Holistic Approach to the Uncertainty in Shelf Life Prediction of Frozen Foods at Dynamic Cold Chain Conditions

Systematic kinetic modeling is required to predict frozen systems behavior in cold dynamic conditions. A one-step procedure, where all data are used simultaneously in a non-linear algorithm, is implemented to estimate the kinetic parameters of both primary and secondary models. Compared to the tradi...

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Main Authors: Maria Giannakourou, Petros Taoukis
Format: Article
Language:English
Published: MDPI AG 2020-06-01
Series:Foods
Subjects:
Online Access:https://www.mdpi.com/2304-8158/9/6/714
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author Maria Giannakourou
Petros Taoukis
author_facet Maria Giannakourou
Petros Taoukis
author_sort Maria Giannakourou
collection DOAJ
description Systematic kinetic modeling is required to predict frozen systems behavior in cold dynamic conditions. A one-step procedure, where all data are used simultaneously in a non-linear algorithm, is implemented to estimate the kinetic parameters of both primary and secondary models. Compared to the traditional two-step methodology, more precise estimates are obtained, and the calculated parameter uncertainty can be introduced in realistic shelf life predictions, as a tool for cold chain optimization. Additionally, significant variability of the real distribution/storage conditions is recorded, and must be also incorporated in a kinetic prediction scheme. The applicability of the approach is theoretically demonstrated in an analysis of data on frozen green peas Vitamin C content, for the calculation of joint confidence intervals of kinetic parameters. A stochastic algorithm is implemented, through a double Monte Carlo scheme incorporating the temperature variability during distribution, drawn from cold chain databases. Assuming a distribution scenario of 130 days in the cold chain, 93 ± 110 days remaining shelf life was predicted compared to 180 days assumed based on the use by date. Overall, through the theoretical case study investigated, the uncertainty of models’ parameters and cold chain dynamics were incorporated into shelf life assessment, leading to more realistic predictions.
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spelling doaj.art-3acbe71e44304dd0904866685382951c2023-11-20T02:33:23ZengMDPI AGFoods2304-81582020-06-019671410.3390/foods9060714Holistic Approach to the Uncertainty in Shelf Life Prediction of Frozen Foods at Dynamic Cold Chain ConditionsMaria Giannakourou0Petros Taoukis1Department of Food Science and Technology, University of West Attica, 12243 Athens, GreeceLaboratory of Food Chemistry and Technology, School of Chemical Engineering, National Technical University of Athens, 15780 Athens, GreeceSystematic kinetic modeling is required to predict frozen systems behavior in cold dynamic conditions. A one-step procedure, where all data are used simultaneously in a non-linear algorithm, is implemented to estimate the kinetic parameters of both primary and secondary models. Compared to the traditional two-step methodology, more precise estimates are obtained, and the calculated parameter uncertainty can be introduced in realistic shelf life predictions, as a tool for cold chain optimization. Additionally, significant variability of the real distribution/storage conditions is recorded, and must be also incorporated in a kinetic prediction scheme. The applicability of the approach is theoretically demonstrated in an analysis of data on frozen green peas Vitamin C content, for the calculation of joint confidence intervals of kinetic parameters. A stochastic algorithm is implemented, through a double Monte Carlo scheme incorporating the temperature variability during distribution, drawn from cold chain databases. Assuming a distribution scenario of 130 days in the cold chain, 93 ± 110 days remaining shelf life was predicted compared to 180 days assumed based on the use by date. Overall, through the theoretical case study investigated, the uncertainty of models’ parameters and cold chain dynamics were incorporated into shelf life assessment, leading to more realistic predictions.https://www.mdpi.com/2304-8158/9/6/714cold chainfrozen foodsshelf life modelinguncertaintyvariabilityjoint confidence intervals
spellingShingle Maria Giannakourou
Petros Taoukis
Holistic Approach to the Uncertainty in Shelf Life Prediction of Frozen Foods at Dynamic Cold Chain Conditions
Foods
cold chain
frozen foods
shelf life modeling
uncertainty
variability
joint confidence intervals
title Holistic Approach to the Uncertainty in Shelf Life Prediction of Frozen Foods at Dynamic Cold Chain Conditions
title_full Holistic Approach to the Uncertainty in Shelf Life Prediction of Frozen Foods at Dynamic Cold Chain Conditions
title_fullStr Holistic Approach to the Uncertainty in Shelf Life Prediction of Frozen Foods at Dynamic Cold Chain Conditions
title_full_unstemmed Holistic Approach to the Uncertainty in Shelf Life Prediction of Frozen Foods at Dynamic Cold Chain Conditions
title_short Holistic Approach to the Uncertainty in Shelf Life Prediction of Frozen Foods at Dynamic Cold Chain Conditions
title_sort holistic approach to the uncertainty in shelf life prediction of frozen foods at dynamic cold chain conditions
topic cold chain
frozen foods
shelf life modeling
uncertainty
variability
joint confidence intervals
url https://www.mdpi.com/2304-8158/9/6/714
work_keys_str_mv AT mariagiannakourou holisticapproachtotheuncertaintyinshelflifepredictionoffrozenfoodsatdynamiccoldchainconditions
AT petrostaoukis holisticapproachtotheuncertaintyinshelflifepredictionoffrozenfoodsatdynamiccoldchainconditions