). Raman chemical imaging applications for food safety and quality evaluation are also reviewed.

키워드

참고문헌

  1. Afseth, N.K., M. Bloomfield, J.P. Wold and P. Matousek. 2014. A novel approach for subsurface through-skin analysis of salmon using spatially offset Raman spectroscopy (SORS). Applied Spectroscopy 68(2): 255-262. https://doi.org/10.1366/13-07215
  2. Bailo, E. and V. Deckert. 2008. Tip-enhanced Raman scattering. Chemical Society Reviews 37(5): 921-930. https://doi.org/10.1039/b705967c
  3. Baranska, M. and H. Schulz. 2005. Spatial tissue distribution of polyacetylenes in carrot root. Analyst 130(6): 855-859. https://doi.org/10.1039/b500975h
  4. Baranska, M., H. Schulz and L.P. Christensen. 2006. Structural changes of polyacetylenes in American ginseng root can be observed in situ by using Raman spectroscopy. Journal of Agricultural and Food Chemistry 54(10): 3629-3635. https://doi.org/10.1021/jf060422d
  5. Chang, C.I. 2000. An information-theoretic approach to spectral variability, similarity, and discrimination for hyperspectral image analysis. IEEE Transactions on Information Theory 46(5): 1927-1932. https://doi.org/10.1109/18.857802
  6. Christensen, K.A. and M.D. Morris. 1998. Hyperspectral Raman microscopic imaging using Powell lens line illumination. Applied Spectroscopy 52(9): 1145-1147. https://doi.org/10.1366/0003702981945138
  7. Chu, H., Y. Huang and Y. Zhao. 2008. Silver nanorod arrays as a surface-enhanced Raman scattering substrate for foodborne pathogenic bacteria detection. Applied Spectroscopy 62(8): 922-931. https://doi.org/10.1366/000370208785284330
  8. Craig, A.P., A.S. Franca and J. Irudayaraj,. 2013. Surface-enhanced Raman spectroscopy applied to food safety. Annual Review of Food Science and Technology 4: 369-380. https://doi.org/10.1146/annurev-food-022811-101227
  9. Eksi-Kocak, H., O. Mentes‑Yilmaz and I.H. Boyaci. 2016. Detection of green pea adulteration in pistachio nut granules by using Raman hyperspectral imaging. European Food Research and Technology 242: 271-277. https://doi.org/10.1007/s00217-015-2538-3
  10. Guicheteau, J., S. Christesen, D. Emge and A. Tripathi. 2010. Bacterial mixture identification using Raman and surface-enhanced Raman chemical imaging. Journal of Raman Spectroscopy 41(12): 1632-1637. https://doi.org/10.1002/jrs.2601
  11. Herrero, A.M. 2008. Raman spectroscopy a promising technique for quality assessment of meat and fish: A review. Food Chemistry, 107: 1642-1651. https://doi.org/10.1016/j.foodchem.2007.10.014
  12. Hwang, J., S. Kang, K. Lee and H. Chung. 2012. Enhanced Raman spectroscopic discrimination of the geographical origins of rice samples via transmission spectral collection through packed grains. Talanta 101: 488-494. https://doi.org/10.1016/j.talanta.2012.10.001
  13. Kalasinsky, K.S., T. Hadfield, A.A. Shea, V.F. Kalasinsky, M.P. Nelson, J. Neiss, A.J. Drauch, G.S. Vanni and P.J. Treado. 2007. Raman chemical imaging spectroscopy reagentless detection and identification of pathogens: Signature development and evaluation. Analytical Chemistry 79(7): 2658-2673. https://doi.org/10.1021/ac0700575
  14. Krafft, C., C. Cervellati, C. Paetz, B. Schneider and J. Popp. 2012. Distribution of amygdalin in apricot (Prunus armeniaca) seeds studied by Raman microscopic imaging. Applied Spectroscopy 66(6): 644-649. https://doi.org/10.1366/11-06521
  15. Larmour, I.A., K. Faulds and D. Graham. 2010. Rapid Raman mapping for chocolate analysis. Analytical Methods 2(9): 1230-1232. https://doi.org/10.1039/c0ay00320d
  16. Li-Chan, E.C.Y. 1996. The applications of Raman spectroscopy in food science. Trends in Food Science & Technology 7(11): 361-370. https://doi.org/10.1016/S0924-2244(96)10037-6
  17. Lieber, C.A. and A. Mahadevan-Jansen. 2003. Automated method for subtraction of fluorescence from biological Raman spectra. Applied Spectroscopy 57(11): 1363-1367. https://doi.org/10.1366/000370203322554518
  18. Lin, M., L. He, J. Awika, L. Yang, D.R. Ledoux, H. Li and A. Mustapha. 2008. Detection of melamine in gluten, chicken feed, and processed foods using surface enhanced Raman spectroscopy and HPLC. Journal of Food Science 73(8): T129-T134. https://doi.org/10.1111/j.1750-3841.2008.00901.x
  19. Liu, B., G. Han, Z. Zhang, R. Liu, C. Jiang, S. Wang and M. Han. 2012. Shell thickness-dependent Raman enhancement for rapid identification and detection of pesticide residues at fruit peels. Analytical Chemistry 84(1): 255-261. https://doi.org/10.1021/ac202452t
  20. Liu, Y., K. Chao, M.S. Kim, D. Tuschel, O. Olkhovyk and R.J. Priore. 2009. Potential of Raman spectroscopy and imaging methods for rapid and routine screening of the presence of melamine in animal feed and foods. Applied Spectroscopy 63(4): 477-480. https://doi.org/10.1366/000370209787944398
  21. Markwort, L., B. Kip, E. Dasilva and B. Roussel. 1995. Raman imaging of heterogeneous polymers: A Comparison of Global Versus Point Illumination. Applied Spectroscopy 49(10): 1411-1430. https://doi.org/10.1366/0003702953965452
  22. Matousek, P., I.P. Clark, E.R.C. Draper, M.D. Morris, A.E. Goodship, N. Everall, M. Towrie, W.F. Finney and A.W Parker. 2005. Subsurface probing in diffusely scattering media using spatially offset Raman spectroscopy. Applied Spectroscopy 59(4): 393-400. https://doi.org/10.1366/0003702053641450
  23. McCreery, R.L. 2000. Raman Spectroscopy for Chemical Analysis. John Wiley and Sons, New York, NY, USA.
  24. Morris, H.R., C.C. Hoyt and P.J. Treado. 1994. Imaging spectrometers for fluorescence and Raman microscopy: Acousto-optic and liquid-crystal tunable filters. Applied Spectroscopy 48(7): 857-866. https://doi.org/10.1366/0003702944029820
  25. Pelletier, M.J. 2003. Quantitative analysis using Raman spectrometry. Applied Spectroscopy 57(1): 20A-42A. https://doi.org/10.1366/000370203321165133
  26. Piot, O., J.C. Autran and M. Manfait. 2000. Spatial distribution of protein and phenolic constituents in wheat grain as probed by confocal Raman microspectroscopy. Journal of Cereal Science 32(1): 57-71. https://doi.org/10.1006/jcrs.2000.0314
  27. Qin, J., K. Chao and M.S. Kim. 2010. Raman chemical imaging system for food safety and quality inspection. Transactions of the ASABE 53: 1873-1882. https://doi.org/10.13031/2013.35796
  28. Qin, J., K. Chao and M.S. Kim. 2011. Investigation of Raman chemical imaging for detection of lycopene changes in tomatoes during postharvest ripening. Journal of Food Engineering 107(3-4): 277-288. https://doi.org/10.1016/j.jfoodeng.2011.07.021
  29. Qin, J., K. Chao and M.S. Kim. 2012. Nondestructive evaluation of internal maturity of tomatoes using spatially offset Raman spectroscopy. Postharvest Biology and Technology 71: 21-31. https://doi.org/10.1016/j.postharvbio.2012.04.008
  30. Qin, J., K. Chao and M.S. Kim. 2013. Simultaneous detection of multiple adulterants in dry milk using macro-scale Raman chemical imaging. Food Chemistry 138(2-3): 998-1007. https://doi.org/10.1016/j.foodchem.2012.10.115
  31. Qin, J., K. Chao, B. Cho, Y. Peng and M.S. Kim. 2014a. High-throughput Raman chemical imaging for rapid evaluation of food safety and quality. Transactions of the ASABE 57(6): 1783-1792.
  32. Qin, J., K. Chao, M.S. Kim, H. Lee and Y. Peng. 2014b. Development of a Raman chemical imaging detection method for authenticating skim milk powder. Journal of Food Measurement and Characterization 8(2): 122-131. https://doi.org/10.1007/s11694-014-9172-9
  33. Qin, J., K. Chao and M.S. Kim. 2016a. Chapter 14: Raman scattering for food quality and safety assessment. In: Light Scattering Technology for Food Property, Quality and Safety Assessment, eds. R. Lu, pp. 387-428. Boca Raton, FL, USA: Taylor & Francis.
  34. Qin, J., K. Chao and M.S. Kim. 2016b. Chapter 6: Introduction to Raman chemical imaging technology. In: Computer Vision Technology for Food Quality Evaluation (2nd Edition), eds. D. Sun, pp. 141-171. San Diego, CA, USA: Elsevier.
  35. Qin, J., M.S. Kim, K. Chao, W.F. Schmidt, S. Dhakal, B. Cho, Y. Peng and M. Huang. 2017a. Subsurface inspection of food safety and quality using line-scan spatially offset Raman spectroscopy technique. Food Control, 75, 246-254. https://doi.org/10.1016/j.foodcont.2016.12.012
  36. Qin, J., M.S. Kim, K. Chao, W.F. Schmidt, B. Cho and S.R. Delwiche. 2017b. Line-scan Raman imaging and spectroscopy platform for surface and subsurface evaluation of food safety and quality. Journal of Food Engineering, 198, 17-27. https://doi.org/10.1016/j.jfoodeng.2016.11.016
  37. Roman, M., R. Baranski and M. Baranska. 2011. Nondestructive Raman analysis of polyacetylenes in apiaceae vegetables. Journal of Agricultural and Food Chemistry 59(14): 7647-7653. https://doi.org/10.1021/jf202366w
  38. Schulmerich, M.V., M.J. Walsh, M.K. Gelber, R. Kong, M.R. Kole, S.K. Harrison, J. McKinney, D. Thompson, L.S. Kull and R. Bhargava. 2012. Protein and oil composition predictions of single soybeans by transmission Raman spectroscopy. Journal of Agricultural and Food Chemistry 60(33): 8097-8102. https://doi.org/10.1021/jf301247w
  39. Schulz, H., M. Baranska and R. Baranski. 2005. Potential of NIR-FT-Raman spectroscopy in natural carotenoid analysis. Biopolymers 77(4): 212-221. https://doi.org/10.1002/bip.20215
  40. Schulze, G., A. Jirasek, M.M.L. Yu, A. Lim, R.F.B. Turner and M.W. Blades. 2005. Investigation of selected baseline removal techniques as candidates for automated implementation. Applied Spectroscopy 59(5): 545-574. https://doi.org/10.1366/0003702053945985
  41. Sharma, B., K. Ma, M.R. Glucksberg and R.P. Van Duyne. 2013. Seeing through bone with surface-enhanced spatially offset Raman spectroscopy. Journal of the American Chemical Society 135(46): 17290-17293. https://doi.org/10.1021/ja409378f
  42. Sharma, S.K., P.G. Lucey, M. Ghosh, H.W. Hubble and K.A. Horton. 2003. Stand-off Raman spectroscopic detection of minerals on planetary surfaces. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 59(10): 2391-2407. https://doi.org/10.1016/S1386-1425(03)00080-5
  43. Shin, K., H. Chung and C.W. Kwak. 2012. Transmission Raman measurement directly through packed corn kernels to improve sample representation and accuracy of compositional analysis. Analyst 137(16): 3690-3696. https://doi.org/10.1039/c2an35443h
  44. Smith, W.E. and G. Dent. 2005. Modern Raman Spectroscopy: A Practical Approach. John Wiley and Sons, Chichester, West Sussex, UK.
  45. Sowoidnich, K. and H.D. Kronfeldt. 2012. Shifted excitation Raman difference spectroscopy at multiple wavelengths for in-situ meat species differentiation. Applied Physics B: Lasers and Optics 108(4): 975-982. https://doi.org/10.1007/s00340-012-5160-0
  46. Stewart, S., R.J. Priore, M.P. Nelson and P.J. Treado. 2012. Raman imaging. Annual Review of Analytical Chemistry 5: 337-360. https://doi.org/10.1146/annurev-anchem-062011-143152
  47. Stone, N., K. Faulds, D. Graham and P. Matousek. 2010. Prospects of deep Raman spectroscopy for noninvasive detection of conjugated surface enhanced resonance Raman scattering nanoparticles buried within 25 mm of mammalian tissue. Analytical Chemistry 82(10): 3969-3973. https://doi.org/10.1021/ac100039c
  48. Stone, N., M. Kerssens, G.R. Lloyd, K. Faulds, D. Graham and P. Matousek. 2011. Surface enhanced spatially offset Raman spectroscopic (SESORS) imaging - the next dimension. Chemical Science 2(4): 776-780. https://doi.org/10.1039/c0sc00570c
  49. Strehle, M.A., P. Rosch, M. Baranska, H. Schulz and J. Popp. 2005. On the way to a quality control of the essential oil of fennel by means of Raman spectroscopy. Biopolymers 77(1): 44-52. https://doi.org/10.1002/bip.20178
  50. Sun, L., B.A. Simmons and S. Singh. 2011. Understanding tissue specific compositions of Bioenergy feedstocks through hyperspectral Raman imaging. Biotechnology and Bioengineering 108(2): 286-295. https://doi.org/10.1002/bit.22931
  51. Szymanska-Chargot, M., M. Chylinska, P.M. Pieczywek, P. Rosch, M. Schmitt, J. Popp and A. Zdunek. 2016. Raman imaging of changes in the polysaccharides distribution in the cell wall during apple fruit development and senescence. Planta, 243: 935-945. https://doi.org/10.1007/s00425-015-2456-4
  52. Windig, W. and J. Guilment. 1991. Interactive self-modeling mixture analysis. Analytical Chemistry 63(14): 1425-1432. https://doi.org/10.1021/ac00014a016
  53. Yang, D. and Y. Ying. 2011. Applications of Raman spectroscopy in agricultural products and food analysis: A review. Applied Spectroscopy Reviews 46(7): 539-560. https://doi.org/10.1080/05704928.2011.593216
  54. Zhang, Z., S. Chen and Y. Liang. 2010. Baseline correction using adaptive iteratively reweighted penalized least squares. Analyst 135(5): 1138-1146. https://doi.org/10.1039/b922045c