Skip to main content
SpringerLink
Log in

A comprehensive review of food rheology: analysis of experimental, computational, and machine learning techniques

  • Review Article
  • Published:
Korea-Australia Rheology Journal Aims and scope Submit manuscript

Abstract

The main objective of food rheology is to identify food structure and texture by rheological measurements, thereby reducing the requirement for sensory analysis in evaluating food products. However, determining food texture and structure exclusively from rheological measurements can be challenging because of the complicated composition and structure of food, as well as the complexities of factoring in the changes that occur during food mastication. This article provides a comprehensive review of the current experimental, computational and machine learning techniques used in food rheology to probe the structure and texture of food products. The textural attributes and structural information that can be inferred from each measurement technique is discussed and recent studies that carried out the measurements are highlighted. Also presented in this review are the recent progress in the experimental techniques and challenges.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  1. Nnyigide OS (2019) A Study of Bovine Serum Albumin (BSA) Hydrogel with Various Chemical Denaturants by Rheological Measurements and Molecular Dynamics Simulation, Ph.D. Thesis. Pusan National University.

  2. Borwankar RP (1992) Food texture and rheology: a tutorial review. J Food Eng 16:1–16. https://doi.org/10.1016/0260-8774(92)90016-Y

    Article  Google Scholar 

  3. Melito HS, Daubert CR (2011) Rheological innovations for characterizing food material properties. Annu Rev Food Sci Technol 2:153–179. https://doi.org/10.1146/annurev-food-022510-133626

    Article  CAS  Google Scholar 

  4. Tabilo-Munizaga G, Barbosa-Cánovas GV (2005) Rheology for the food industry. J Food Eng 67:147–156. https://doi.org/10.1016/j.jfoodeng.2004.05.062

    Article  Google Scholar 

  5. Ipsen R (2008) Food rheology: A personal view of the past and future. Annu trans Nord Rheol Soc 16

  6. Zhu Y, Gao H, Liu W, Zou L, McClements DJ (2020) A review of the rheological properties of dilute and concentrated food emulsions. J Texture Stud 51:45–55. https://doi.org/10.1111/jtxs.12444

    Article  CAS  Google Scholar 

  7. Diamante L, Umemoto M (2015) Rheological properties of fruits and vegetables: a review. Int J Food Prop 18(6):1191–1210. https://doi.org/10.1080/10942912.2014.898653

    Article  Google Scholar 

  8. Wang Y, Selomulya C (2022) Food rheology applications of large amplitude oscillation shear (LAOS). Trends Food Sci Technol 127:221–244. https://doi.org/10.1016/j.tifs.2022.05.018

    Article  CAS  Google Scholar 

  9. Joyner HS (2021) Nonlinear (large-amplitude oscillatory shear) rheological properties and their impact on food processing and quality. Annu Rev Food Sci Technol 12:591–609. https://doi.org/10.1146/annurev-food-061220-100714

    Article  CAS  Google Scholar 

  10. Nnyigide OS, Hyun K (2018) Effects of anionic and cationic surfactants on the rheological properties and kinetics of bovine serum albumin hydrogel. Rheol Acta 57:563–573. https://doi.org/10.1007/s00397-018-1100-1

    Article  CAS  Google Scholar 

  11. Nnyigide OS, Hyun K (2020) The rheological properties and gelation kinetics of corn starch/bovine serum albumin blend. Korea Aust Rheol J 32:71–78. https://doi.org/10.1007/s13367-020-0008-3

    Article  Google Scholar 

  12. Nnyigide OS, Hyun K (2018) Rheo-kinetics of bovine serum albumin in catanionic surfactant systems. Korean J Chem Eng 35:1969–1978. https://doi.org/10.1007/s11814-018-0128-3

    Article  CAS  Google Scholar 

  13. Faber TJ, Van Breemen LCA, McKinley GH (2017) From firm to fluid—structure-texture relations of filled gels probed under large amplitude oscillatory shear. J Food Eng 210:1–18. https://doi.org/10.1016/j.jfoodeng.2017.03.028

    Article  CAS  Google Scholar 

  14. Fancey KS (2005) A mechanical model for creep, recovery and stress relaxation in polymeric materials. J Mater Sci 40(18):4827–4831. https://doi.org/10.1007/s10853-005-2020-x

    Article  CAS  Google Scholar 

  15. Freer EM, Yim KS, Fuller GG, Radke CJ (2004) Interfacial rheology of globular and flexible proteins at the hexadecane/water interface: comparison of shear and dilatation deformation. J Phys Chem B 108(12):3835–3844. https://doi.org/10.1021/jp037236k

    Article  CAS  Google Scholar 

  16. Heyer P, Läuger J (2008) Interfacial Shear Rheology of Films Formed by Coffee. Annu trans Nord Rheol Soc 16.

  17. Joyner HS (2018) Explaining food texture through rheology. Curr Opin Food Sci 21:7–14. https://doi.org/10.1016/j.cofs.2018.04.003

    Article  Google Scholar 

  18. Hyun K, Wilhelm M, Klein CO, Cho KS, Nam JG, Ahn KH, Lee SJ, Ewoldt RH, McKinley GH (2011) A review of nonlinear oscillatory shear tests: analysis and application of large amplitude oscillatory shear (LAOS). Prog Polym Sci 36:1697–1753. https://doi.org/10.1016/j.progpolymsci.2011.02.002

    Article  CAS  Google Scholar 

  19. Du J, Ohtani H, Owens CE, Zhang L, Ellwood K, McKinley GH (2021) An improved capillary breakup extensional rheometer to characterize weakly rate-thickening fluids: applications in synthetic automotive oils. J Nonnewton Fluid Mech 291:104496. https://doi.org/10.1016/j.jnnfm.2021.104496

    Article  CAS  Google Scholar 

  20. Huang Q (2022) When polymer chains are highly aligned: a perspective on extensional rheology. Macromolecules 55(3):715–727. https://doi.org/10.1021/acs.macromol.1c02262

    Article  CAS  Google Scholar 

  21. Lee CW, Rogers SA (2017) A sequence of physical processes quantified in LAOS by continuous local measures. Korea-Aust Rheol J 29(4):269–279. https://doi.org/10.1007/s13367-017-0027-x

    Article  Google Scholar 

  22. Melito HS, Daubert CR, Foegeding EA (2013) Relating large amplitude oscillatory shear and food behavior: correlation of nonlinear viscoelastic, rheological, sensory and oral processing behavior of whey protein isolate/κ-carrageenan gels: relating LAOS and food behavior. J Food Process Eng 36(4):521–534. https://doi.org/10.1111/jfpe.12015

    Article  CAS  Google Scholar 

  23. Yue Q, Li M, Liu C, Li L, Zheng X, Bian K (2020) Comparison of uniaxial/biaxial extensional rheological properties of mixed dough with traditional rheological test results: relationship with the quality of steamed bread. Int J Food Sci Technol 55(7):2751–2761. https://doi.org/10.1111/ijfs.14528

    Article  CAS  Google Scholar 

  24. Del Nobile MA, Chillo S, Mentana A, Baiano A (2007) Use of the generalized maxwell model for describing the stress relaxation behavior of solid-like foods. J Food Eng 78(3):978–983. https://doi.org/10.1016/j.jfoodeng.2005.12.011

    Article  Google Scholar 

  25. Vithanage CR, Grimson MJ, Smith BG, Wills PR (2011) Creep test observation of viscoelastic failure of edible fats. J Phys Conf Ser 286:012008. https://doi.org/10.1088/1742-6596/286/1/012008

    Article  CAS  Google Scholar 

  26. Yu C, Gunasekaran S (2001) Correlation of dynamic and steady flow viscosities of food materials. Appl Rheol 11(3):134–140. https://doi.org/10.1515/arh-2001-0008

    Article  Google Scholar 

  27. Song HY, Nnyigide OS, Salehiyan R, Hyun K (2016) Investigation of nonlinear rheological behavior of linear and 3-arm star 1,4-Cis-polyisoprene (PI) under medium amplitude oscillatory shear (MAOS) flow via FT-rheology. Polymer 104:268–278. https://doi.org/10.1016/j.polymer.2016.04.052

    Article  CAS  Google Scholar 

  28. Hyun K, Kim SH, Ahn KH, Lee SJ (2002) Large amplitude oscillatory shear as a way to classify the complex fluids. J Nonnewton Fluid Mech 107(1):51–65. https://doi.org/10.1016/S0377-0257(02)00141-6

    Article  CAS  Google Scholar 

  29. Song HY, Park SY, Kim S, Youn HJ, Hyun K (2022) Linear and nonlinear oscillatory rheology of chemically pretreated and non-pretreated cellulose nanofiber suspensions. Carbohydr Polym 275:118765. https://doi.org/10.1016/j.carbpol.2021.118765

    Article  CAS  Google Scholar 

  30. Van Den Berg L, Jan Klok H, Van Vliet T, Van Der Linden E, Van Boekel MAJS, Van De Velde F (2008) Quantification of a 3D structural evolution of food composites under large deformations using microrheology. Food Hydrocoll 22(8):1574–1583. https://doi.org/10.1016/j.foodhyd.2007.11.002

    Article  CAS  Google Scholar 

  31. Rogers SA (2017) In search of physical meaning: defining transient parameters for nonlinear viscoelasticity. Rheol Acta 56(5):501–525. https://doi.org/10.1007/s00397-017-1008-1

    Article  CAS  Google Scholar 

  32. Wilhelm M (2002) Fourier-Transform Rheology. Macromol Mater Eng 287:83–105. https://doi.org/10.1002/1439-2054(20020201)287:2%3c83::AID-MAME83%3e3.0.CO;2-B

    Article  CAS  Google Scholar 

  33. Kohyama K, Ishihara S, Nakauma M, Funami T (2021) Fracture phenomena of soft gellan gum gels during compression with artificial tongues. Food Hydrocoll 112:106283. https://doi.org/10.1016/j.foodhyd.2020.106283

    Article  CAS  Google Scholar 

  34. Ishihara S, Isono M, Nakao S, Nakauma M, Funami T, Hori K, Ono T, Kohyama K, Nishinari K (2014) Instrumental uniaxial compression test of gellan gels of various mechanical properties using artificial tongue and its comparison with human oral strategy for the first size reduction. J Texture Stud 45:354–366. https://doi.org/10.1111/jtxs.12080

    Article  Google Scholar 

  35. Zhou B, Tobin JT, Drusch S, Hogan SA (2021) Dynamic adsorption and interfacial rheology of whey protein isolate at oil-water interfaces: effects of protein concentration. PH and Heat Treatment Food Hydrocoll 116:106640. https://doi.org/10.1016/j.foodhyd.2021.106640

    Article  CAS  Google Scholar 

  36. Moore PB, Langley K, Wilde PJ, Fillery-Travis A, Mela DJ (1998) Effect of emulsifier type on sensory properties of oil-in-water emulsions. J Sci Food Agric 76:469–476. https://doi.org/10.1002/(SICI)1097-0010(199803)76:3%3c469::AID-JSFA974%3e3.0.CO;2-Y

    Article  CAS  Google Scholar 

  37. El Omari Y, Yousfi M, Duchet-Rumeau J, Maazouz A (2022) Recent advances in the interfacial shear and dilational rheology of polymer systems: from fundamentals to applications. Polymers 14(14):2844. https://doi.org/10.3390/polym14142844

    Article  CAS  Google Scholar 

  38. Murray BS (2002) Interfacial rheology of food emulsifiers and proteins. Curr Opin Colloid Interface 7:426–431. https://doi.org/10.1016/S1359-0294(02)00077-8

    Article  CAS  Google Scholar 

  39. Zhou Y, Mei Y, Luo T, Chen W, Zhong Q, Chen H, Chen W (2021) Study on the relationship between emulsion properties and interfacial rheology of sugar beet pectin modified by different enzymes. Molecules 26(9):2829. https://doi.org/10.3390/molecules26092829

    Article  CAS  Google Scholar 

  40. Wang S, Zhou B, Yang X, Niu L, Li S (2022) Tannic acid enhanced the emulsion stability, rheology and interface characteristics of clanis bilineata tingtauica mell protein stabilised oil-in-water emulsion. Int J of Food Sci Tech 57(8):5228–5238. https://doi.org/10.1111/ijfs.15839

    Article  CAS  Google Scholar 

  41. Wang S, Yang J, Shao G, Qu D, Zhao H, Yang L, Zhu L, He Y, Liu H, Zhu D (2020) Soy protein isolated-soy hull polysaccharides stabilized O/W emulsion: effect of polysaccharides concentration on the storage stability and interfacial rheological properties. Food Hydrocoll 101:105490. https://doi.org/10.1016/j.foodhyd.2019.105490

    Article  CAS  Google Scholar 

  42. Yang J, Thielen I, Berton-Carabin CC, van der Linden E, Sagis LMC (2020) Nonlinear interfacial rheology and atomic force microscopy of air-water interfaces stabilized by whey protein beads and their constituents. Food Hydrocoll 101:105466. https://doi.org/10.1016/j.foodhyd.2019.105466

    Article  CAS  Google Scholar 

  43. Geonzon LC, Kobayashi M, Tassieri M, Bacabac RG, Adachi Y, Matsukawa S (2023) Microrheological properties and local structure of ι-carrageenan gels probed by using optical tweezers. Food Hydrocoll 137:108325. https://doi.org/10.1016/j.foodhyd.2022.108325

    Article  CAS  Google Scholar 

  44. Mettu S, Zhou M, Tardy BL, Ashokkumar M, Dagastine RR (2016) Temperature dependent mechanical properties of air, oil and water filled microcapsules studied by atomic force microscopy. Polymer 102:333–341. https://doi.org/10.1016/j.polymer.2016.02.046

    Article  CAS  Google Scholar 

  45. Papagiannopoulos A, Sotiropoulos K, Pispas S (2016) Particle tracking microrheology of the power-law viscoelasticity of xanthan solutions. Food Hydrocoll 61:201–210. https://doi.org/10.1016/j.foodhyd.2016.05.020

    Article  CAS  Google Scholar 

  46. Jin H, Chen J, Zhang J, Sheng L (2021) Impact of phosphates on heat-induced egg white gel properties: Texture, water state, micro-rheology and microstructure. Food Hydrocoll 110:106200. https://doi.org/10.1016/j.foodhyd.2020.106200

    Article  CAS  Google Scholar 

  47. Bakhsh A, Elobeid T, Avci E, Demirci M, Taylan O, Ozmen D, Meral R, Yilmaz MT (2022) A tracer microrheology for determination of viscoelasticity of dilute ovalbumin colloids. Emerg Mater Res 11(1):98–104. https://doi.org/10.1680/jemmr.20.00282

    Article  Google Scholar 

  48. Xu J, Boddu VM, Liu SX (2023) Microrheological investigation of low-viscosity barley β-glucan (LVBBG) solutions by diffusion wave spectroscopy (DWS). Am J Food Technol 18:8–15. https://doi.org/10.3923/ajft.2023.8.15

    Article  CAS  Google Scholar 

  49. Yang N, Lv R, Jia J, Nishinari K, Fang Y (2017) Application of microrheology in food science. Annu Rev Food Sci Technol 8(1):493–521. https://doi.org/10.1146/annurev-food-030216-025859

    Article  CAS  Google Scholar 

  50. Nath PC, Debnath S, Sridhar K, Inbaraj BS, Nayak PK, Sharma M (2022) A comprehensive review of food hydrogels: principles, formation mechanisms, microstructure, and its applications. Gels 9(1):1. https://doi.org/10.3390/gels9010001

    Article  CAS  Google Scholar 

  51. Lodge JFM, Heyes DM (1999) Rheology of transient colloidal gels by brownian dynamics computer simulation. J Rheol 4(1):219–244. https://doi.org/10.1122/1.550984

    Article  Google Scholar 

  52. Whittle M, Dickinso E (1997) Brownian Dynamics simulation of gelation in soft sphere systems with irreversible bond formation. Mol Phys 90(5):739–758. https://doi.org/10.1080/002689797172101

    Article  CAS  Google Scholar 

  53. Carpen IC, Brady JF (2005) Microrheology of colloidal dispersions by brownian dynamics simulations. J Rheol 49(6):1483–1502. https://doi.org/10.1122/1.2085174

    Article  CAS  Google Scholar 

  54. Santos PHS, Campanella OH, Carignano MA (2010) Brownian dynamics study of gel-forming colloidal particles. J Phys Chem B 114(41):13052–13058. https://doi.org/10.1021/jp105711y

    Article  CAS  Google Scholar 

  55. Liu L, Zhang Y, Dao L, Huang X, Qiu R, Pang P, Wu S (2023) Efficient and accurate multi-scale simulation for viscosity mechanism of konjac glucomannan colloids. Int J Biol Macromol 236:123992. https://doi.org/10.1016/j.ijbiomac.2023.123992

    Article  CAS  Google Scholar 

  56. Azevedo TN, Rizzi LG (2020) Microrheology of filament networks from brownian dynamics simulations. J Phys Conf Ser 1483(1):012001. https://doi.org/10.1088/1742-6596/1483/1/012001

    Article  Google Scholar 

  57. Chen JY, Li Z, Szlufarska I, Klingenberg DJ (2021) Rheology and structure of suspensions of spherocylinders via brownian dynamics simulations. J Rheol 65(2):273–288. https://doi.org/10.1122/8.0000155

    Article  CAS  Google Scholar 

  58. Park JD, Rogers SA (2020) Rheological manifestation of microstructural change of colloidal gel under oscillatory shear flow. Phys Fluids 32(6):063102. https://doi.org/10.1063/5.0006792

    Article  CAS  Google Scholar 

  59. Sánchez-Diáz LE, Iwashita T, Egami T, Chen WR (2019) Connection between the anisotropic structure and nonlinear rheology of sheared colloidal suspensions investigated by brownian dynamics simulations. J Phys Commun 3(5):055018. https://doi.org/10.1088/2399-6528/ab1e79

    Article  CAS  Google Scholar 

  60. Liu L, Zhou N, Yang Y, Huang X, Qiu R, Pang J, Wu S (2022) Rheological properties of konjac glucomannan composite colloids in strong shear flow affected by mesoscopic structures: Multi-scale simulation and experiment. Colloids Surf A Physicochem Eng Asp 652:129850. https://doi.org/10.1016/j.colsurfa.2022.129850

    Article  CAS  Google Scholar 

  61. Nnyigide OS, Lee SG, Hyun K (2018) Exploring the differences and similarities between urea and thermally driven denaturation of bovine serum albumin: intermolecular forces and solvation preferences. J Mol Model 24:75. https://doi.org/10.1007/s00894-018-3622-y

    Article  CAS  Google Scholar 

  62. Nnyigide OS, Hyun K (2021) Molecular dynamics studies of the protective and destructive effects of sodium dodecyl sulfate in thermal denaturation of hen egg-white lysozyme and bovine serum albumin. J Biomol Struct Dyn 39:1106–1120. https://doi.org/10.1080/07391102.2020.1726209

    Article  CAS  Google Scholar 

  63. Nnyigide OS, Lee SG, Hyun K (2019) In Silico Characterization of the binding modes of surfactants with bovine serum albumin. Sci Rep 9:10643. https://doi.org/10.1038/s41598-019-47135-2

    Article  CAS  Google Scholar 

  64. Nnyigide OS, Nnyigide TO, Lee SG, Hyun K (2022) Protein repair and analysis server: a web server to repair PDB structures, add missing heavy atoms and hydrogen atoms, and assign secondary structures by amide interactions. J Chem Inf Model 62:4232–4246. https://doi.org/10.1021/acs.jcim.2c00571

    Article  CAS  Google Scholar 

  65. Ore Areche F, Flores DDC, Quispe-Solano MA, Nayik GA, Cruz-Porta EADL, Rodríguez AR, Roman AV, Chweya R (2023) Formulation, characterization, and determination of the rheological profile of loquat compote Mespilus Germánica L. through sustenance artificial intelligence. J Food Qual 2023:1–12. https://doi.org/10.1155/2023/3344539

    Article  CAS  Google Scholar 

  66. Lee S, Kim SR, Lee HJ, Kim BS, Oh H, Lee JB, Park K, Yi YJ, Park CH, Park JD (2022) Predictive model for the spreadability of cosmetic formulations based on large amplitude oscillatory shear (LAOS) and machine learning. Phys Fluids 34(10):103109. https://doi.org/10.1063/5.0117989

    Article  CAS  Google Scholar 

  67. Schmidt J, Marques MRG, Botti S, Marques MAL (2019) Recent advances and applications of machine learning in solid-state materials science. npj Comput Mater 5(1): 83. https://doi.org/10.1038/s41524-019-0221-0

  68. Guiné, RPF (2019) The use of artificial neural networks (ANN) in food process engineering. Int J Food Eng 5: 15–21. https://doi.org/10.18178/ijfe.5.1.15-21

  69. Abang Zaidel DN, Chin NL, Yusof YA (2010) A Review on rheological properties and measurements of dough and gluten. J Appl Sci 10:2478–2490. https://doi.org/10.3923/jas.2010.2478.2490

    Article  CAS  Google Scholar 

  70. Xia W, Siu WK, Sagis LMC (2021) Linear and non-linear rheology of heat-set soy protein gels: effects of selective proteolysis of β-conglycinin and glycinin. Food Hydrocoll 120:106962. https://doi.org/10.1016/j.foodhyd.2021.106962

    Article  CAS  Google Scholar 

  71. Deblais A, Hollander ED, Boucon C, Blok AE, Veltkamp B, Voudouris P, Versluis P, Kim HJ, Mellema M, Stieger M, Bonn D, Velikov KP (2021) Predicting thickness perception of liquid food products from their non-newtonian rheology. Nat Commun 12(1):6328. https://doi.org/10.1038/s41467-021-26687-w

    Article  CAS  Google Scholar 

  72. Agoda-Tandjawa G, Le Garnec C, Boulenguer P, Gilles M, Langendorff V (2017) Rheological behavior of starch/carrageenan/milk proteins mixed systems: role of each biopolymer type and chemical characteristics. Food Hydrocoll 73:300–312. https://doi.org/10.1016/j.foodhyd.2017.07.012

    Article  CAS  Google Scholar 

  73. Contador L, Díaz M, Hernández E, Shinya P, Infante R (2017) The relationship between instrumental tests and sensory determinations of peach and nectarine texture. Eur J Hoticultural Sci 81:189 196.https://doi.org/10.17660/eJHS.2016/81.4.1

  74. Nnyigide OS, Oh Y, Song HY, Park E, Choi SH, Hyun K (2017) Effect of urea on heat-induced gelation of bovine serum albumin (BSA) studied by rheology and small angle neutron scattering (SANS). Korea Aust Rheol J 29:101–113. https://doi.org/10.1007/s13367-017-0012-4

    Article  Google Scholar 

  75. Kohyama K, Ishihara S, Nakauma M, Funami T (2019) Compression test of soft food gels using a soft machine with an artificial tongue. Foods 8(6):182. https://doi.org/10.3390/foods8060182

    Article  CAS  Google Scholar 

  76. Ishihara S, Nakao S, Nakauma M, Funami T, Hori K, Ono T, Kohyama K, Nishinari K (2013) Compression test of food gels on artificial tongue and its comparison with human test. J Texture Stud 44(2):104–114. https://doi.org/10.1111/jtxs.12002

    Article  Google Scholar 

  77. Melito HS, Daubert CR, Foegeding EA (2013) Relating Large Amplitude Oscillatory Shear and Food Behavior: Correlation of Nonlinear Viscoelastic, Rheological, Sensory and Oral Processing Behavior of Whey Protein Isolate/κ-Carrageenan Gels. J Food Process Eng 36: 521–534.https://doi.org/10.1111/jfpe.12015

  78. Morell P, Hernando I, Llorca E, Fiszman S (2015) Yogurts with an increased protein content and physically modified starch: rheological, structural, oral digestion and sensory properties related to enhanced satiating capacity. Food Res Int 70:64–73. https://doi.org/10.1016/j.foodres.2015.01.024

    Article  CAS  Google Scholar 

  79. Varela P, Pintor A, Fiszman S (2014) How hydrocolloids affect the temporal oral perception of ice cream. Food Hydrocoll 36:220–228. https://doi.org/10.1016/j.foodhyd.2013.10.005

    Article  CAS  Google Scholar 

  80. Pereira EPR, Cavalcanti RN, Esmerino EA, Silva R, Guerreiro LRM, Cunha RL, Bolini HMA, Meireles MA, Faria JAF, Cruz AG (2016) Effect of incorporation of antioxidants on the chemical, rheological, and sensory properties of probiotic petit suisse cheese. J Dairy Sci 99:1762–1772. https://doi.org/10.3168/jds.2015-9701

    Article  CAS  Google Scholar 

  81. Basu S, Shivhare US (2013) Rheological, textural, microstructural, and sensory properties of sorbitol-substituted mango jam. Food Bioprocess Technol 6:1401–1413. https://doi.org/10.1007/s11947-012-0795-8

    Article  CAS  Google Scholar 

  82. Li Z, Yang Z, Zhang Y, Lu T, Zhang X, Qi Y, Wang P, Xu X (2021) Innovative characterization based on stress relaxation and creep to reveal the tenderizing effect of ultrasound on wooden breast. Foods 10(1):195. https://doi.org/10.3390/foods10010195

    Article  CAS  Google Scholar 

  83. Jakubczyk E, Kamińska-Dwórznicka A, Kot A (2022) The rheological properties and texture of agar gels with canola oil—effect of mixing rate and addition of lecithin. Gels 8(11):738. https://doi.org/10.3390/gels8110738

    Article  Google Scholar 

  84. Okonkwo VC, Kwofie EM, Mba OI, Ngadi MO (2021) Impact of thermos sonication on quality indices of starch-based sauces. Ultrason Sonochem 73:105473. https://doi.org/10.1016/j.ultsonch.2021.105473

    Article  CAS  Google Scholar 

  85. Nnyigide OS, Hyun K (2020) The protection of bovine serum albumin against thermal denaturation and gelation by sodium dodecyl sulfate studied by rheology and molecular dynamics simulation. Food Hydrocoll 103:105656. https://doi.org/10.1016/j.foodhyd.2020.105656

    Article  CAS  Google Scholar 

  86. Stading M (2021) Bolus rheology of texture-modified food: effect of degree of modification. J Texture Stud 52:540–551. https://doi.org/10.1111/jtxs.12598

    Article  Google Scholar 

  87. Melito HS, Foegeding DCREA (2013) Relating LAOS and food behavior. J Food Process Eng 36:521–534. https://doi.org/10.1111/jfpe.12015

    Article  CAS  Google Scholar 

  88. Jung H, Oyinloye TM, Yoon WB (2022) Evaluating the mechanical response of agarose-xanthan mixture gels using tensile testing, numerical simulation, and a large amplitude oscillatory shear (LAOS) approach. Foods 11(24):4042. https://doi.org/10.3390/foods11244042

    Article  CAS  Google Scholar 

  89. Nnyigide OS, Nnyigide TO, Hyun K (2021) The degradation of xanthan gum in ionic and non-ionic denaturants studied by rheology and molecular dynamics simulation. Carbohydr Polym 251:117061. https://doi.org/10.1016/j.carbpol.2020.117061

    Article  CAS  Google Scholar 

  90. Schreuders FKG, Schlangen M, Bodnár I, Erni P, Boom RM, Jan van der Goot A (2022) Structure formation and non-linear rheology of blends of plant proteins with pectin and cellulose. Food Hydrocoll 124:107327. https://doi.org/10.1016/j.foodhyd.2021.107327

    Article  CAS  Google Scholar 

  91. Hyun K, Wilhelm M (2009) Establishing a new mechanical nonlinear coefficient Q from FT-rheology: first investigation of entangled linear and comb polymer model systems. Macromolecules 42(1):411–422. https://doi.org/10.1021/ma8017266

    Article  CAS  Google Scholar 

  92. Pearson DS, Rochefort WE (1982) Behavior of concentrated polystyrene solutions in large-amplitude oscillating shear fields. J polym sci 20(1):83–98. https://doi.org/10.1002/pol.1982.180200107

    Article  CAS  Google Scholar 

  93. Cho KS, Hyun K, Ahn KH, Lee SJ (2005) A geometrical interpretation of large amplitude oscillatory shear response. J Rheol 49(3):747–758. https://doi.org/10.1122/1.1895801

    Article  CAS  Google Scholar 

  94. Ewoldt RH, Hosoi AE, McKinley GH (2008) New measures for characterizing nonlinear viscoelasticity in large amplitude oscillatory shear. J Rheol 52(6):1427–1458. https://doi.org/10.1122/1.2970095

    Article  CAS  Google Scholar 

  95. Erturk MY, Rogers SA, Kokini J (2022) Comparison of sequence of physical processes (SPP) and fourier transform coupled with chebyshev polynomials (FTC) methods to interpret large amplitude oscillatory shear (LAOS) response of viscoelastic doughs and viscous pectin solution. Food Hydrocoll 128:107558. https://doi.org/10.1016/j.foodhyd.2022.107558

    Article  CAS  Google Scholar 

  96. Jimenez LN, Martínez Narváez CDV, Sharma V (2020) Capillary breakup and extensional rheology response of food thickener cellulose gum (NaCMC) in salt-free and excess salt solutions. Phys Fluids 32(1):012113. https://doi.org/10.1063/1.5128254

    Article  CAS  Google Scholar 

  97. Hadde EK, Cichero JAY, Zhao S, Chen W, Chen J (2019) The importance of extensional rheology in bolus control during swallowing. Sci Rep 9(1):16106. https://doi.org/10.1038/s41598-019-52269-4

    Article  CAS  Google Scholar 

  98. Dinic J, Jimenez LN, Sharma V (2017) Pinch-off dynamics and dripping-onto-substrate (DoS) rheometry of complex fluids. Lab Chip 17(3):460–473. https://doi.org/10.1039/C6LC01155A

    Article  CAS  Google Scholar 

  99. Renardy M (1995) A numerical study of the asymptotic evolution and breakup of Newtonian and viscoelastic jets. J Non-Newton Fluid Mech 59:267–282. https://doi.org/10.1016/0377-0257(95)01375-6

    Article  CAS  Google Scholar 

  100. Marconati M, Ramaioli M (2020) The role of extensional rheology in the oral phase of swallowing: an in vitro study. Food Funct 11(5):4363–4375. https://doi.org/10.1039/C9FO02327E

    Article  CAS  Google Scholar 

  101. Hadde EK, Nicholson TM, Cichero JAY (2020) Evaluation of thickened fluids used in dysphagia management using extensional rheology. Dysphagia 35:242–252. https://doi.org/10.1007/s00455-019-10012-1

    Article  CAS  Google Scholar 

  102. Yue Q, Li M, Liu C, Li L, Zheng X, Bian K (2020) Extensional rheological properties in mixed and fermented/rested dough and relationships with steamed bread quality. J Cereal Sci 93:102968. https://doi.org/10.1016/j.jcs.2020.102968

    Article  CAS  Google Scholar 

  103. Derkanosova NM, Stakhurlova AA, Vukic M, Vujadinovic D (2022) Rheological properties of composite mixtures from wheat and amaranth flour. IOP Conf Ser Earth Environ Sci 954(1):012079. https://doi.org/10.1088/1755-1315/954/1/012079

    Article  Google Scholar 

  104. Ravera F, Loglio G, Kovalchuk VI (2010) Interfacial dilational rheology by oscillating bubble/drop methods. Curr Opin Colloid Interface 15(4):217–228. https://doi.org/10.1016/j.cocis.2010.04.001

    Article  CAS  Google Scholar 

  105. Wijmans CM, Dickinson E (1998) Simulation of interfacial shear and dilatational rheology of an adsorbed protein monolayer modeled as a network of spherical particles. Langmuir 14(25):7278–7286. https://doi.org/10.1021/la980687p

    Article  CAS  Google Scholar 

  106. Meleties M, Martineau RL, Gupta MK, Montclare JK (2022) Particle-based microrheology as a tool for characterizing protein-based materials. ACS Biomater Sci Eng 8(7):2747–2763. https://doi.org/10.1021/acsbiomaterials.2c00035

    Article  CAS  Google Scholar 

  107. Nnyigide TO, Nnyigide OS, Hyun K (2023) Rheological and molecular dynamics simulation studies of the gelation of human serum albumin in anionic and cationic surfactants. Korean J Chem Eng. https://doi.org/10.1007/s11814-023-1513-0

    Article  Google Scholar 

  108. Lee YJ, Jin H, Kim S, Myung JS, Ahn KH (2021) Brownian dynamics simulation on orthogonal superposition rheology: time-shear rate superposition of colloidal gel. J Rheol 65(3):337–354. https://doi.org/10.1122/8.0000161

    Article  CAS  Google Scholar 

  109. Nnyigide OS, Hyun K (2016) Effect of urea and temperature on the molecular dynamics of bovine serum albumin in heavy and light water. J Chem Technol Metall 51:147–149

    CAS  Google Scholar 

  110. Nnyigide OS, Hyun K (2023) Charge-induced low-temperature gelation of mixed proteins and the effect of pH on the gelation: a spectroscopic, rheological and coarse-grained molecular dynamics study. Colloids Surf B 230:113527. https://doi.org/10.1016/j.colsurfb.2023.113527

    Article  CAS  Google Scholar 

  111. Saeidirad MH, Rohani A, Zarifneshat S (2013) Predictions of viscoelastic behavior of pomegranate using artificial neural network and maxwell model. Comput Electron Agric 98:1–7. https://doi.org/10.1016/j.compag.2013.07.009

    Article  Google Scholar 

  112. Torkashvand AM, Ahmadi A, Nikravesh NL (2017) Prediction of kiwifruit firmness using fruit mineral nutrient concentration by artificial neural network (ANN) and multiple linear regressions (MLR). J Integr Agric 16(7):1634–1644. https://doi.org/10.1016/S2095-3119(16)61546-0

    Article  CAS  Google Scholar 

  113. Toker OS, Dogan M (2013) Effect of temperature and starch concentration on the creep/recovery behaviour of the grape molasses: modelling with ANN, ANFIS and response surface methodology. Eur Food Res Technol 236(6):1049–1061. https://doi.org/10.1007/s00217-013-1959-0

    Article  CAS  Google Scholar 

  114. Al-Mahasneh M, Aljarrah M, Rababah T, Alu’datt M, (2016) Application of hybrid neural fuzzy system (ANFIS) in food processing and technology. Food Eng Rev 8(3):351–366. https://doi.org/10.1007/s12393-016-9141-7

    Article  CAS  Google Scholar 

  115. Al-Mahasneh MA, Rababah TM, Ma’Abreh AS (2013) Evaluating the Combined Effect of Temperature, Shear Rate and Water Content on Wild-Flower Honey Viscosity Using Adaptive Neural Fuzzy Inference System and Artificial Neural Networks. J Food Process Eng 36(4): 510–520. https://doi.org/10.1111/jfpe.12014

  116. Jeong S, Kim H, Lee S (2021) Rheology-based classification of foods for the elderly by machine learning analysis. Appl Sci 11(5):2262. https://doi.org/10.3390/app11052262

    Article  CAS  Google Scholar 

Download references

Funding

Funded by Pusan National University (2-Year Research Grant)

Author information

Authors and Affiliations

  1. School of Chemical Engineering, Pusan National University, Busan, 46241, Korea

    Osita Sunday Nnyigide & Kyu Hyun

Authors
  1. Osita Sunday Nnyigide
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kyu Hyun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kyu Hyun.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nnyigide, O.S., Hyun, K. A comprehensive review of food rheology: analysis of experimental, computational, and machine learning techniques. Korea-Aust. Rheol. J. 35, 279–306 (2023). https://doi.org/10.1007/s13367-023-00075-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13367-023-00075-w

Keywords

Search

Navigation