Literature DB >> 26396305

Retort process modelling for Indian traditional foods.

S V Gokhale1, S S Lele1.   

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.

Entities:  

Keywords:  Indian traditional foods; Modelling; Retort processing; Sterilization

Year:  2012        PMID: 26396305      PMCID: PMC4571269          DOI: 10.1007/s13197-012-0844-3

Source DB:  PubMed          Journal:  J Food Sci Technol        ISSN: 0022-1155            Impact factor:   2.701


  2 in total

1.  Impact of retort process on characteristics and bioactivities of herbal soup based on hydrolyzed collagen from seabass skin.

Authors:  Soottawat Benjakul; Kasidate Chantakun; Supatra Karnjanapratum
Journal:  J Food Sci Technol       Date:  2018-07-18       Impact factor: 2.701

2.  Effect of high-amylose corn starch addition on canning of yellow alkaline noodle composed of wheat flour and microbial transglutaminase: Optimization by RSM.

Authors:  Lida Shahsavani Mojarrad; Ali Rafe
Journal:  Food Sci Nutr       Date:  2018-05-03       Impact factor: 2.863

  2 in total

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