Weight loss of frozen bread dough under isothermal and fluctuating temperature storage conditions

Evaporative weight loss from food leads to both loss of saleable weight and quality deterioration so it need to be minimized. The effect of isothermal and fluctuating conditions on frozen dough weight loss was measured and compared with kinetic, physical and artificial neural network (ANN) models. F...

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Main Authors: Yuthana Phimolsiripol, Ubonrat Siripatrawan, Donald J. Cleland
Format: Journal
Published: 2018
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http://cmuir.cmu.ac.th/jspui/handle/6653943832/49572
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Institution: Chiang Mai University
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spelling th-cmuir.6653943832-495722018-09-04T04:04:07Z Weight loss of frozen bread dough under isothermal and fluctuating temperature storage conditions Yuthana Phimolsiripol Ubonrat Siripatrawan Donald J. Cleland Agricultural and Biological Sciences Evaporative weight loss from food leads to both loss of saleable weight and quality deterioration so it need to be minimized. The effect of isothermal and fluctuating conditions on frozen dough weight loss was measured and compared with kinetic, physical and artificial neural network (ANN) models. Frozen dough samples were regularly weighed during storage for up to 112 days in loose-fitting plastic bags. The storage temperatures were in the range of -8 to -25 °C with fluctuations of ±0.1 °C (isothermal), ±1, ±3 or ±5 °C about the mean. For each combination of temperature and fluctuation amplitude, the rate of dough weight loss was constant. The rate of weight loss at constant temperature was nearly proportional to water vapour pressure consistent with standard theories for evaporative weight loss from packaged foods but was also accurately fitted by Arrhenius kinetics. Weight loss increased with amplitude of temperature fluctuations. The increase could not be fully explained by either the physic model based on water vapour pressure differences or the kinetic model alone. An ANN model with six neurons in the input layer, six neurons in hidden layers and one neuron in the output layer, achieved a good fit between experimental and predicted data for all trials. However, the ANN model may not be accurate for product, packaging and storage systems different to that studied. © 2011 Elsevier Ltd. All rights reserved. 2018-09-04T04:04:07Z 2018-09-04T04:04:07Z 2011-09-01 Journal 02608774 2-s2.0-79957702414 10.1016/j.jfoodeng.2011.04.020 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=79957702414&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/49572
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
topic Agricultural and Biological Sciences
spellingShingle Agricultural and Biological Sciences
Yuthana Phimolsiripol
Ubonrat Siripatrawan
Donald J. Cleland
Weight loss of frozen bread dough under isothermal and fluctuating temperature storage conditions
description Evaporative weight loss from food leads to both loss of saleable weight and quality deterioration so it need to be minimized. The effect of isothermal and fluctuating conditions on frozen dough weight loss was measured and compared with kinetic, physical and artificial neural network (ANN) models. Frozen dough samples were regularly weighed during storage for up to 112 days in loose-fitting plastic bags. The storage temperatures were in the range of -8 to -25 °C with fluctuations of ±0.1 °C (isothermal), ±1, ±3 or ±5 °C about the mean. For each combination of temperature and fluctuation amplitude, the rate of dough weight loss was constant. The rate of weight loss at constant temperature was nearly proportional to water vapour pressure consistent with standard theories for evaporative weight loss from packaged foods but was also accurately fitted by Arrhenius kinetics. Weight loss increased with amplitude of temperature fluctuations. The increase could not be fully explained by either the physic model based on water vapour pressure differences or the kinetic model alone. An ANN model with six neurons in the input layer, six neurons in hidden layers and one neuron in the output layer, achieved a good fit between experimental and predicted data for all trials. However, the ANN model may not be accurate for product, packaging and storage systems different to that studied. © 2011 Elsevier Ltd. All rights reserved.
format Journal
author Yuthana Phimolsiripol
Ubonrat Siripatrawan
Donald J. Cleland
author_facet Yuthana Phimolsiripol
Ubonrat Siripatrawan
Donald J. Cleland
author_sort Yuthana Phimolsiripol
title Weight loss of frozen bread dough under isothermal and fluctuating temperature storage conditions
title_short Weight loss of frozen bread dough under isothermal and fluctuating temperature storage conditions
title_full Weight loss of frozen bread dough under isothermal and fluctuating temperature storage conditions
title_fullStr Weight loss of frozen bread dough under isothermal and fluctuating temperature storage conditions
title_full_unstemmed Weight loss of frozen bread dough under isothermal and fluctuating temperature storage conditions
title_sort weight loss of frozen bread dough under isothermal and fluctuating temperature storage conditions
publishDate 2018
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=79957702414&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/49572
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