Skip to main content
Log in

Retort process modelling for Indian traditional foods

  • Original Article
  • Published:
Journal of Food Science and Technology Aims and scope Submit manuscript

Abstract

Indian traditional staple and snack food is typically a heterogeneous recipe that incorporates varieties of vegetables, lentils and other ingredients. Modelling the retorting process of multilayer pouch packed Indian food was achieved using lumped-parameter approach. A unified model is proposed to estimate cold point temperature. Initial process conditions, retort temperature and % solid content were the significantly affecting independent variables. A model was developed using combination of vegetable solids and water, which was then validated using four traditional Indian vegetarian products: Pulav (steamed rice with vegetables), Sambar (south Indian style curry containing mixed vegetables and lentils), Gajar Halawa (carrot based sweet product) and Upama (wheat based snack product). The predicted and experimental values of temperature profile matched with ±10 % error which is a good match considering the food was a multi component system. Thus the model will be useful as a tool to reduce number of trials required to optimize retorting of various Indian traditional vegetarian foods.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Abbreviations

F:

Accumulated lethality (min)

fh :

Heating rate constant

jc :

Lag constant

kc :

Empirical model constant for cooling

kh :

Empirical model constant for heating

L:

Lethality

t:

Time (min)

R2 :

Correlation regression coefficient

RMSE:

Root mean square error

SS:

Sum of squares of errors

Ta :

Ambient temperature (°C)

Tcw :

Cooling water temperature (°C)

TP :

Product temperature (°C) at any time t

TP0 :

Initial product temperature (°C)

TR :

Desired retort temperature (°C)

Tref :

Reference temperature (°C)

z:

Slope index of thermal death curve (°C)

χ 2 :

Chi square error

References

  • Abbatemarco C, Ramaswamy HS (1994) End-over-end thermal processing of canned vegetables: effect on texture and color. Food Res Int 27(4):327–334

    Article  Google Scholar 

  • Alonso AA, Banga JR, Perez-Martin R (1997) A complete dynamic model for the thermal processing of bioproducts in batch units and its application to controller design. Chem Eng Sci 52(8):1307–1322

    Article  CAS  Google Scholar 

  • Baucour P, Cronin K, Stynes M (2003) Process optimization strategies to diminish variability in the quality of discrete packaged foods during thermal processing. J Food Eng 60(2):147–155

    Article  Google Scholar 

  • Bindu J, Ravishankar CN, Srinivasa Gopal TK (2007) Shelf life evaluation of a ready-to-eat black clam (Villorita cyprinoides) product in indigenous retort pouches. J Food Eng 78(3):995–1000

    Article  CAS  Google Scholar 

  • Box GEP, Behnken DW (1960) Some new three level designs for the study of quantitative variables. Technometrics 2:455–475

    Article  Google Scholar 

  • Chandrasekar V, Srinivasa Gopal TK (2008) Heat penetration characteristics of mushroom curry packed in retort pouch. Int J Postharvest Technol Innov 1(3):312–319

    Article  Google Scholar 

  • Chandrasekar V, Gopal TKS, Rai RD (2004) Heat penetration characteristics and shelf-life studies of mushrooms in brine processed in retort pouches. Packag Technol Sci 17(4):213–217

    Article  Google Scholar 

  • Chen CR, Ramaswamy HS (2002a) Modeling and optimization of constant retort temperature (CRT) thermal processing using coupled neural networks and genetic algorithms. J Food Process Eng 25(5):351–379

    Article  Google Scholar 

  • Chen CR, Ramaswamy HS (2002b) Modeling and optimization of variable retort temperature (VRT) thermal processing using coupled neural networks and genetic algorithms. J Food Eng 53(3):209–220

    Article  Google Scholar 

  • Chen CR, Ramaswamy HS (2003) Analysis of critical control points in deviant thermal processes using artificial neural networks. J Food Eng 57(3):225–235

    Article  Google Scholar 

  • Chen CR, Ramaswamy HS (2007) Visual Basics computer simulation package for thermal process calculations. Chem Eng Process 46(7):603–613

    Article  CAS  Google Scholar 

  • Dileep AO, Sudhakara NS (2007) Retortable pouch packaging of deep-sea shrimp (Aristeus alcocki) in curry and quality evaluation during storage. J Food Sci Technol 44(1):90–93

    CAS  Google Scholar 

  • Geetha P, Jayaraj Rao K (2008) Technology of retort processed poppy seeds (Papaver somniferum) payasam. 2. Shelf-life studies. J Food Sci Technol 45(6):534–536

    CAS  Google Scholar 

  • Gonçalves EC, Minim LA, Coimbra JSR, Minim VPR (2005) Modeling sterilization process of canned foods using artificial neural networks. Chem Eng Process 44(12):1269–1276

    Article  Google Scholar 

  • Jayakumar V, Pandey MC, Jayathilakan K, Manral M (2007) Development and evaluation of thermally processed pearlspot (Etroplus suratensis) fish curry. J Food Sci Technol 44(4):350–352

    CAS  Google Scholar 

  • Mallick AK, Srinivasa Gopal TK, Ravishankar CN, Vijayan PK (2006) Canning of rohu (Labeo rohita) in North Indian style curry medium using polyester-coated tin free steel cans. Food Sci Technol Int 12(6):539–545

    Article  CAS  Google Scholar 

  • Miri T, Tsoukalas A, Bakalis S, Pistikopoulos EN, Rustem B, Fryer PJ (2008) Global optimization of process conditions in batch thermal sterilization of food. J Food Eng 87(4):485–494

    Article  Google Scholar 

  • Montgomery DC (1984) Design and analysis of experiments. Wiley, New York

    Google Scholar 

  • Paul Singh R, Heldman DR (2004) Introduction to food engineering. Academic, New Delhi

    Google Scholar 

  • Prakash M, Ravi R, Sathish HS, Shyamala JC, Shwetha MA, Rangarao GCP (2005) Sensory and instrumental texture measurement of thermally processed rice. J Sens Stud 20(5):410–420

    Article  Google Scholar 

  • Ravi Shankar CN, Srinivasa Gopal TK, Vijayan PK (2002) Studies on heat processing and storage of seer fish curry in retort pouches. Packag Technol Sci 15(1):3–7

    Article  Google Scholar 

  • Rodríguez JJ, Olivas GI, Sepúlveda DR, Warner H, Clark S, Barbosa-Cánovas GV (2003) Shelf-life study of retort pouch black bean and rice burrito combat rations packaged at selected residual gas levels. J Food Qual 26(5):409–424

    Article  Google Scholar 

  • Sablani SS, Shayya WH (2001) Computerization of Stumbo’s method of thermal process calculations using neural networks. J Food Eng 47(3):233–240

    Article  Google Scholar 

  • Simpson R, Almonacid S, Mitchell M (2004) Mathematical model development, experimental validation and process optimization: retortable pouches packed with seafood in cone frustum shape. J Food Eng 63(2):153–162

    Article  Google Scholar 

Download references

Acknowledgment

The authors would like to acknowledge the “University Grant Commission (UGC)”, Government of India for financial support.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to S. S. Lele.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gokhale, S.V., Lele, S.S. Retort process modelling for Indian traditional foods. J Food Sci Technol 51, 3134–3143 (2014). https://doi.org/10.1007/s13197-012-0844-3

Download citation

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13197-012-0844-3

Keywords

Navigation