Quality and Shelf-Life Modeling of Frozen Fish at Constant and Variable Temperature Conditions

Tsironi, Theofania; Stoforos, Nikolaos G.; Taoukis, Petros

doi:10.3390/foods9121893

Cited by 14 publications

(8 citation statements)

References 38 publications

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“…There are currently many standard guidelines for assessing stability for pharmaceuticals regulated by organizations around the world such as ICH [1], WHO [2], Europe [3] and domestically according to Circular 32/2018/TT-BYT for pharmaceuticals and Circular 18/2019/TT-BYT for functional foods. For food, some methods have also been published to determine stability/shelf life such as: kinetic modeling, accelerated aging patterns on food products in general [4] or for some specific groups of subjects such as: cool products [5], frozen products [6], dried products [7], meat [8][9][10], grease [11], yogurt [12], ... Due to the differences in the nature of the sample background as well as the observed indicators, there is not a common method to apply to all food sample backgrounds. For each object or group of objects, it is necessary to survey and choose its own model and criteria.…”

Section: Introductionmentioning

confidence: 99%

Research for stability of food products and functional foods

Viết

Thị

Thi

et al. 2022

VJFC

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The study uses rapid aging and real-condition methods to assess the stability of frozen meat, yogurt and functional foods. The method of real conditions is applied to frozen meat and yogurt with the following factors being: temperature: -20°C ± 2°C; humidity: 100% for frozen meat and temperature: 4°C ± 2°C; humidity: > 80% for yogurt. The accelerated aging method is applied to functional foods in conditions: 35 ± 2°C, 45 ± 2°C, 55 ± 2°C, and humidity: 70 ± 5% in parallel assessment combination with real-time monitoring at 25 ± 2°C, and humidity 70 ± 5%. The results showed that the yogurt sample reached stability until the 45th day and did not reach from the 47th day due to layering and deposits at the bottom of the box. The frozen meat sample remained stable when monitored until the 12.6 months with all indicators. Functional food samples were estimated to reach stability of up to 40.3 months when using the Van't Hoff formula, 32.0 months when using the Q10 coefficient, and 36.8 months when monitored in real conditions. The results of the study indicate that the less stable criteria on each sample background are the basis for selecting evaluation criteria for subsequent studies that contribute to reducing the cost of monitoring. The difference in the time it takes to estimate the stability of the product when using different methods also helps manufacturers consider the appropriate evaluation method for their products.

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Section: Introductionmentioning

confidence: 99%

Research for stability of food products and functional foods

Viết

Thị

Thi

et al. 2022

VJFC

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“…Although freezing can significantly extend the shelf-life of fish, inevitably, cryopreservation can also lead to several undesirable effects, including moisture loss, protein degradation, loss of freshness, and microstructural disorders [6]. To minimize these undesirable effects, one promising method is achieved by the ice glazing [7,8].…”

Section: Introductionmentioning

confidence: 99%

“…Multiple accelerated shelf-life testing (MASLT) can monitor different characteristics of food, and it is an acceptable method for shelf-life prediction. In recent years, shelflife prediction of squids [15], gilthead sea bream [6], and dried shrimp [16] have been mostly predicted using MASLT. Therefore, the MASLT methods were chosen to predict the shelf-life of glazed large yellow croaker in our research.…”

Section: Introductionmentioning

confidence: 99%

“…Kinetic models are now being widely used to predict food quality changes, and there are many reports using the Arrhenius model to simulate the prediction of quality changes in fish. Examples include sardine fillets [17], rainbow trout [18], and tuna [6]. The use of the Arrhenius model provides a good prediction of the quality changes of fish products during storage.…”

Section: Introductionmentioning

confidence: 99%

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Shelf-Life Prediction of Glazed Large Yellow Croaker (Pseudosciaena crocea) during Frozen Storage Based on Arrhenius Model and Long-Short-Term Memory Neural Networks Model

Chu

Tan

et al. 2021

Fishes

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In this study, the changes in centrifugal loss, TVB-N, K-value, whiteness and sensory evaluation of glazed large yellow croaker were analyzed at −10, −20, −30 and −40 °C storage. The Arrhenius prediction model and long-short-term memory neural networks (LSTM-NN) prediction model were developed to predict the shelf-life of the glazed large yellow croaker. The results showed that the quality of glazed large yellow croaker gradually decreased with the extension of frozen storage time, and the decrease in quality slowed down at lower temperatures. Both the Arrhenius model and the LSTM-NN prediction model were good tools for predicting the shelf-life of glazed large yellow croaker. However, for the relative error, the prediction accuracy of LSTM-NN (with a mean value of 7.78%) was higher than that of Arrhenius model (with a mean value of 11.90%). Moreover, the LSTM-NN model had a more intelligent, convenient and fast data processing capability, so the new LSTM-NN model provided a better choice for predicting the shelf-life of glazed large yellow croaker.

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“…By implementation of kinetics on quality degradation and shelf life prediction of frozen foods, Tsironi et al (2021) [4] developed mathematical models to describe the effect of variable storage conditions on the quality of frozen fish and validated their applicability as effective predictive tools for frozen chain monitoring and management.…”

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confidence: 99%