Food Analytical Methods

, Volume 11, Issue 9, pp 2472–2484 | Cite as

Hyperspectral Imaging Sensing of Changes in Moisture Content and Color of Beef During Microwave Heating Process

  • Yuwei Liu
  • Da-Wen Sun
  • Jun-Hu Cheng
  • Zhong Han


Moisture content (MC) and color are two important quality parameters of beef during microwave heating process. This study examined the effects of microwave heating time (0–75 s) on MC, color, and myoglobins of beef samples. The results showed that heating time significantly influenced the MC, color (L*, a*), and percentage of related myoglobins. The suitability of hyperspectral imaging (HSI) (400–1000 nm) was investigated to correlate the mean spectra of beef samples and the color and MC values during microwave treatment. After the use of pre-processing methods and optimum wavelengths selection, the SG-SPA-LS-SVM prediction model for MC (R2P = 0.869, RMSEP = 1.304, and RPD = 2.724) and the SG-RC-MLR model for a* (R2P = 0.890, RMSEP = 0.735, and RPD = 2.733) were established. The models were then used to develop the distribution maps of MC and a* values, respectively, showing that both MC and a* at the center of the meat slices were higher than those at the edge, corresponding to the temperature distribution during microwave heating. The results demonstrated the ability of HSI system for monitoring the changes of some quality parameters during microwave heating.


Hyperspectral imaging Beef Microwave heating Moisture content Color Myoglobin 



The authors wish to acknowledge the contribution of undergraduate student Zhibin Liang for his assistance in the experiment.

Funding Information

The authors are grateful to the National Key R&D Program of China (2017YFD0400404) for its support. This research was also supported by the Natural Science Foundation of Guangdong Province (2017A030310558), the S&T Project of Guangzhou (201804010469), the China Postdoctoral Science Foundation (2017M612672), the Agricultural Development and Rural Work of Guangdong Province (2017LM4173), the S&T Project of Guangdong Province (2017B020207002), the Pearl River S&T Nova Program of Guangzhou (201610010104), the International and Hong Kong – Macau - Taiwan Collaborative Innovation Platform of Guangdong Province on Intelligent Food Quality Control and Process Technology & Equipment (2015KGJHZ001), the Guangdong Provincial R & D Centre for the Modern Agricultural Industry on Non-destructive Detection and Intensive Processing of Agricultural Products, the Common Technical Innovation Team of Guangdong Province on Preservation and Logistics of Agricultural Products (2016LM2154) and the Innovation Centre of Guangdong Province for Modern Agricultural Science and Technology on Intelligent Sensing and Precision Control of Agricultural Product Qualities.

Compliance with Ethical Standards

Conflict of Interest

Yuwei Liu declares that she has no conflict of interest. Da-Wen Sun declares that he has no conflict of interest. Jun-Hu Cheng declares that he has no conflict of interest. Zhong Han declares that he has no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants performed by any of the authors.

Informed Consent

Not applicable.


  1. Aaslyng MD, Bejerholm C, Ertbjerg P, Bertram HC, Andersen HJ (2003) Cooking loss and juiciness of pork in relation to raw meat quality and cooking procedure. Food Qual Prefer 14(4):277–288CrossRefGoogle Scholar
  2. Barbin DF, ElMasry G, Sun D-W, Allen P (2012) Predicting quality and sensory attributes of pork using near-infrared hyperspectral imaging. Anal Chim Acta 719:30–42CrossRefPubMedGoogle Scholar
  3. Bhattacharya M, Punathil L, Basak T (2017) A theoretical analysis on the effect of containers on the microwave heating of materials. Int Comm Heat and Mass Transfer 82:145–153CrossRefGoogle Scholar
  4. Burfoot D, Griffin WJ, James SJ (1988) Microwave pasteurisation of prepared meals. J Food Eng 8(3):145–156CrossRefGoogle Scholar
  5. Carlez A, Veciana-Nogues T, Cheftel J-C (1995) Changes in colour and myoglobin of minced beef meat due to high pressure processing. LWT Food Sci Technol 28(5):528–538CrossRefGoogle Scholar
  6. Cheng J-H, Sun D-W (2015) Rapid and non-invasive detection of fish microbial spoilage by visible and near infrared hyperspectral imaging and multivariate analysis. LWT Food Sci Technol 62:1060–1068CrossRefGoogle Scholar
  7. Cheng J-H, Sun D-W, Pu H-B, Wang Q-J, Chen Y-N (2015a) Suitability of hyperspectral imaging for rapid evaluation of thiobarbituric acid (TBA) value in grass carp (Ctenopharyngodon idella) fillet. Food Chem 171:258–265CrossRefPubMedGoogle Scholar
  8. Cheng J-H, Sun D-W, Pu H, Zhu Z (2015b) Development of hyperspectral imaging coupled with chemometric analysis to monitor K value for evaluation of chemical spoilage in fish fillets. Food Chem 185:245–253CrossRefPubMedGoogle Scholar
  9. Cheng W, Sun D-W, Cheng J-H (2016a) Pork biogenic amine index (BAI) determination based on chemometric analysis of hyperspectral imaging data. LWT Food Sci Technol 73:13–19CrossRefGoogle Scholar
  10. Cheng J-H, Sun D-W, Pu H (2016b) Combining the genetic algorithm and successive projection algorithm for the selection of feature wavelengths to evaluate exudative characteristics in frozen-thawed fish muscle. Food Chem 197:855–863CrossRefPubMedGoogle Scholar
  11. Cheng J-H, Sun D-W, Qu J-H, Pu H-B, Zhang X-C, Song Z, Chen X, Zhang H (2016c) Developing a multispectral imaging for simultaneous prediction of freshness indicators during chemical spoilage of grass carp fish fillet. J Food Eng 182:9–17CrossRefGoogle Scholar
  12. Cheng J-H, Sun D-W (2017a) Partial least squares regression (PLSR) applied to NIR and HSI spectral data modeling to predict chemical properties of fish muscle. Food Eng Rev 9:36–49CrossRefGoogle Scholar
  13. Cheng L, Sun D-W, Zhu Z, Zhang Z (2017b) Emerging techniques for assisting and accelerating food freezing processes: a review of recent research progresses. Crit Rev Food Sci Nutr 57:769–781CrossRefPubMedGoogle Scholar
  14. Choi YM, Ryu YC, Lee SH, Go GW, Shin HG, Kim KH, Rhee MS, Kim BC (2008) Effects of supercritical carbon dioxide treatment for sterilization purpose on meat quality of porcine longissimus dorsi muscle. LWT Food Sci Technol 41(2):317–322CrossRefGoogle Scholar
  15. Chun J-Y, Min S-G, Hong G-P (2014) Effects of high-pressure treatments on the redox state of porcine myoglobin and color stability of pork during cold storage. Food Bioprocess Technol 7(2):588–597CrossRefGoogle Scholar
  16. Cui ZW, Xu SY, Sun D-W (2003) Dehydration of garlic slices by combined microwave-vacuum and air drying. Dry Technol 21(7):1173–1184CrossRefGoogle Scholar
  17. Cui ZW, Xu SY, Sun D-W (2004a) Microwave-vacuum drying kinetics of carrot slices. J Food Eng 65(2):157–164CrossRefGoogle Scholar
  18. Cui ZW, Xu SY, Sun D-W (2004b) Effect of microwave-vacuum drying on the carotenoids retention of carrot slices and chlorophyll retention of Chinese chive leaves. Dry Technol 22(3):561–574CrossRefGoogle Scholar
  19. Cui ZW, Xu SY, Sun D-W, Chen W (2005) Temperature changes during microwave-vacuum drying of sliced carrots. Dry Technol 23(5):1057–1074CrossRefGoogle Scholar
  20. Dai Y, Miao J, Yuan S-Z, Liu Y, Li X-M, Dai R-T (2013) Colour and sarcoplasmic protein evaluation of pork following water bath and ohmic cooking. Meat Sci 93(4):898–905CrossRefPubMedGoogle Scholar
  21. Dai Q, Sun D-W, Cheng J-H, Pu H, Zeng X-A, Xiong Z (2014) Recent advances in De-noising methods and their applications in hyperspectral image processing for the food industry. Compr Rev Food Sci Food Saf 13(6):1207–1218CrossRefGoogle Scholar
  22. Dai Q, Cheng J-H, Sun D-W, Zhu Z, Pu H (2016) Prediction of total volatile basic nitrogen contents using wavelet features from visible/near-infrared hyperspectral images of prawn (Metapenaeus ensis). Food Chem 197:257–265CrossRefPubMedGoogle Scholar
  23. Desmond EM, Kenny TA, Ward P, Sun D-W (2000) Effect of rapid and conventional cooling methods on the quality of cooked ham joints. Meat Sci 56:271–277CrossRefPubMedGoogle Scholar
  24. Du CJ, Sun D-W (2005) Pizza sauce spread classification using colour vision and support vector machines. J Food Eng 66:137–145CrossRefGoogle Scholar
  25. ElMasry G, Barbin DF, Sun D-W, Allen P (2012) Meat quality evaluation by hyperspectral imaging technique: an overview. Crit Rev Food Sci Nutr 52(8):689–711CrossRefPubMedGoogle Scholar
  26. ElMasry G, Sun D-W, Allen P (2013) Chemical-free assessment and mapping of major constituents in beef using hyperspectral imaging. J Food Eng 117:235–246CrossRefGoogle Scholar
  27. García-Segovia P, Andrés-Bello A, Martínez-Monzó J (2007) Effect of cooking method on mechanical properties, color and structure of beef muscle (M. pectoralis). J Food Eng 80(3):813–821CrossRefGoogle Scholar
  28. He H-J, Wu D, Sun D-W (2014) Potential of hyperspectral imaging combined with chemometric analysis for assessing and visualising tenderness distribution in raw farmed salmon fillets. Journal of Food Engineering 126(Supplement C):156–164CrossRefGoogle Scholar
  29. Hu ZH, Sun D-W (2000) CFD simulation of heat and moisture transfer for predicting cooling rate and weight loss of cooked ham during air-blast chilling process. J Food Eng 46:189–197CrossRefGoogle Scholar
  30. Huff-Lonergan E, Lonergan SM (2005) Mechanisms of water-holding capacity of meat: the role of postmortem biochemical and structural changes. Meat Sci 71(1):194–204CrossRefPubMedGoogle Scholar
  31. Iqbal A, Sun D-W, Allen P (2013) Prediction of moisture, color and pH in cooked, pre-sliced turkey hams by NIR hyperspectral imaging system. J Food Eng 117(1):42–51CrossRefGoogle Scholar
  32. Jackman P, Sun D-W, Du C-J, Allen P (2008) Prediction of beef eating quality from colour, marbling and wavelet texture features. Meat Sci 80:1273–1281CrossRefPubMedGoogle Scholar
  33. Jackman P, Sun D-W, Allen P (2009a) Automatic segmentation of beef longissimus dorsi muscle and marbling by an adaptable algorithm. Meat Sci 83:187–194CrossRefPubMedGoogle Scholar
  34. Jackman P, Sun D-W, Du C-J, Allen P (2009b) Prediction of beef eating qualities from colour, marbling and wavelet surface texture features using homogenous carcass treatment. Pattern Recogn 42:751–763CrossRefGoogle Scholar
  35. Jackman P, Sun D-W, Allen P (2011) Recent advances in the use of computer vision technology in the quality assessment of fresh meats. Trends Food Sci Technol 22:185–197CrossRefGoogle Scholar
  36. Jeong JY, Lee ES, Choi JH, Lee JY, Kim JM, Min SG, Chae YC, Kim CJ (2007) Variability in temperature distribution and cooking properties of ground pork patties containing different fat level and with/without salt cooked by microwave energy. Meat Sci 75(3):415–422CrossRefPubMedGoogle Scholar
  37. Jouquand C, Tessier FJ, Bernard J, Marier D, Woodward K, Jacolot P, Gadonna-Widehem P, Laguerre J-C (2015) Optimization of microwave cooking of beef burgundy in terms of nutritional and organoleptic properties. LWT Food Sci Technol 60(1):271–276CrossRefGoogle Scholar
  38. Kandpal LM, Lee H, Kim MS, Mo C, Cho BK (2013) Hyperspectral reflectance imaging technique for visualization of moisture distribution in cooked chicken breast. Sensors (Basel) 13(10):13289–13300CrossRefGoogle Scholar
  39. Kiani H, Sun D-W, Delgado A, Zhang Z (2012) Investigation of the effect of power ultrasound on the nucleation of water during freezing of agar gel samples in tubing vials. Ultrason Sonochem 19:576–581CrossRefPubMedGoogle Scholar
  40. Krzywicki K (1982) The determination of haem pigments in meat. Meat Sci 7(1):29–36CrossRefPubMedGoogle Scholar
  41. Laycock L, Piyasena P, Mittal GS (2003) Radio frequency cooking of ground, comminuted and muscle meat products. Meat Sci 65(3):959–965CrossRefPubMedGoogle Scholar
  42. Li J-L, Sun D-W, Pu H, Jayas DS (2017) Determination of trace thiophanate-methyl and its metabolite carbendazim with teratogenic risk in red bell pepper (Capsicumannuum L.) by surface-enhanced Raman imaging technique. Food Chem 218:543–552CrossRefPubMedGoogle Scholar
  43. Lien R, Hunt MC, Anderson S, Kropf DH, Loughin TM, Dikeman ME, Velazco J (2002) Effects of endpoint temperature on the internal color of pork loin chops of different quality. J Food Sci 67(3):1007–1010CrossRefGoogle Scholar
  44. Lindahl G, Lundström K, Tornberg E (2001) Contribution of pigment content, myoglobin forms and internal reflectance to the colour of pork loin and ham from pure breed pigs. Meat Sci 59(2):141–151CrossRefPubMedGoogle Scholar
  45. Liu Y, Pu H, Sun D-W (2017) Hyperspectral imaging technique for evaluating food quality and safety during various processes: a review of recent applications. Trends Food Sci Technol 69:25–35CrossRefGoogle Scholar
  46. Ma J, Pu H, Sun D-W, Gao W, Qu J-H, Ma K-Y (2015) Application of Vis-NIR hyperspectral imaging in classification between fresh and frozen-thawed pork longissimus Dorsi muscles. Int J Refrig Rev Int Froid 50:10–18CrossRefGoogle Scholar
  47. Ma J, Sun D-W, Pu H (2016) Spectral absorption index in hyperspectral image analysis for predicting moisture contents in pork longissimus dorsi muscles. Food Chemistry 197(Part A):848–854CrossRefPubMedGoogle Scholar
  48. Mancini RA, Hunt MC (2005) Current research in meat color. Meat Sci 71(1):100–121CrossRefPubMedGoogle Scholar
  49. Marcos B, Kerry JP, Mullen AM (2010) High pressure induced changes on sarcoplasmic protein fraction and quality indicators. Meat Sci 85(1):115–120CrossRefPubMedGoogle Scholar
  50. McDonald K, Sun D-W (2001) The formation of pores and their effects in a cooked beef product on the efficiency of vacuum cooling. J Food Eng 47:175–183CrossRefGoogle Scholar
  51. McDonald K, Sun D-W, Kenny T (2001) The effect of injection level on the quality of a rapid vacuum cooled cooked beef product. J Food Eng 47:139–147CrossRefGoogle Scholar
  52. Pathare PB, Opara UL, Al-Said FA-J (2013) Colour measurement and analysis in fresh and processed foods: a review. Food Bioprocess Technol 6(1):36–60CrossRefGoogle Scholar
  53. Półtorak A, Wyrwisz J, Moczkowska M, Marcinkowska-Lesiak M, Stelmasiak A, Rafalska U, Wierzbicka A, Sun D-W (2015) Microwave vs. convection heating of bovine Gluteus Medius muscle: impact on selected physical properties of final product and cooking yield. Int J Food Sci Technol 50(4):958–965CrossRefGoogle Scholar
  54. Pu Y-Y, Sun D-W (2015) Vis-NIR hyperspectral imaging in visualizing moisture distribution of mango slices during microwave-vacuum drying. Food Chem 188:271–278CrossRefPubMedGoogle Scholar
  55. Pu H, Kamruzzaman M, Sun D-W (2015a) Selection of feature wavelengths for developing multispectral imaging systems for quality, safety and authenticity of muscle foods-a review. Trends Food Sci Technol 45:86–104CrossRefGoogle Scholar
  56. Pu H, Sun D-W, Ma J, Cheng J-H (2015b) Classification of fresh and frozen-thawed pork muscles using visible and near infrared hyperspectral imaging and textural analysis. Meat Sci 99:81–88CrossRefPubMedGoogle Scholar
  57. Pu H, Xie A, Sun D-W, Kamruzzaman M, Ma J (2015c) Application of wavelet analysis to spectral data for categorization of lamb muscles. Food Bioprocess Technol 8:1–16CrossRefGoogle Scholar
  58. Pu Y-Y, Sun D-W (2016) Prediction of moisture content uniformity of microwave-vacuum dried mangoes as affected by different shapes using NIR hyperspectral imaging. Innovative Food Sci Emerg Technol 34:348–356CrossRefGoogle Scholar
  59. Pu H, Liu D, Wang L, Sun D-W (2016) Soluble solids content and pH prediction and maturity discrimination of lychee fruits using visible and near infrared hyperspectral imaging. Food Anal Methods 9:235–244CrossRefGoogle Scholar
  60. Pu Y-Y, Sun D-W (2017) Combined hot-air and microwave-vacuum drying for improving drying uniformity of mango slices based on hyperspectral imaging visualization of moisture content distribution. Biosyst Eng 156:108–119CrossRefGoogle Scholar
  61. Qiao J, Wang N, Ngadi MO, Gunenc A, Monroy M, Gariépy C, Prasher SO (2007) Prediction of drip-loss, pH, and color for pork using a hyperspectral imaging technique. Meat Sci 76(1):1–8CrossRefPubMedGoogle Scholar
  62. Ramaswamy HS, Pillet-Will T (1992) Temperature distribution in microwave-heated food models. J Food Qual 15(6):435–448CrossRefGoogle Scholar
  63. Singh CB, Choudhary R, Jayas DS, Paliwal J (2008) Wavelet analysis of signals in agriculture and food quality inspection. Food Bioprocess Technol 3(1):2CrossRefGoogle Scholar
  64. Sivertsen AH, Kimiya T, Heia K (2011) Automatic freshness assessment of cod (Gadus morhua) fillets by Vis/Nir spectroscopy. J Food Eng 103(3):317–323CrossRefGoogle Scholar
  65. Suman SP, Faustman C, Stamer SL, Liebler DC (2007) Proteomics of lipid oxidation-induced oxidation of porcine and bovine oxymyoglobins. Proteomics 7(4):628–640CrossRefPubMedGoogle Scholar
  66. Sumnu G (2001) A review on microwave baking of foods. Int J Food Sci Technol 36(2):117–127CrossRefGoogle Scholar
  67. Sun D-W, Woods JL (1993) The moisture-content relative-humidity equilibrium relationship of wheat - a review. Dry Technol 11:1523–1551CrossRefGoogle Scholar
  68. Sun D-W, Woods JL (1994a) Low-temperature moisture transfer characteristics of barley - thin-layer models and equilibrium isotherms. J Agric Eng Res 59:273–283CrossRefGoogle Scholar
  69. Sun D-W, Woods JL (1994b) Low-temperature moisture transfer characteristics of wheat in thin-layers. Trans ASAE 37:1919–1926CrossRefGoogle Scholar
  70. Sun D-W, Woods JL (1994c) The selection of sorption isotherm equations for wheat-based on the fitting of available data. J Stored Prod Res 30:27–43CrossRefGoogle Scholar
  71. Sun D-W, Eames IW (1996) Performance characteristics of HCFC-123 ejector refrigeration cycles. Int J Energy Res 20:871–885CrossRefGoogle Scholar
  72. Sun D-W (1999) Comparison and selection of EMC ERH isotherm equations for rice. J Stored Prod Res 35:249–264CrossRefGoogle Scholar
  73. Sun D-W, Brosnan T (2003a) Pizza quality evaluation using computer vision - part 1 - Pizza base and sauce spread. J Food Eng 57:81–89CrossRefGoogle Scholar
  74. Sun D-W, Brosnan T (2003b) Pizza quality evaluation using computer vision - part 2 - pizza topping analysis. J Food Eng 57:91–95CrossRefGoogle Scholar
  75. Sun D-W (2004) Computer vision - an objective, rapid and non-contact quality evaluation tool for the food industry. J Food Eng 61:1–2CrossRefGoogle Scholar
  76. Swatland HJ (1997) Internal Fresnel reflectance from meat microstructure in relation to pork paleness and pH. Food Res Int 30(8):565–570CrossRefGoogle Scholar
  77. Turabi E, Sumnu G, Sahin S (2008) Optimization of baking of rice cakes in infrared -microwave combination oven by response surface methodology. Food Bioprocess Technol 1(1):64–73CrossRefGoogle Scholar
  78. Vadivambal R, Jayas DS (2010) Non-uniform temperature distribution during microwave heating of food materials—a review. Food Bioprocess Technol 3(2):161–171CrossRefGoogle Scholar
  79. Wang LJ, Sun D-W (2001) Rapid cooling of porous and moisture foods by using vacuum cooling technology. Trends Food Sci Technol 12:174–184CrossRefGoogle Scholar
  80. Wu D, He Y, Feng S (2008) Short-wave near-infrared spectroscopy analysis of major compounds in milk powder and wavelength assignment. Anal Chim Acta 610(2):232–242CrossRefPubMedGoogle Scholar
  81. Wu D, Shi H, Wang S, He Y, Bao Y, Liu K (2012a) Rapid prediction of moisture content of dehydrated prawns using online hyperspectral imaging system. Anal Chim Acta 726:57–66CrossRefPubMedGoogle Scholar
  82. Wu J, Peng Y, Li Y, Wang W, Chen J, Dhakal S (2012b) Prediction of beef quality attributes using VIS/NIR hyperspectral scattering imaging technique. J Food Eng 109(2):267–273CrossRefGoogle Scholar
  83. Wu D, Wang S, Wang N, Nie P, He Y, Sun D-W, Yao J (2013) Application of time series hyperspectral imaging (TS-HSI) for determining water distribution within beef and spectral kinetic analysis during dehydration. Food Bioprocess Technol 6(11):2943–2958CrossRefGoogle Scholar
  84. Xie A, Sun D-W, Xu Z, Zhu Z (2015) Rapid detection of frozen pork quality without thawing by Vis-NIR hyperspectral imaging technique. Talanta 139:208–215CrossRefPubMedGoogle Scholar
  85. Xiong Z, Sun D-W, Zeng X-A, Xie A (2014) Recent developments of hyperspectral imaging systems and their applications in detecting quality attributes of red meats: a review. J Food Eng 132:1–13CrossRefGoogle Scholar
  86. Xiong Z, Sun D-W, Pu H, Xie A, Han Z, Luo M (2015a) Non-destructive prediction of thiobarbituric acid reactive substances (TSARS) value for freshness evaluation of chicken meat using hyperspectral imaging. Food Chem 179:175–181CrossRefPubMedGoogle Scholar
  87. Xiong Z, Sun D-W, Xie A, Han Z, Wang L (2015b) Potential of hyperspectral imaging for rapid prediction of hydroxyproline content in chicken meat. Food Chem 175:417–422CrossRefPubMedGoogle Scholar
  88. Xu J-L, Riccioli C, Sun D-W (2016) Development of an alternative technique for rapid and accurate determination of fish caloric density based on hyperspectral imaging. J Food Eng 190:185–194CrossRefGoogle Scholar
  89. Xu J-L, Riccioli C, Sun D-W (2017) Comparison of hyperspectral imaging and computer vision for automatic differentiation of organically and conventionally farmed salmon. J Food Eng 196:170–182CrossRefGoogle Scholar
  90. Xu J-L, Sun D-W (2017) Identification of freezer burn on frozen salmon surface using hyperspectral imaging and computer vision combined with machine learning algorithm. Int J Refrig Rev Int Du Froid 74:151–164CrossRefGoogle Scholar
  91. Yang Q, Sun D-W, Cheng W (2017) Development of simplified models for nondestructive hyperspectral imaging monitoring of TVB-N contents in cured meat during drying process. J Food Eng 192:53–60CrossRefGoogle Scholar
  92. Yousefi S, Emam-Djomeh Z, Mousavi SMA, Askari GR (2012) Comparing the effects of microwave and conventional heating methods on the evaporation rate and quality attributes of pomegranate (punica granatum L.) juice concentrate. Food Bioprocess Technol 5(4):1328–1339CrossRefGoogle Scholar

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

Authors and Affiliations

  1. 1.School of Food Science and EngineeringSouth China University of TechnologyGuangzhouChina
  2. 2.Academy of Contemporary Food Engineering, Guangzhou Higher Education Mega CenterSouth China University of TechnologyGuangzhouChina
  3. 3.Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain FoodsGuangzhou Higher Education Mega CenterGuangzhouChina
  4. 4.Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science CentreUniversity College Dublin, National University of IrelandDublin 4Ireland

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