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A Novel Hyperspectral Microscopic Imaging System for Evaluating Fresh Degree of Pork

  • Xu, Yi (School of Food and Biological Engineering, Jiangsu University) ;
  • Chen, Quansheng (School of Food and Biological Engineering, Jiangsu University) ;
  • Liu, Yan (School of Food and Biological Engineering, Jiangsu University) ;
  • Sun, Xin (Animal Science Department, North Dakota State University) ;
  • Huang, Qiping (School of Food and Biological Engineering, Jiangsu University) ;
  • Ouyang, Qin (School of Food and Biological Engineering, Jiangsu University) ;
  • Zhao, Jiewen (School of Food and Biological Engineering, Jiangsu University)
  • Received : 2018.01.02
  • Accepted : 2018.03.16
  • Published : 2018.04.30

Abstract

This study proposed a rapid microscopic examination method for pork freshness evaluation by using the self-assembled hyperspectral microscopic imaging (HMI) system with the help of feature extraction algorithm and pattern recognition methods. Pork samples were stored for different days ranging from 0 to 5 days and the freshness of samples was divided into three levels which were determined by total volatile basic nitrogen (TVB-N) content. Meanwhile, hyperspectral microscopic images of samples were acquired by HMI system and processed by the following steps for the further analysis. Firstly, characteristic hyperspectral microscopic images were extracted by using principal component analysis (PCA) and then texture features were selected based on the gray level co-occurrence matrix (GLCM). Next, features data were reduced dimensionality by fisher discriminant analysis (FDA) for further building classification model. Finally, compared with linear discriminant analysis (LDA) model and support vector machine (SVM) model, good back propagation artificial neural network (BP-ANN) model obtained the best freshness classification with a 100 % accuracy rating based on the extracted data. The results confirm that the fabricated HMI system combined with multivariate algorithms has ability to evaluate the fresh degree of pork accurately in the microscopic level, which plays an important role in animal food quality control.

Keywords

References

  1. Ahmed FE. 2005. Artificial neural networks for diagnosis and survival prediction in colon cancer. Mol Cancer 4:29. https://doi.org/10.1186/1476-4598-4-29
  2. Chen Q, Hu W, Su J, Li H, Ouyang Q, Zhao J. 2016. Nondestructively sensing of total viable count (TVC) in chicken using an artificial olfaction system based colorimetric sensor array. J Food Eng 168:259-266. https://doi.org/10.1016/j.jfoodeng.2015.08.003
  3. Chen Q, Hui Z, Zhao J, Ouyang Q. 2014. Evaluation of chicken freshness using a low-cost colorimetric sensor array with AdaBoost-OLDA classification algorithm. LWT - Food Sci Technol 57:502-507. https://doi.org/10.1016/j.lwt.2014.02.031
  4. Chen Q, Sun C, Ouyang Q, Wang Y, Liu A, Li H, Zhao J. 2015. Classification of different varieties of Oolong tea using novel artificial sensing tools and data fusion. LWT - Food Sci Technol 60:781-787. https://doi.org/10.1016/j.lwt.2014.10.017
  5. Cheng JH, Sun DW, Qu JH, Pu H, Zhang XC, Song Z, Chen X, Zhang H. 2016a. Developing a multispectral imaging for simultaneous prediction of freshness indicators during chemical spoilage of grass carp fish fillet. J Food Eng 182:9-17. https://doi.org/10.1016/j.jfoodeng.2016.02.004
  6. Cheng W, Sun DW, Pu H, Liu Y. 2016b. Integration of spectral and textural data for enhancing hyperspectral prediction of K value in pork meat. LWT-Food Sci Technol 72:322-329. https://doi.org/10.1016/j.lwt.2016.05.003
  7. China National Standard GB/T 5009.44-2003. Method for analysis of hygienic standard of meat and meat products. Available from: http://www.eshian.com/standards/13041.html.
  8. Chinese Standard GB/T 2707-2016. National standard for food safety - fresh (frozen) livestock and poultry products. Available from: http://www.eshian.com/standards/37292.html.
  9. Gaston E, Frias JM, Cullen PJ, O'Donnell CP, Gowen AA. 2010. Prediction of polyphenol oxidase activity using visible nearinfrared hyperspectral imaging on mushroom (Agaricus bisporus) caps. J Agric Food Chem 58:6226-6233. https://doi.org/10.1021/jf100501q
  10. Girolami A, Napolitano F, Faraone D, Braghieri A. 2013. Measurement of meat color using a computer vision system. Meat Sci 93:111-118. https://doi.org/10.1016/j.meatsci.2012.08.010
  11. Guo W, Gu J, Liu D, Shang L. 2016. Peach variety identification using near-infrared diffuse reflectance spectroscopy. Comput Electron Agr 123:297-303. https://doi.org/10.1016/j.compag.2016.03.005
  12. Holl L, Behr J, Vogel RF. 2016. Identification and growth dynamics of meat spoilage microorganisms in modified atmosphere packaged poultry meat by MALDI-TOF MS. Food Microbiol 60:84-91. https://doi.org/10.1016/j.fm.2016.07.003
  13. Huang L, Zhao J, Chen Q, Zhang Y. 2013. Rapid detection of total viable count (TVC) in pork meat by hyperspectral imaging. Food Res Int 54:821-828. https://doi.org/10.1016/j.foodres.2013.08.011
  14. Huang L, Zhao J, Chen Q, Zhang Y. 2014. Nondestructive measurement of total volatile basic nitrogen (TVB-N) in pork meat by integrating near infrared spectroscopy, computer vision and electronic nose techniques. Food Chem 145:228-236. https://doi.org/10.1016/j.foodchem.2013.06.073
  15. Huang Q, Chen Q, Li H, Huang G, Ouyang Q, Zhao J. 2015. Non-destructively sensing pork's freshness indicator using near infrared multispectral imaging technique. J Food Eng 154:69-75. https://doi.org/10.1016/j.jfoodeng.2015.01.006
  16. Jia W, Zhao D, Ding L. 2016. An optimized RBF neural network algorithm based on partial least squares and genetic algorithm for classification of small sample. Appl Soft Comput 48:373-384. https://doi.org/10.1016/j.asoc.2016.07.037
  17. Jiang B, Zhu X, Huang D, Paulson JA, Braatz RD. 2015. A combined canonical variate analysis and Fisher discriminant analysis (CVA-FDA) approach for fault diagnosis. Comput Chem Eng 77:1-9. https://doi.org/10.1016/j.compchemeng.2015.03.001
  18. Kamruzzaman M, Elmasry G, Sun DW, Allen P. 2012. Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis. Anal Chim Acta 714:57-67. https://doi.org/10.1016/j.aca.2011.11.037
  19. Khulal U, Zhao J, Hu W, Chen Q. 2016. Nondestructive quantifying total volatile basic nitrogen (TVB-N) content in chicken using hyperspectral imaging (HSI) technique combined with different data dimension reduction algorithms. Food Chem 197:1191-1199. https://doi.org/10.1016/j.foodchem.2015.11.084
  20. Kutsanedzie F, Chen Q, Hassan MM, Yang M, Sun H, Rahman MH. 2018. Near infrared system coupled chemometric algorithms for enumeration of total fungi count in cocoa beans neat solution. Food Chem 240:231-238. https://doi.org/10.1016/j.foodchem.2017.07.117
  21. Lopez-Garcia F, Andreu-Garcia G, Blasco J, Aleixos N, Valiente JM. 2010. Automatic detection of skin defects in citrus fruits using a multivariate image analysis approach. Comput Electron Agr 71:189-197. https://doi.org/10.1016/j.compag.2010.02.001
  22. Lee TL. 2004. Back-propagation neural network for long-term tidal predictions. Ocean Eng 31:225-238. https://doi.org/10.1016/S0029-8018(03)00115-X
  23. Li G, You J, Liu X. 2015a. Support vector machine (SVM) based prestack AVO inversion and its applications. J Appl Geophys 120:60-68. https://doi.org/10.1016/j.jappgeo.2015.06.009
  24. Li H, Chen Q, Zhao J, Wu M. 2015b. Nondestructive detection of total volatile basic nitrogen (TVB-N) content in pork meat by integrating hyperspectral imaging and colorimetric sensor combined with a nonlinear data fusion. LWT-Food Sci Technol 63:268-274. https://doi.org/10.1016/j.lwt.2015.03.052
  25. Li H, Kutsanedzie F, Zhao J, Chen Q. 2016a. Quantifying total viable count in pork meat using combined hyperspectral imaging and artificial olfaction techniques. Food Anal Method 9:3015-3024. https://doi.org/10.1007/s12161-016-0475-9
  26. Li H, Xin S, Pan W, Kutsanedzie F, Zhao J, Chen Q. 2016b. Feasibility study on nondestructively sensing meat's freshness using light scattering imaging technique. Meat Sci 119:102-109. https://doi.org/10.1016/j.meatsci.2016.04.031
  27. Li Q, Liu H, Wang Y, Sun Z, Guo F, Zhu J. 2014. Methyl green and nitrotetrazolium blue chloride co-expression in colon tissue: A hyperspectral microscopic imaging analysis. Opt Laser Technol 64:337-342. https://doi.org/10.1016/j.optlastec.2014.06.005
  28. Lin H, Chen Q, Zhao J, Zhou P. 2009. Determination of free amino acid content in Radix Pseudostellariae using near infrared (NIR) spectroscopy and different multivariate calibrations. J Pharm Biomed Anal 50:803-808. https://doi.org/10.1016/j.jpba.2009.06.040
  29. Liu L, Cozzolino D, Cynkar WU, Gishen M, Colby CB. 2006. Geographic classification of Spanish and Australian tempranillo red wines by visible and near-infrared spectroscopy combined with multivariate analysis. J Agric Food Chem 54:6754-6759. https://doi.org/10.1021/jf061528b
  30. Morales IR, Cebrian DR, Blanco EF, Sierra AP. 2016. Early warning in egg production curves from commercial hens: A SVM approach. Comput Electron Agr 121:169-179. https://doi.org/10.1016/j.compag.2015.12.009
  31. National Standard of PR China GB/T17236-2008. Operating procedures of pig-slaughtering. Available from: http://www.eshian.com/standards/9288.html.
  32. Olgun M, Onarcan AO, Ozkan K, Isik S, Sezer O, Ozgisi K, Ayter NG, Basciftci ZB, Ardic M, Koyuncu O. 2016. Wheat grain classification by using dense SIFT features with SVM classifier. Comput Electron Agr 122:185-190. https://doi.org/10.1016/j.compag.2016.01.033
  33. Ortac G, Bilgi AS, Tasdemir K, Kalkan H. 2016. A hyperspectral imaging based control system for quality assessment of dried figs. Comput Electron Agr 130:38-47. https://doi.org/10.1016/j.compag.2016.10.001
  34. Ouyang Q, Zhao J, Pan W, Chen Q. 2016. Real-time monitoring of process parameters in rice wine fermentation by a portable spectral analytical system combined with multivariate analysis. Food Chem 190:135-141. https://doi.org/10.1016/j.foodchem.2015.05.074
  35. Prieto N, Roehe R, Lavin P, Batten G, Andres S. 2009. Application of near infrared reflectance spectroscopy to predict meat and meat products quality: A review. Meat Sci 83:175-186. https://doi.org/10.1016/j.meatsci.2009.04.016
  36. Pu H, Sun DW, Ma J, Cheng JH. 2015. Classification of fresh and frozen-thawed pork muscles using visible and near infrared hyperspectral imaging and textural analysis. Meat Sci 99:81-88. https://doi.org/10.1016/j.meatsci.2014.09.001
  37. Qu JH, Cheng JH, Sun DW, Pu H, Wang QJ, Ma J. 2015. Discrimination of shelled shrimp (Metapenaeus ensis) among fresh, frozen-thawed and cold-stored by hyperspectral imaging technique. LWT-Food Sci Technol 62:202-209. https://doi.org/10.1016/j.lwt.2015.01.018
  38. Ring M, Eskofier BM. 2016. An approximation of the Gaussian RBF kernel for efficient classification with SVMs. Pattern Recogn Lett 84:107-113. https://doi.org/10.1016/j.patrec.2016.08.013
  39. Saraiva C, Vasconcelos H, de Almeida JMMMD. 2016. A chemometrics approach applied to Fourier transform infrared spectroscopy (FTIR) for monitoring the spoilage of fresh salmon (Salmo salar) stored under modified atmospheres. Int J Food Microbiol 241:331-339.
  40. Sharma A, Paliwal KK. 2008. Cancer classification by gradient LDA technique using microarray gene expression data. Data Knowl Eng 66:338-347. https://doi.org/10.1016/j.datak.2008.04.004
  41. Siqueira LFS, Junior RFA, de Araujo AA, Morais CLM, Lima KMG. 2017. LDA vs. QDA for FT-MIR prostate cancer tissue classification. Chemom Intell Lab Syst 162:123-129. https://doi.org/10.1016/j.chemolab.2017.01.021
  42. Sone I, Olsen RL, Sivertsen AH, Eilertsen G, Heia K. 2012. Classification of fresh Atlantic salmon (Salmo salar L.) fillets stored under different atmospheres by hyperspectral imaging. J Food Eng 109:482-489. https://doi.org/10.1016/j.jfoodeng.2011.11.001
  43. Su WH, Sun DW. 2016. Multivariate analysis of hyper/multi-spectra for determining volatile compounds and visualizing cooking degree during low-temperature baking of tubers. Comput Electron Agr 127:561-571. https://doi.org/10.1016/j.compag.2016.07.007
  44. Trebar M, Steele N. 2008. Application of distributed SVM architectures in classifying forest data cover types. Comput Electron Agr 63:119-130. https://doi.org/10.1016/j.compag.2008.02.001
  45. Uhr JW, Huebschman ML, Frenkel EP, Lane NL, Ashfaq R, Liu H, Rana DR, Cheng L, Lin AT, Hughes GA, Zhang XJ, Garner HR. 2012. Molecular profiling of individual tumor cells by hyperspectral microscopic imaging. Transl Res 159:366-375. https://doi.org/10.1016/j.trsl.2011.08.003
  46. Wu J, Peng Y, Li Y, Wang W, Chen J, Dhakal S. 2012. Prediction of beef quality attributes using VIS/NIR hyperspectral scattering imaging technique. J Food Eng 109:267-273. https://doi.org/10.1016/j.jfoodeng.2011.10.004
  47. Xiong Z, Sun DW, Pu H, Xie A, Han Z, Luo M. 2015. Non-destructive prediction of thiobarbituricacid reactive substances (TBARS) value for freshness evaluation of chicken meat using hyperspectral imaging. Food Chem 179:175-181. https://doi.org/10.1016/j.foodchem.2015.01.116
  48. Ye X, Iino K, Zhang S. 2016. Monitoring of bacterial contamination on chicken meat surface using a novel narrowband spectral index derived from hyperspectral imagery data. Meat Sci 122:25-31. https://doi.org/10.1016/j.meatsci.2016.07.015
  49. Yu L, Wang S, Lai KK. 2005. A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates. Comput Oper Res 32:2523-2541. https://doi.org/10.1016/j.cor.2004.06.024
  50. Zhao C, Gao F. 2015. A nested-loop fisher discriminant analysis algorithm. Chemom Intell Lab Syst 146:396-406. https://doi.org/10.1016/j.chemolab.2015.06.008
  51. Xiaobo Z, Jiyong S, Limin H, Jiewen Z, Hanpin M, Zhenwei C, Yanxiao L, Holmes M. 2011. In vivo noninvasive detection of chlorophyll distribution in cucumber (Cucumis sativus) leaves by indices based on hyperspectral imaging. Anal Chim Acta 706:105-112. https://doi.org/10.1016/j.aca.2011.08.026

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