Skip to main content
SpringerLink
Log in

Hybrid method for predicting protein denaturation and docosahexaenoic acid decomposition in Atlantic salmon (Salmo salar L.) using computational fluid dynamics and response surface methodology

  • Original Paper
  • Published:
European Food Research and Technology Aims and scope Submit manuscript

Abstract

Atlantic salmon is characterized by highly acceptable sensory qualities and a nutritional composition rich in fatty acids. However, food processing procedures, including improper heat treatment, can lead to unfavorable changes in quality and nutritional value. In this study, a computational fluid dynamics computer simulation was used to model the quality attributes of the roasted salmon product by controlling input parameters such as temperature, humidity, and air movement speed. Including the degree of denaturation of myosin, collagen, sarcoplasmic proteins, and actin, as well as docosahexaenoic acid decomposition and weight loss. Based on the conducted simulations, a prediction model was developed using the response surface methodology. According to the optimized model, salmon samples should undergo processing at a temperature of 151.38 ℃, with 20% humidity, and the fan speed set to 452.78 RPM. After the optimized heat treatment process, the degree of denaturation of salmon proteins was as follows: myosin denaturation at 95.12 ± 1.35%, collagen denaturation at 84.97 ± 1.72%, sarcoplasmic proteins denaturation at 37.71 ± 1.52%, and actin denaturation at 16.43 ± 0.71%. Furthermore, the weight loss was measured at 17.88 ± 0.55%, and docosahexaenoic acid decomposition at 0.56 ± 0.07%. This innovative hybrid method, using computational fluid dynamics and response surface methodology, for forecasting and optimization, can be applied to model thermal processes in the food industry.

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

Similar content being viewed by others

Data availability

The authors declare availability of data and material.

Code availability

Not Applicable.

Abbreviations

CTP:

Connective tissue protein

DHA:

Docosahexaenoic fatty acid C22:6 n-3

EPA:

Eicosapentaenoic fatty acid C20:5 n-3

FA:

Fatty acids

FAME:

Fatty acid methyl ester composition

HAAs:

Heterocyclic aromatic amines

PAHs:

Polycyclic aromatic hydrocarbons

PUFA:

Polyunsaturated fatty acids

CFD:

Computational fluid dynamics

DSC:

Differential scanning calorimetry

FID:

Flame ionization detector

GC:

Gas chromatography

RSM:

Response surface methodology

λ:

Conduction coefficient (W/(m⋅K))

C p :

Specific heat (J/kgK)

β:

Heating rate (°C/min)

Ton :

Onset temperature (°C)

Tmax :

Maximum peak temperature (°C)

Tend :

End set temperature (°C)

ΔH:

Areas under the peaks (J/g)

X̄:

Mean

SE:

Standard error

GLM:

General linear model

RPM:

Revolutions per minute

References

  1. Fomena Temgoua NS, Sun Z, Okoye CO, Pan H (2022) Fatty Acid Profile, Physicochemical Composition, and Sensory Properties of Atlantic Salmon Fish (Salmo salar) during Different Culinary Treatments. J Food Qual. https://doi.org/10.1155/2022/7425142

  2. Djuricic I, Calder PC (2021) Beneficial outcomes of omega-6 and omega-3 polyunsaturated fatty acids on human health: an update for 2021. Nutrients. https://doi.org/10.3390/nu13072421

    Article  PubMed  PubMed Central  Google Scholar 

  3. Katan T, Caballero-Solares A, Taylor RG et al (2019) Effect of plant-based diets with varying ratios of ω6 to ω3 fatty acids on growth performance, tissue composition, fatty acid biosynthesis and lipid-related gene expression in Atlantic salmon (Salmo salar). Comp Biochem Physiol—Part D Genomics Proteomics 30:290–304. https://doi.org/10.1016/j.cbd.2019.03.004

    Article  CAS  PubMed  Google Scholar 

  4. Kovaleva OA, Zdrabova EM, Kireeva OS (2021) Influence of heat-induced changes in meat proteins on the quality characteristics of the finished product. In: IOP Conference Series: Earth and Environmental Science. pp 1–8

  5. Liu Y, Wang Z, Zhang D et al (2022) Effect of protein thermal denaturation on the texture profile evolution of beijing roast duck. Foods 11:1–14. https://doi.org/10.3390/foods11050664

    Article  CAS  Google Scholar 

  6. Ishiwatari N, Fukuoka M, Sakai N (2013) Effect of protein denaturation degree on texture and water state of cooked meat. J Food Eng 117:361–369. https://doi.org/10.1016/j.jfoodeng.2013.03.013

    Article  CAS  Google Scholar 

  7. Khalid W, Maggiolino A, Kour J et al (2023) Dynamic alterations in protein, sensory, chemical, and oxidative properties occurring in meat during thermal and non-thermal processing techniques: a comprehensive review. Front Nutr 9:1–19. https://doi.org/10.3389/fnut.2022.1057457

    Article  CAS  Google Scholar 

  8. Bhat ZF, Morton JD, Bekhit AEDA et al (2021) Thermal processing implications on the digestibility of meat, fish and seafood proteins. Compr Rev Food Sci Food Saf 20:4511–4548. https://doi.org/10.1111/1541-4337.12802

    Article  CAS  PubMed  Google Scholar 

  9. Gysel N, Dixit P, Schmitz DA et al (2018) Chemical speciation, including polycyclic aromatic hydrocarbons (PAHs), and toxicity of particles emitted from meat cooking operations. Sci Total Environ 633:1429–1436. https://doi.org/10.1016/j.scitotenv.2018.03.318

    Article  CAS  PubMed  ADS  Google Scholar 

  10. Bellamri M, Walmsley SJ, Turesky RJ (2021) Metabolism and biomarkers of heterocyclic aromatic amines in humans. Genes Environ 43:1–32. https://doi.org/10.1186/s41021-021-00200-7

    Article  CAS  Google Scholar 

  11. Chan DS (2020) Computer simulation with a temperature-step frying approach to mitigate acrylamide formation in French fries. Foods. https://doi.org/10.3390/foods9020200

  12. Abraha B, Admassu H, Mahmud A, et al (2018) Effect of processing methods on nutritional and physico-chemical composition of fish: a review. MOJ Food Process Technol 6:376–382. https://doi.org/10.15406/mojfpt.2018.06.00191

  13. Piepiórka-Stepuk J, Jakubowski M (2013) Numerical studies of fluid flow in flat, narrow-gap channels simulating plate heat exchanger. Chem Process Eng—Inz Chem i Proces 34:507–514. https://doi.org/10.2478/cpe-2013-0041

    Article  CAS  Google Scholar 

  14. Szpicer A, Bińkowska W, Wojtasik-Kalinowska I, et al (2023) Application of computational fluid dynamics simulations in food industry. Eur Food Res Technol 1–20. https://doi.org/10.1007/s00217-023-04231-y

  15. Yesiltas B, García-Moreno PJ, Sørensen ADM et al (2019) Physical and oxidative stability of high fat fish oil-in-water emulsions stabilized with sodium caseinate and phosphatidylcholine as emulsifiers. Food Chem 276:110–118. https://doi.org/10.1016/j.foodchem.2018.09.172

    Article  CAS  PubMed  Google Scholar 

  16. Piepiórka-Stepuk J, Diakun J, Sterczyńska M et al (2021) Mathematical modeling and analysis of the interaction of parameters in the clean-in-place procedure during the pre-rinsing stage. J Clean Prod 297:1–12. https://doi.org/10.1016/j.jclepro.2021.126484

    Article  Google Scholar 

  17. Szpicer A, Wierzbicka A, Półtorak A (2022) Optimization of beef heat treatment using CFD simulation: Modeling of protein denaturation degree. J Food Process Eng 45:e14014. https://doi.org/10.1111/jfpe.14014

    Article  CAS  Google Scholar 

  18. Dadras S (2013) Composition and morphology of Atlantic salmon (Salmo salar L.) as affected by dietary oil. 75

  19. Murray J, Burt JR (2001) The Composition of Fish Torry Advisory Note No. 38, M inistry of Technology.Torry Research Station, U.K.,

  20. Aas TS, Åsgård T, Ytrestøyl T (2022) Chemical composition of whole body and fillet of slaughter sized Atlantic salmon (Salmo salar) and rainbow trout (Oncorhynchus mykiss) farmed in Norway in 2020. Aquac Reports 25:0–5. https://doi.org/10.1016/j.aqrep.2022.101252

  21. Agafonkina I V., Korolev IA, Sarantsev TA (2019) The study of thermal Denaturation of beef, pork, chicken and turkey muscle proteins using differential scanning calorimetry. Theory Pract meat Process 4:19–23. https://doi.org/10.21323/2414-438x-2019-4-3-19-23

  22. Sathivel S, Prinyawiwatkul W, Negulescu II, King JM (2008) Determination of melting points, specific heat capacity and enthalpy of catfish visceral oil during the purification process. JAOCS, J Am Oil Chem Soc 85:291–296. https://doi.org/10.1007/s11746-007-1191-9

    Article  CAS  Google Scholar 

  23. Bircan C, Barringer SA (2002) Determination of protein denaturation of muscle foods using the dielectric properties J. Food Sci 67:202–205. https://doi.org/10.1111/j.1365-2621.2002.tb11384.x

    Article  CAS  Google Scholar 

  24. Szpicer A, Binkowska W, Wojtasik-Kalinowska I, Poltorak A (2023) Prediction of protein denaturation and weight loss in pork loin (muscle Longissimus dorsi) using computational fluid dynamics. Eur Food Res Technol 249:3055–3068. https://doi.org/10.1007/s00217-023-04348-0

    Article  CAS  Google Scholar 

  25. Choi Y, Okos MR (1986) Thermal Properties of Liquid Foods - Review. ASAE Publ 35–77

  26. Singh RP, Heldman DR (2014) Introduction to food engineering: Fifth edition. Academic Press

  27. Markočič E, Knez Ž (2016) Redlich-Kwong equation of state for modelling the solubility of methane in water over a wide range of pressures and temperatures. Fluid Phase Equilib 408:108–114. https://doi.org/10.1016/j.fluid.2015.08.021

    Article  CAS  Google Scholar 

  28. Goff JA, Gratch S (1946) Low-pressure properties of water from −160 to 212 F. In: Transactions of the American Society of Heating and Ventilating Engineers presented at the 52nd annual meeting of the American Society of Heating and Ventilating Engineers. New York, pp 95–122

  29. Szpicer A, Onopiuk A, Półtorak A, Wierzbicka A (2018) Influence of oat β-glucan and canola oil addition on the physico-chemical properties of low-fat beef burgers. J Food Process Preserv 42:1–12. https://doi.org/10.1111/jfpp.13785

    Article  CAS  Google Scholar 

  30. Hǎdǎruga DI, Ünlüsayin M, Gruia AT et al (2016) Thermal and oxidative stability of Atlantic salmon oil (Salmo salar L.) and complexation with β-cyclodextrin. Beilstein J Org Chem 12:179–191. https://doi.org/10.3762/bjoc.12.20

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. van der Sman RGM (2007) Moisture transport during cooking of meat: an analysis based on Flory-Rehner theory. Meat Sci 76:730–738. https://doi.org/10.1016/j.meatsci.2007.02.014

    Article  CAS  PubMed  Google Scholar 

  32. Wählby U, Skjöldebrand C (2001) NIR-measurements of moisture changes in foods. J Food Eng 47:303–312. https://doi.org/10.1016/S0260-8774(00)00134-5

    Article  Google Scholar 

  33. Khare AK, Biswas AK, Balasubramanium S et al (2015) Optimization of meat level and processing conditions for development of chicken meat noodles using response surface methodology. J Food Sci Technol 52:3719–3729. https://doi.org/10.1007/s13197-014-1431-6

    Article  CAS  PubMed  Google Scholar 

  34. Cepeda JF, Weller CL, Negahban M et al (2013) Heat and mass transfer modeling for microbial food safety applications in the meat industry: a review. Food Eng Rev 5:57–76. https://doi.org/10.1007/s12393-013-9063-6

    Article  Google Scholar 

  35. Bouvier L, Moreau A, Ronse G et al (2014) A CFD model as a tool to simulate β-lactoglobulin heat-induced denaturation and aggregation in a plate heat exchanger. J Food Eng 136:56–63. https://doi.org/10.1016/j.jfoodeng.2014.03.025

    Article  CAS  Google Scholar 

  36. Plana-Fattori A, Doursat C, Coutouly A, et al (2013) Developing a CFD model for studying whey protein denaturation-aggregation. In: 2012 Annual Meeting of the European Federation of Food Science and Technology. (2012 EFFoST), Le Corum, Montpellier, France, p 91300

  37. Liu W, Feng Y, Pan F et al (2022) Effect of calcium on the thermal denaturation of whey proteins and subsequent fouling in a benchtop fouling device: an experimental and numerical approach. Food Bioprod Process 136:1–13. https://doi.org/10.1016/j.fbp.2022.09.002

    Article  CAS  Google Scholar 

  38. Jaskulski M, Atuonwu JC, Tran TTH et al (2017) Predictive CFD modeling of whey protein denaturation in skim milk spray drying powder production. Adv Powder Technol 28:3140–3147. https://doi.org/10.1016/j.apt.2017.09.026

    Article  CAS  Google Scholar 

  39. Plana-Fattori A, Doursat C, Coutouly A, et al (2020) A numerical model for studying the thermal denaturation-aggregation of whey proteins under continuous thermal processing. Int J Food Stud 9:SI17–SI37. https://doi.org/10.7455/ijfs/9.SI.2020.a2

  40. Gu Y, Bouvier L, Tonda A, Delaplace G (2019) A mathematical model for the prediction of the whey protein fouling mass in a pilot scale plate heat exchanger. Food Control 106:1–10. https://doi.org/10.1016/j.foodcont.2019.106729

    Article  CAS  Google Scholar 

  41. Grijspeerdt K, Hazarika B, Vucinic D (2003) Application of computational fluid dynamics to model the hydrodynamics of plate heat exchangers for milk processing. J Food Eng 57:237–242. https://doi.org/10.1016/S0260-8774(02)00303-5

    Article  Google Scholar 

  42. Habtegebriel H, Edward D, Motsamai O et al (2021) The potential of computational fluid dynamics simulation to investigate the relation between quality parameters and outlet temperature during spray drying of camel milk. Dry Technol 39:2010–2024. https://doi.org/10.1080/07373937.2019.1684317

    Article  CAS  Google Scholar 

  43. Alvarenga N, Martins J, Caeiro J et al (2021) Applying computational fluid dynamics in the development of smart ripening rooms for traditional cheeses. Foods 10:1–14. https://doi.org/10.3390/foods10081716

    Article  Google Scholar 

  44. Khatir Z, Paton J, Thompson H et al (2012) Computational fluid dynamics (CFD) investigation of air flow and temperature distribution in a small scale bread-baking oven. Appl Energy 89:89–96. https://doi.org/10.1016/j.apenergy.2011.02.002

    Article  ADS  Google Scholar 

  45. Adamic RM (2012) CFD and heat transfer model of baking bread in a tunnel oven. ETD Arch 3:1–210

    Google Scholar 

  46. Halder A, Dhall A, Datta AKK (2007) An improved, easily implementable, porous media based model for deep-fat frying. Food Bioprod Process 85:220–230. https://doi.org/10.1205/fbp07034

    Article  CAS  Google Scholar 

  47. Emin MA, Schuchmann HP (2013) Analysis of the dispersive mixing efficiency in a twin-screw extrusion processing of starch based matrix. J Food Eng 115:132–143. https://doi.org/10.1016/j.jfoodeng.2012.10.008

    Article  Google Scholar 

  48. Piepiórka-Stepuk J, Diakun J (2014) Numerical analysis of fluid flow velocity between plates channel of heat exchanger by different surface configuration in reference to the effects of cleaning. Ital J Food Sci 26:210–220

    Google Scholar 

  49. Malekjani N, Jafari SM (2018) Simulation of food drying processes by Computational Fluid Dynamics (CFD); recent advances and approaches. Trends Food Sci Technol 78:206–223. https://doi.org/10.1016/j.tifs.2018.06.006

    Article  CAS  Google Scholar 

  50. Mu A, Abdul I, Tantiyani N, Ali B (2021) Computational fluid dynamics simulation of fluidized bed dryer for sago pith waste drying process computational fluid dynamics simulation of fluidized bed dryer for sago pith waste drying process. J Kejuruter 33:239–248. https://doi.org/10.17576/jkukm-2021-33(2)-09

  51. Anandharamakrishnan C (2007) Computational fluid dynamics in food processing, 1st edn. Springer, London

    Google Scholar 

  52. Xia B, Sun DW (2002) Applications of computational fluid dynamics (CFD) in the food industry: a review. Comput Electron Agric 34:5–24. https://doi.org/10.1016/S0168-1699(01)00177-6

    Article  Google Scholar 

  53. Aslam Bhutta MM, Hayat N, Bashir MH et al (2012) CFD applications in various heat exchangers design: a review. Appl Therm Eng 32:1–12. https://doi.org/10.1016/j.applthermaleng.2011.09.001

    Article  Google Scholar 

  54. Jamaleddine TJ, Ray MB (2010) Application of computational fluid dynamics for simulation of drying processes: A review. Dry Technol 28:120–154. https://doi.org/10.1080/07373930903517458

    Article  CAS  Google Scholar 

Download references

Funding

Research financed by the Polish Ministry of Science and Higher Education within funds of Faculty of Human Nutrition and Consumer Sciences, Warsaw University of Life Sciences (WULS), for scientific research.

Author information

Authors and Affiliations

  1. Department of Technique and Food Development, Warsaw University of Life Sciences-SGGW, Nowoursynowska Str. 159 C Building 32, Office 0120, 02-776, Warsaw, Poland

    Arkadiusz Szpicer, Weronika Binkowska, Iwona Wojtasik-Kalinowska, Adrian Stelmasiak & Andrzej Poltorak

Authors
  1. Arkadiusz Szpicer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weronika Binkowska
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Iwona Wojtasik-Kalinowska
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Adrian Stelmasiak
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Andrzej Poltorak
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

AS: conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing—original draft, writing—review & editing, visualization, supervision project administration. WB: writing—original draft, writing—review & editing. IW-K: writing—original draft, writing—review & editing. AS: writing—original draft, writing—review & editing. AP: software, writing—review & editing, funding acquisition.

Corresponding author

Correspondence to Arkadiusz Szpicer.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Ethics approval

Not Applicable.

Consent to participate

Not Applicable.

Consent for publication

Not Applicable.

Additional information

Publisher's Note

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

Practical Application: The CFD modeling and RSM optimization of thermal treatment allow for the selection of appropriate process parameters. This innovative solution will provide high nutritional value, economic profitability (weight loss) and denaturation of selected proteins of salmon. This hybrid method of simulation and optimization will solve the problem of the decrease in the nutritional value of salmon during roasting, which will increase consumer interest. This innovative hybrid method on an industrial scale will allow us to maintain the highest, reproducible quality and increase sales of roasted fish products. In addition, this solution will contribute to the reduction of food waste, which, as a result of improper processing, is thrown away by consumers or disposed of by the production plant.

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

Szpicer, A., Binkowska, W., Wojtasik-Kalinowska, I. et al. Hybrid method for predicting protein denaturation and docosahexaenoic acid decomposition in Atlantic salmon (Salmo salar L.) using computational fluid dynamics and response surface methodology. Eur Food Res Technol 250, 1163–1176 (2024). https://doi.org/10.1007/s00217-023-04453-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00217-023-04453-0

Keywords

Search

Navigation