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Raman spectroscopic techniques for meat analysis: A review

https://doi.org/10.21323/2414-438X-2022-7-2-97-111

Abstract

Raman spectroscopy (vibrational spectroscopy) proved to be an effective analytical approach in the field of geology, semiconductors, materials and polymers. Over the past decade, Raman spectroscopy has attracted the attention of researchers as a non-destructive, highly sensitive, fast and eco-friendly method and has demonstrated the unique capabilities of food analysis. The use of Raman spectroscopic methods (RSMs) to assess the quality of meat and finished products is rapidly expanding. From the analysis of one sample, you can get a large amount of information about the structure of proteins, the composition of fatty acids, organoleptic parameters, autolysis and spoilage indicators, authentication of raw materials, technological properties. An important advantage of the method is the comparability of the results obtained with the data of traditional analytical methods. Traditional methods of determining the quality of meat are often time-consuming, expensive and lead to irreversible damage to a sample. It is difficult to use them in production conditions directly on the meat processing lines. Technological advances have made it possible to develop portable Raman spectroscopes to use directly in production. The article presents the basic principles of Raman spectroscopy, system  atizes the results of the use of RSMs for the analysis of meat quality from different types of slaughter animals and provides tools for analyzing the data of the obtained spectra. Raman spectra have many dependent variables, so chemometric assays are used to work with them. Literature analysis has shown that currently there is no unified database of meat spectra in the world, standardized protocols for conducting research and processing the obtained results. In Russia, the use of RSMs is a new,

About the Authors

V. A. Pchelkina
V. M. Gorbatov Federal Research Center for Food Systems
Russian Federation

Viktoriya A. Pchelkina, Candidate of Technical Sciences, Senior Research Scientist, Experimental Clinic-Laboratory of Biologically Active Substances of Animal Origin

26, Talalikhina str., 109316, Moscow

 



I. M. Chernukha
V. M. Gorbatov Federal Research Center for Food Systems
Russian Federation

Irina M. Chernukha, Doctor of Technical Sciences, Professor, Academician of the Russian Academy of Sciences, Principal Researcher, Experimental Clinic-Laboratory of Biologically Active Substances of Animal Origin

26, Talalikhina str., 109316, Moscow



L. V. Fedulova
V. M. Gorbatov Federal Research Center for Food Systems
Russian Federation

Liliya V. Fedulova, Candidate of Technical Sciences, Head of the Experimental Clinic-Laboratory of Biologically Active Substances of Animal Origin

26, Talalikhina str., 109316, Moscow



N. A. Ilyin
V. M. Gorbatov Federal Research Center for Food Systems
Russian Federation

Nikolai A. Ilyin, Senior Laboratory Assistant, Experimental Clinic-Laboratory of Biologically Active Substances of Animal Origin

26, Talalikhina str., 109316, Moscow



References

1. DeSmet, S., Vossen, E. (2016). Meat: The balance between nutrition and health. A review. Meat Science, 120, 145–156. https://doi.org/10.1016/j.meatsci.2016.04.008

2. Shahbandeh, M. (2021). Meat consumption worldwide 1990– 2020, by meat type. Retrieved from https://www.statista.com/statistics/274522/global-per-capita-consumption-of-meat/. Accessed April 15, 2022

3. Kravchenko, V. (2022). Meat market: development continues. Livestock in Russia, 1, 11–13. (In Russian)

4. Barbon, A.P.A.D.C., Barbon, S., Campos, G.F.C., Seixas, J. L. Peres, L.M., Mastelini, S.M. et al. (2017). Development of a flexible computer vision system for marbling classification. Computers and Electronics in Agriculture, 142, 536–544. https://doi.org/10.1016/j.compag.2017.11.017

5. Khaled, A. Y., Parrish, C. A., Adedeji, A. (2021) Emerging nondestructive approaches for meat quality and safety evaluation — A review. Comprehensive Reviews in Food Science and Food Safety, 20, 3438–3463. https://doi.org/10.1111/1541-4337.12781

6. Kamruzzaman, M., Makino, Y., Oshita, S. (2015). Non-invasive analytical technology for the detection of contamination, adulteration, and authenticity of meat, poultry, and fish: a review. Analytica Chimica Acta, 853(1), 19–29. https://doi.org/10.1016/j.aca.2014.08.043

7. Grujić, R., Savanović, D. (2018). Analysis of myofibrillar and sarcoplasmic proteins in pork meatby capillary gel electrophoresis. Foods and Raw Materials, 6(2), 421–428. http://doi.org/10.21603/2308-4057-2018-2-421-428

8. Gecaj, R., Muji, S., Ajazi, F., Berisha, B., Kryeziu, A., Smaili, M. (2021). Investigation of pork meat in chicken- and beef-based commercial products by ELISA and real-time PCR sold at retail in Kosovo. Czech Journal of Food Sciences, 39(5), 368–375. https://doi.org/10.17221/164/2020-CJFS

9. Balog, J., Perenyi, D., Guallar-Hoyas, C., Egri, A., Pringle, S. D., Stead, S. et al. (2016). Identification of the species of origin for meat products by rapid evaporative ionization mass spectrometry. Journal of Agricultural and Food Chemistry, 64(23), 4793– 4800. https://doi.org/10.1021/acs.jafc.6b01041

10. Chiu, H.-H., Kuo, C.-H. (2019). Gas chromatography-mass spectrometry-based analytical strategies for fatty acid analysis in biological samples. Journal of Food and Drug Analysis, 28(1), 60–73. https://doi.org/10.1016/j.jfda.2019.10.003

11. Enomoto, H., Takeda, S. (2021). Mass spectrometry imaging of diacyl-, alkylacyl-, and plasmalogen-phosphatidylethanolamines in pork chop tissues. Journal of Food Measurement and Characterization. 15(6), 5047–5059. https://doi.org/10.1007/s11694-021-01075-6

12. Pham, T.H., Manful, C.F., Pumphrey, R.P., Hamilton, M.C., Adigun, O.A., Vidal, N.P. et al. (2021). Big game cervid meat as a potential good source of plasmalogens for functional foods. Journal of Food Composition and Analysis, 96, Article 103724. https://doi.org/10.1016/j.jfca.2020.103724

13. Golian, J., Benešov, L., Drdolov, Z., Martišov, P., Semjon, B., Kozelov, D. (2020). Molecular diagnostic test systems for meat identification: A comparison study of the MEAT 5.0 LCD-Array and innuDETECT Assay detection methods. Acta Veterinaria Brno, 89(1), 89–96. https://doi.org/10.2754/avb202089010089

14. Zdeňková, K., Akhatova, D, Fialová, E., Krupa, O., Kubica, L., Lencová, S. et al. (2018). Detection of meat adulteration: Use of efficient and routine-suited multiplex polymerase chain reaction-based methods for species authentication and quantification in meat products. Journal of Food and Nutrition Research, 57(4), 351–362.

15. Ivanov, A.V., Popravko, D.S., Safenkova, I.V., Zvereva, E.A., Dzantiev, B.B., Zherdev, A.V. (2021). Rapid full-cycle technique to control adulteration of meat products: Integration of accelerated sample preparation, recombinase polymerase amplification, and test-strip detection. Molecules, 26, Article 6804. https://doi.org/10.3390/molecules26226804

16. Chen, Y.-N., Sun, D.-W., Cheng, J.-H., Gao, W.-H. (2016). Recent advances for rapid identification of chemical information of muscle foods by hyperspectral imaging analysis. Food Engineering Reviews, 8(3), 336–350. https://doi.org/10.1007/s12393-016-9139-1

17. Teixeira, A., Silva, S., Rodrigues, S. (2019). Advances in sheep and goat meat products Research. Advances in Food and Nutrition Research, 87, 305–370. https://doi.org/10.1016/bs.afnr.2018.09.002

18. Narsaiah, K., Biswas, A.K., Mandal, P.K. (2019). Nondestructive methods for carcass and meat quality evaluation. Chapter in a book: Meat Quality Analysis: Advanced Evaluation Methods, Techniques, and Technologies. Academic Press, 2019. https://doi.org/10.1016/B978-0-12-819233-7.00003-3

19. Ekiz, B., Baygul, O., Yalcintan, H., Ozcan, M. (2020). Comparison of the decision tree, artificial neural network and multiple regression methods for prediction of carcass tissues composition of goat kids. Meat Science, 161, Article 108011. https://doi.org/10.1016/j.meatsci.2019.108011

20. Chapman, J., Elbourne, A., Truong, V.K., Cozzolino, D. (2020). Shining light into meat — a review on the recent advances in in vivo and carcass applications of near infrared spectroscopy. International Journal of Food Science and Technology, 55(3), 935– 941. https://doi.org/10.1111/ijfs.14367

21. Butler, H.J., Ashton, L., Bird, B., Cinque, G., Curtis, K., Dorney, J. et al. (2016). Using Raman spectroscopy to characterize biological materials. Nature Protocols, 11(4), 664–687. https://doi.org/10.1038/nprot.2016.036

22. Herrero, A. M. (2008). Raman spectroscopy for monitoring protein structure in muscle food systems. Critical Reviews in Food Science and Nutrition, 48(6), 512–523. https://doi.org/10.1080/10408390701537385

23. Ozaki, Y., Šašić, S. (2007). Introduction to Raman Spectroscopy. Chapter in a book: Pharmaceutical Applications of Raman Spectroscopy. John Wiley and Sons, 2007. https://doi.org/10.1002/9780470225882.ch1

24. Wang, K., Li, Z., Li, J., Lin, H. (2021). Raman spectroscopic techniques for nondestructive analysis of agri-foods: A state-ofthe-art review. Trends in Food Science and Technology, 118, 490– 504. https://doi.org/10.1016/j.tifs.2021.10.010

25. Bauer, A., Scheier, R., Eberle, T., Schmidt, H. (2016) Assessment of tenderness of aged bovine gluteus medius muscles using Raman spectroscopy. Meat Science, 115, 27–33. https://doi.org/10.1016/j.meatsci.2015.12.020

26. Fowler, S.M., Schmidt, H., van de Ven, R., Wynn, P., Hopkins, D.L. (2014). Raman spectroscopy compared against traditional predictors of shear force in lamb m. longissimus lumborum. Meat Science, 98(4), 652–656. https://doi.org/10.1016/j.meatsci.2014.06.042

27. Tomasevic, I., Nedeljkovic, A., Stanisic, N., Puda P. (2016). Authenticity assessment of cooked emulsified sausages using Raman spectroscopy and chemometrics. Fleischwirtschaft –Frankfurt, 3, 70–73.

28. Liberati, A., Altman, D.G., Tetzlaff, J., Mulrow, C., Gøtzsche, P.C., Ioannidis, J.P.A. et al. (2009). The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. Journal of Clinical Epidemology, 62(10), e1 — e34. https://doi.org/10.1016/j.jclinepi.2009.06.006

29. Singh, R. (2018). Celebrating the 90th Anniversary of the Raman Effect. Indian Journal of History of Science, 53(1), 50–58. https://doi.org/10.16943/ijhs/2018/v53i1/49363

30. Andryukov, B.G., Karpenko, A.A., Matosova, E.V., Lyapun, I.N. (2019). Raman spectroscopy as a modern diagnostic technology for study and indication of infectious agents (Review). Modern Technologies in Medicine, 11(4), 161–174. https://doi.org/10.17691/stm2019.11.4.19

31. Raman, C. V. Krishnan K. S. (1928). The negative absorption of radiation. Nature, 122(3062), 12–13. https://doi.org/10.1038/122012b0

32. Azhar, U. (2019). Spectroscopic imaging of multiplex bioassays encoded by raman and SERS Tags. A Thesis for the Degree of Doctor of Philosophy. The University of Adelaide, Australia, 2019.

33. Xu, Z., He, Z., Song, Y., Fu, X., Rommel, M., Luo, X. et al. (2018). Topic review: Application of Raman spectroscopy characterization in micro/nano-machining. Micromachines, 9, Article 361. https://doi.org/10.3390/mi9070361

34. Salgueiro, C.A., Dantas, J.M., Morgado, L. (2019). Principles of nuclear magnetic resonance and selected biological applications. Chapter in a book: Radiation in Bioanalysis, Bioanalysis, vol 8. Springer, Cham. https://doi.org/10.1007/978-3-030-28247-9_9

35. Long, D. A. (2002). Quantum mechanical theory of rayleigh and Raman scattering. Chapter in a book: The Raman effect: a unified treatment of the theory of Raman scattering by molecules. Chichester: John Wiley and Sons, 2002. https://doi.org/10.1002/0470845767.ch4

36. Zhang, W., Tang, Y., Shi, A., Bao, L., Shen, Y., Shen, R. et al. (2018). Recent developments in spectroscopic techniques for the detection of explosives. Materials, 11(8), Article 1364. https://doi.org/10.3390/ma11081364

37. McCreery, R.L. (2001). Raman spectroscopy for chemical analysis. Measurement Science and Technology, 55(9), Article 295. https://doi.org/10.1088/0957-0233/12/5/704

38. Piastek, J., Mach, J., Bardy, S., Édes, Z., Bartošík, M., Maniš, J. et al. (2022). Correlative Raman imaging and scanning electron microscopy: The role of single Ga islands in surface-enhanced Raman Spectroscopy of Graphene. The Journal of Physical Chemistry C, 129(6), 4508–4514. https://doi.org/10.48550/arXiv.2201.04377

39. Gomez, S., Egidi, F., Puglisi, A., Giovannini, T., Rossi, B., Cappelli, C. (2022). Unlocking the power of resonance Raman spectroscopy: The case of amides in aqueous solution. Journal of Molecular Liquids, 346, Article 117841. https://doi.org/10.1016/j.molliq.2021.117841

40. Smith, Z.J., Berger, A.J. (2008). Integrated Raman- and angular-scattering microscopy. Optics Letters, 33(7), 714–6. https://doi.org/10.1364/OL.33.000714

41. Wang, C., Pan, Y.-L., Videen, G. (2021). Optical trapping and laser-spectroscopy measurements of single particles in air: a review. Measurement Science and Technology, 32(10), Article 102005. https://doi.org/10.1088/1361-6501/ac0acf

42. Mosca, S., Conti, C., Stone, N., Matousek. P. (2021). Spatially offset Raman spectroscopy. Nature Reviews Methods Primers, 1, Article 21. https://doi.org/10.1038/s43586-021-00019-0

43. Barron, L.D., Hecht, L., McColl, I.H., Blanch, E.W. (2004). Raman optical activity comes of age. Molecular Physics, 102(8), 731–744. https://doi.org/10.1080/00268970410001704399

44. Misra, A.K., Sharma, S.K., Acosta, T.E., Porter, J.N., Bates, D.E. (2012). Single-pulse standoff Raman detection of chemicals from 120 m distance during daytime. Applied Spectroscopy, 66(11), 1279–1285. https://doi.org/10.1366/12-06617

45. Misra, A.K., Sharma, S.K., Kamemoto, L., Zinin, P.V., Yu, Q., Hu, N. et al. (2009). Novel micro-cavity substrates for improving the Raman signal from submicrometer size materials. Applied spectroscopy, 63(3), 373–377. https://doi.org/10.1366/000370209787598988

46. Rull, F., Vegas, A.T., Sansano, A., Sobrón, P. (2011). Analysis of Arctic ices by remote Raman spectroscopy. Spectrochimica Acta — Part A. Molecular and Biomolecular Spectroscopy, 80(1), 148–155. https://doi.org/10.1016/j.saa.2011.04.007

47. Li, S., Yuan, J., Li, F., Liu, Z. (2016). Local structures and the dissolving behavior of aqueous ammonia and its KCl and NH4Cl solutions: A Raman spectroscopy and X-ray scattering study. Spectrochimica Acta — Part A. Molecular and biomolecular spectroscopy, 162, 27–35. https://doi.org/10.1016/j.saa.2016.02.025

48. Romano, S., Zito, G., Managò, S., Calafiore, G.C., Penzo, E., Cabrini, S. et al. (2018). Surface-enhanced Raman and Fluorescence Spectroscopy with an all-dielectric metasurface. The Journal of Physical Chemistry C, 122(34), 19738–19745. https://doi.org/10.1021/acs.jpcc.8b03190

49. Lombardi, J.R., Birke, R.L. (2008). A Unified approach to surface-enhanced Raman spectroscopy. The Journal of Physical Chemistry C, 112(14), 5605–5617. https://doi.org/10.1021/jp800167v

50. Stefancu, A., Iancu, S.D., Leopold, N. (2021). Selective single molecule SERRS of cationic and anionic dyes by Cl– and Mg2+ adions: An old new idea. The Journal of Physical Chemistry C, 125(23), 12802–12810. https://doi.org/10.1021/acs.jpcc.1c03155

51. He, Z., Han, Z., Kizer, M.E., Linhardt, R.J., Wang, X., Sinyukov, A.M. et al. (2019). Tip-enhanced Raman imaging of single-stranded DNA with single base resolution. Journal of the American Chemical Society, 141(2), 753–757. https://doi.org/10.1021/jacs.8b11506

52. Lee, J., Crampton, K.T., Tallarida, N., Apkarian, V.A. (2019). Visualizing vibrational normal modes of a single molecule with atomically confined light. Nature, 568(7750), 78–82. https://doi.org/10.1038/s41586-019-1059-9

53. Zaccaria, R.P., De Angelis, F., Toma, A., Razzari, L., Alabastri, A., Das, G. et al. (2012). Surface plasmon polariton compression through radially and linearly polarized source. Optics Letters, 37(4), 545–547. https://doi.org/10.1364/OL.37.000545

54. Asakura, M., Okuno, M. (2021). Hyper-Raman spectroscopic investigation of amide bands of N-methylacetamide in liquid/solution phase. The Journal of Physical Chemistry Letters, 12(20), 4780–4785. https://doi.org/10.1021/acs.jpclett.1c01215

55. Zhao, H., Clemmen, S., Raza, A., Baets, R. (2018). Stimulated Raman spectroscopy of analytes evanescently probed by a silicon nitride photonic integrated waveguide. Optics Letters 43(6), 1403–1406. https://doi.org/10.1364/OL.43.001403

56. Takaya, T., Enokida, I., Furukawa, Y., Iwata, K. (2019). Direct observation of structure and dynamics of photogenerated charge carriers in poly(3-hexylthiophene) films by femtosecond time-resolved near-IR inverse Raman spectroscopy. Molecules, 24, Article 431. https://doi.org/10.3390/molecules24030431

57. Virga, A., Ferrante, C., Batignani, G., De Fazio, D., Nunn, A. D. G., Ferrari, A. C. et al. (2019). Coherent anti-Stokes Raman spectroscopy of single and multi-layer graphene. Nature Communications, 10(1), Article 3658. https://doi.org/10.1038/s41467-019-11165-1

58. Farias, G., Shur, J., Price, R., Bielski, E., Newman, B. (2021). A Systematic approach in the development of the morphologically-directed Raman spectroscopy methodology for characterizing nasal suspension drug roducts. AAPS Journal, 23(4), Article 73. https://doi.org/10.1208/s12248-021-00605-w

59. Fowler, S.M., Schmidt, H., Scheier, R., Hopkins, D.L. (2017) Raman spectroscopy for predicting meat quality traits. Chapter in a book: Advanced Technologies for Meat Processing. 2nd ed. CRC Press, Boca Raton, FL, USA, 2018.

60. Kucha, C.T., Liu, L., Ngadi, M.O. (2018). Non-destructive spectroscopic techniques and multivariate analysis for assessment of fat quality in pork and pork products: A review. Sensors (Switzerland), 18(2), Article 377. https://doi.org/10.3390/s18020377

61. Beganović, A., Hawthorne, L. M., Bach, K., Huck, C. W. (2019). Critical review on the utilization of handheld and portable Raman spectrometry in meat science. Foods, 8(2), Article 49. https://doi.org/10.3390/foods8020049

62. Silva, S., Guedes, C., Rodrigues, S., Teixeira, A. (2020). Non-destructive imaging and spectroscopic techniques for assessment of carcass and meat quality in sheep and goats: A review. Foods, 9(8), Article 1074. https://doi.org/10.3390/foods9081074

63. Shi, Y., Wang, X., Borhan, M.S., Young, J., Newman, D., Berg, E. et al. (2021). A review on meat quality evaluation methods based on non-destructive computer vision and artificial intelligence technologies. Food Science of Animal Resources, 41(4), 563–588. https://doi.org/10.5851/kosfa.2021.e25

64. Robert, C., Fraser-Miller, S.J., Jessep, W.T., Bain, W.E., Hicks, T.M., Ward, J.F. et al. (2021). Rapid discrimination of intact beef, venison and lamb meat using Raman spectroscopy. Food Chemistry, 343, Article 128441. https://doi.org/10.1016/j.foodchem.2020.128441

65. Herrero, A.M. (2008). Raman spectroscopy a promising technique for quality assessment of meat and fish: A review. Food Chemistry, 107(4), 1642–1651. https://doi.org/10.1016/j.foodchem.2007.10.014

66. Ostovar Pour, S., Fowler, S.M., Hopkins, D.L., Torley, P.J., Gill, H., Blanch, E.W. (2019). Investigation of chemical composition of meat using spatially off-set Raman spectroscopy. Analyst, 144(8), 2618–2627. https://doi.org/10.1039/c8an01958d

67. Ostovar Pour, S., Fowler, S.M., Hopkins, D.L., Torley, P., Gill, H., Blanch, E.W. (2020). Differentiating various beef cuts using spatially offset Raman spectroscopy Journal of Raman Spectroscopy, 51, 711–716. https://doi.org/10.1002/jrs.5830

68. Cama-Moncunill, R., Cafferky, J., Augier, C., Sweeney, T., Allen, P., Ferragina, A. et al. (2020). Prediction of Warner-Bratzler shear force, intramuscular fat, drip-loss and cook-loss in beef via Raman spectroscopy and chemometrics. Meat Science, 167, Article 108157. https://doi.org/10.1016/j.meatsci.2020.108157

69. Yang, H., Hopkins, D.L., Zhang, Y., Zhu, L., Dong, P., Wang, X. et al. (2020). Preliminary investigation of the use of Raman spectroscopy to predict beef spoilage in different types of packaging. Meat Science, 165, Article 108136. https://doi.org/10.1016/j.meatsci.2020.108136

70. Li, H., Haruna, S.A., Wang, Y., Mehedi Hassan, Md., Geng, W., Wu, X. et al. (2022). Simultaneous quantification of deoxymyoglobin and oxymyoglobin in pork by Raman spectroscopy coupled with multivariate calibration. Food Chemistry, 372, Article 131146. https://doi.org/10.1016/j.foodchem.2021.131146

71. Boyacı, İ.H., Temiz, H.T., Uysal, R.S., Velioğlu, H.M., Yadegari, R.J., Rishkan, M.M. (2014А). A novel method for discrimination of beef and horsemeat using Raman spectroscopy. Food Chemistry, 148, 37–41. https://doi.org/10.1016/j.foodchem.2013.10.006

72. Boyaci, I.H., Uysal, R.S., Temiz, T., Shendi, E.G., Yadegari, R.J., Rishkan, M.M. et al. (2014). A rapid method for determination of the origin of meat and meat products based on the extracted fat spectra by using of Raman spectroscopy and chemometric method. European Food Research and Technology, 238(5), 845–852. https://doi.org/10.1007/s00217-014-2168-1

73. Zając, A., Hanuza, J., Dymińska, L. (2014). Raman spectroscopy in determination of horse meat content in the mixture with other meats. Food Chemistry, 156, 333–338. https://doi.org/10.1016/j.foodchem.2014.02.002

74. Zhao, M., Nian, Y., Allen, P., Downey, G., Kerry, J.P., O’Donnell, C.P. (2018). Application of Raman spectroscopy and chemometric techniques to assess sensory characteristics of young dairy bull beef. Food Research International, 107, 27–40. https://doi.org/10.1016/j.foodres.2018.02.007

75. Lyndgaard, L.B., Sørensen, K.M., Van Der Berg, F., Engelsen, S.B. (2012). Depth profiling of porcine adipose tissue by Raman spectroscopy. Journal of Raman Spectroscopy, 43(4), 482–489. https://doi.org/10.1002/jrs.3067

76. Motoyama, M., Chikuni, K., Narita, T., Aikawa, K., Sasaki, K. (2013). In situ Raman spectrometric analysis of crystallinity and crystal polymorphism of fat in porcine adipose tissue. Journal of Agricultural and Food Chemistry, 61(1), 69–75. https://doi.org/10.1021/jf3034896

77. Liu, X., Schmidt, H., Mörlein, D. (2016). Feasibility of boar taint classification using a portable Raman device. Meat Science, 116, 133–139. https://doi.org/10.1016/j.meatsci.2016.02.015

78. Wang, Q., Lonergan, S.M., Yu, C. (2012). Rapid determination of pork sensory quality using Raman spectroscopy. Meat Science, 91(3), 232–239. https://doi.org/10.1016/j.meatsci.2012.01.017

79. Nache, M., Hinrichs, J., Scheier, R., Schmidt, H., Hitzmann, B. (2016). Prediction of the pH as indicator of porcine meat quality using Raman spectroscopy and metaheuristics. Chemometrics and Intelligent Laboratory Systems, 154, 45–51. https://doi.org/10.1016/j.chemolab.2016.03.011

80. Sowoidnich, K., Schmidt, H., Kronfeldt, H.-D., Schwägele, F. (2012). A portable 671 nm Raman sensor system for rapid meat spoilage identification. Vibrational Spectroscopy, 62, 70–76. https://doi.org/10.1016/j.vibspec.2012.04.002

81. Saleem, M., Amin, A., Irfan, M. (2021). Raman spectroscopy based characterization of cow, goat and buffalo fats. Journal of Food Science and Technology, 58(1), 234–243. https://doi.org/10.1007/s13197–020–04535-x

82. Zhu, D.-Y., Kang, Z.-L., Ma, H.-J., Xu, X.-L., Zhou, G.-H. (2018). Effect of sodium chloride or sodium bicarbonate in the chicken batters: A physico-chemical and Raman spectroscopy study. Food Hydrocolloids, 83, 222–228. https://doi.org/10.1016/j.foodhyd.2018.05.014

83. Berhe, D.T., Eskildsen, C.E., Lametsch, R., Hviid, M.S., van den Berg, F., Engelsen, S.B. (2016). Prediction of total fatty acid parameters and individual fatty acids in pork backfat using Raman spectroscopy and chemometrics: Understanding the cage of covariance between highly correlated fat parameters. Meat Science, 111, 18–26. https://doi.org/10.1016/j.meatsci.2015.08.009

84. Berhe, D.T., Lawaetz, A.J., Engelsen, S.B., Hviid, M.S., Lametsch, R. (2015). Accurate determination of endpoint temperature of cooked meat after storage by Raman spectroscopy and chemometrics. Food Control, 52, 119–125. https://doi.org/10.1016/j.foodcont.2014.12.011

85. Andersen, P.V., Afseth, N.K., Gjerlaug-Enger, E., Wold, J.P. (2021). Prediction of water holding capacity and pH in porcine longissimus lumborum using Raman spectroscopy. Meat Science, 172, Article 108357. https://doi.org/10.1016/j.meatsci.2020.108357

86. Nunes, K.M., Andrade, M.V.O., Almeida, M.R., Fantini, C., Sena, M.M. (2019). Raman spectroscopy and discriminant analysis applied to the detection of frauds in bovine meat by the addition of salts and carrageenan. Microchemical Journal, 147, 582–589. https://doi.org/10.1016/j.microc.2019.03.076

87. Chen, Q., Zhang, Y., Guo, Y., Cheng, Y., Qian, H., Yao, W. et al. (2019). Non-destructive prediction of texture of frozen/thaw raw beef by Raman spectroscopy. Journal of Food Engineering, 266, Article 109693. https://doi.org/10.1016/j.jfoodeng.2019.109693

88. Logan, B.G., Hopkins, D.L., Schmidtke, L., Morris, S., Fowler, S.M. (2020). Preliminary investigation into the use of Raman spectroscopy for the verification of Australian grass and grain fed beef. Meat Science, 160, Article 107970. https://doi.org/10.1016/j.meatsci.2019.107970

89. Nian, Y., Zhao, M., O’Donnell, C.P., Downey, G., Kerry, J.P., Allen, P. (2017). Assessment of physico-chemical traits related to eating quality of young dairy bull beef at different ageing times using Raman spectroscopy and chemometrics. Food Research International, 99, 778–789. https://doi.org/10.1016/j.Foodres.2017.06.056

90. Fowler, S.M., Schmidt, H., van de Ven, R., Hopkins, D.L. (2018). Preliminary investigation of the use of Raman spectroscopy to predict meat and eating quality traits of beef loins. Meat Science, 138, 53–58. https://doi.org/10.1016/j.meatsci.2018.01.002

91. Lee, J.-Y., Park, J.-H., Mun, H., Shim, W.-B., Lim, S.-H., Kim, M.-G. (2018). Quantitative analysis of lard in animal fat mixture using visible Raman spectroscopy. Food Chemistry, 254, 109–114. https://doi.org/10.1016/j.foodchem.2018.01.185

92. Fowler, S.M., Ponnampalam, E.N., Schmidt, H., Wynn, P., Hopkins, D.L. (2015). Prediction of intramuscular fat content and major fatty acid groups of lamb m. Longissimus lumborum using Raman spectroscopy. Meat Science, 110, 70–75. https://doi.org/10.1016/j.meatsci.2015.06.016

93. Schmidt, H., Scheier, R., Hopkins, D.L. (2013). Preliminary investigation on the relationship of Raman spectra of sheep meat with shear force and cooking loss. Meat Science, 93(1), 138– 143. https://doi.org/10.1016/j.meatsci.2012.08.019

94. Fowler, S.M., Schmidt, H., van de Ven, R., Wynn, P., Hopkins, D.L. (2014). Predicting tenderness of fresh ovine semimembranosus using Raman spectroscopy. Meat Science, 97(4), 597–601. https://doi.org/10.1016/j.meatsci.2014.02.018

95. Fowler, S.M., Schmidt, H., van de Ven, R., Wynn, P., Hopkins, D.L. (2015). Predicting meat quality traits of ovine m. semimembranosus, both fresh and following freezing and thawing, using a hand held Raman spectroscopic device. Meat Science, 108, 138–144. https://doi.org/10.1016/j.meatsci.2015.06.010

96. Andersen, P. V., Wold, J. P., Gjerlaug-Enger, E., Veiseth-Kent, E. (2018). Predicting post-mortem meat quality in porcine longissimus lumborum using Raman, near infrared and fluorescence spectroscopy. Meat Science, 145, 94–100. https://doi.org/10.1016/j.meatsci.2018.06.016

97. Martín-Gómez, A., Arroyo-Manzanares, N., García-Nicolás, M., López-Lorente, Á.I., Cárdenas, S., López-García, I. et al. (2021). Portable Raman spectrometer as a screening tool for characterization of Iberian dry-cured ham. Foods, 10(6), Article 1177. https://doi.org/10.3390/foods10061177

98. Beattie, J.R., Bell, S.E.J., Borggaard, C., Fearon, A.M., Moss, B.W. (2007). Classification of adipose tissue species using Raman spectroscopy. Lipids, 42(7), 679–685. https://doi.org/10.1007/s11745–007–3059-z

99. Tao, F., Ngadi, M. (2018). Recent advances in rapid and nondestructive determination of fat content and fatty acids composition of muscle foods. Critical Reviews in Food Science and Nutrition, 58(9), 1565–1593. https://doi.org/10.1080/10408398.2016.1261332

100. Santos, C.C., Zhao, J., Dong, X., Lonergan, S.M., Huff-Lonergan, E., Outhouse, A. et al. (2018). Predicting aged pork quality using a portable Raman device. Meat Science, 145, 79–85. https://doi.org/10.1016/j.meatsci.2018.05.021


Review

For citations:


Pchelkina V.A., Chernukha I.M., Fedulova L.V., Ilyin N.A. Raman spectroscopic techniques for meat analysis: A review. Theory and practice of meat processing. 2022;7(2):97-111. https://doi.org/10.21323/2414-438X-2022-7-2-97-111

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