Premium
Pulse vacuum pretreatment technology and neural network optimization in drying of tilapia fillets with heat pump
Author(s) -
Li Min,
Wu Yangyang,
Ge Yunting,
Ling Changming
Publication year - 2019
Publication title -
journal of food processing and preservation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.511
H-Index - 48
eISSN - 1745-4549
pISSN - 0145-8892
DOI - 10.1111/jfpp.14258
Subject(s) - chewiness , tilapia , heat pump , process engineering , dehydration , chemistry , pulp and paper industry , materials science , food science , mechanical engineering , fish <actinopterygii> , biochemistry , heat exchanger , engineering , biology , fishery
Abstract Low‐temperature (20 ~ 40°C) heat pump drying technology for food products has advantages over conventional high temperature (45 ~ 90°C) hot air drying but requires proper pretreatment to ensure high‐quality drying products. Subsequently, experimental investigations were carried out for drying of tilapia fillets with heat pump and at different pulse vacuum pretreatment parameters including vacuum cycle rate, cycle number, vacuum pressure, and concentration of trehalose impregnation liquid. The quality of the completed drying product was then evaluated and compared by eight quality indexes containing whiteness, ratio of water loss to solid content increase rate during osmosis dehydration (dehydration efficiency index, DEI), Ca 2+ ‐ATPase (adenosine triphosphatase) activity, rehydration rate, hardness, elasticity, glueyness, and chewiness. These indexes are thus generalized into one comprehensive score based on the effective weight of each index on the product quality and analysis of the fuzzy hierarchy process. The experimental measurements were applied to examine the effect of the pretreatment parameters on the product quality indexes and comprehensive score. Accordingly, an artificial neural network model has been developed to train and achieve the optimal pretreatment parameters based on the maximum product quality comprehensive score. The obtained optimal pretreatment parameters have been validated with the measurements. Ultimately, the optimal heat pump drying process is significant and can be applied to an actual application for different food product dryings. Practical applications Heat pump technology is one of the prospective processing methods for the drying of tilapia fillets. However, the relative low‐temperature heat pump drying process would increase the drying time and thus deteriorates the risk of food product. To cope with this, a high efficient pretreatment method needs to be designed and applied. Correspondingly, in the project described by this paper, extensive measurements were designed and carried out on the product drying with a heat pump. Consequently, a fuzzy hierarchy process was applied to process the relevant experimental data and generate a comprehensive score for each test. Ultimately, an artificial neural network model has been developed to train and achieve the optimal pretreatment parameters. The optimal heat pump drying process is significant and can be applied to an actual application for different food product dryings.