Predicting the shelf life of Trachinotus ovatus during frozen storage using a back propagation (BP) neural network model

Lan, Weiqing; Yang, Xin; Gong, Taoshuo; Xie, Jing

doi:10.1016/j.aaf.2021.12.016

Cited by 20 publications

(13 citation statements)

References 32 publications

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“…In the study by Lan et al. (2022), BP was successfully used to predict the changes in Trachinotus ovatus at various storage freezing temperatures by measuring various values and optimizing data processing. The fitting degree was high, which helped in good prediction.…”

Section: Applications In the Food Sectormentioning

confidence: 99%

“…However, in practical applications, the key lies in the builder's ability to optimize the model if good results are to be presented. The determination of the number of hidden layer nodes in the BP network depends on experience and trial and error (Lan et al., 2022), making it difficult to obtain the optimal network. However, the number of hidden layer nodes in RBF is determined during the training process, making it easier to obtain the optimal solution (Li et al., 2019).…”

Section: Applications In the Food Sectormentioning

confidence: 99%

“…The applicability and common functional areas of different shelf life models differ in different food products. Unlike the traditional kinetic models, the neural network model can effectively improve performance and is more adaptable because of its spontaneous function (Lan et al., 2022). The use of science‐based prediction methods allows us to fully estimate food shelf life (Zou & Li, 2020).…”

Section: Conclusion and Prospectsmentioning

confidence: 99%

“…where n 0 and n 1 are the numbers of input and output nodes, respectively, and a is a constant (Lan et al, 2022). The prediction accuracy of different neural network models can be assessed using relative error (E):…”

Section: Backpropagation Neural Networkmentioning

confidence: 99%

“…The equation for hidden nodes is as follows:

\begin{equation} \def\eqcellsep{&}\begin{array}{*{20}{c}} {n\ = {{\left( {{n}_0 + {n}_1} \right)}}^{\frac{1}{2}} + a} \end{array} ,\end{equation}

where n 0 and n 1 are the numbers of input and output nodes, respectively, and a is a constant (Lan et al., 2022).…”

Section: Introduction and Mechanism Of Shelf Life Modelmentioning

confidence: 99%

See 4 more Smart Citations

Recent advances in shelf life prediction models for monitoring food quality

Cui

Zheng

Wang

et al. 2023

Comp Rev Food Sci Food Safe

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Each year, 1.3 billion tons of food is lost due to spoilage or loss in the supply chain, accounting for approximately one third of global food production. This requires a manufacturer to provide accurate information on the shelf life of the food in each stage. Various models for monitoring food quality have been developed and applied to predict food shelf life. This review classified shelf life models and detailed the application background and characteristics of commonly used models to better understand the different uses and aspects of the commonly used models. In particular, the structural framework, application mechanisms, and numerical relationships of commonly used models were elaborated. In addition, the study focused on the application of commonly used models in the food field. Besides predicting the freshness index and remaining shelf life of food, the study addressed aspects such as food classification (maturity and damage) and content prediction. Finally, further promotion of shelf life models in the food field, use of multivariate analysis methods, and development of new models were foreseen. More reliable transportation, processing, and packaging methods could be screened out based on real‐time food quality monitoring.

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Section: Applications In the Food Sectormentioning

confidence: 99%

Section: Applications In the Food Sectormentioning

confidence: 99%

Section: Conclusion and Prospectsmentioning

confidence: 99%

Section: Backpropagation Neural Networkmentioning

confidence: 99%

“…The equation for hidden nodes is as follows:

\begin{equation} \def\eqcellsep{&}\begin{array}{*{20}{c}} {n\ = {{\left( {{n}_0 + {n}_1} \right)}}^{\frac{1}{2}} + a} \end{array} ,\end{equation}

where n 0 and n 1 are the numbers of input and output nodes, respectively, and a is a constant (Lan et al., 2022).…”

Section: Introduction and Mechanism Of Shelf Life Modelmentioning

confidence: 99%

See 3 more Smart Citations

Recent advances in shelf life prediction models for monitoring food quality

Cui

Zheng

Wang

et al. 2023

Comp Rev Food Sci Food Safe

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The quality evaluation system of ideological and political classroom teaching in universities based on GA‐BP algorithm

Jing

2024

Concurrency and Computation

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SummaryThe advancement of teaching quality is an indispensable section of the reform and growth of universities, and ideological and political education has critical impact on ideological education. The quality of classroom education can provide data support for efficient development, and has crucial influence on achieving scientific, reasonable, and accurate evaluation of ideological and political teaching performance. Thus, a performance assessment system for ideological and political education in universities with genetic algorithm optimized neural network algorithm is put forward. First, based on existing teaching evaluation indicators and combined with actual situations, a targeted teaching quality evaluation system is proposed. Then, based on BP, an adaptive genetic algorithm is proposed for improvement, and the output results are improved using entropy method. The results indicated that the proposed model could reach its optimal state after 81 iterations in this study. In the fitting test, it reached 0.971. In actual testing, the average error was only 2.68, which was much bigger than the other three algorithms. Its accuracy was 2%–3.2% higher than that of the best existing algorithms. These results indicated that the method put forward in this study had better practical significance, lower error, more accurate evaluation results, and offered scientific data support for the education reform work of universities, which can better accelerate the development and construction of universities.

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Quality prediction of whole‐grain rice noodles using backpropagation artificial neural network

Wang,

Shi,

Xue

et al. 2024

J Sci Food Agric

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BACKGROUNDWhole‐grain rice noodles are a kind of healthy food with rich nutritional value, and their product quality has a notable impact on consumer acceptability. The quality evaluation model is of great significance to the optimization of product quality. However, there are few methods that can establish a product quality prediction model with multiple preparation conditions as inputs and various quality evaluation indexes as outputs. In this study, an artificial neural network (ANN) model based on a backpropagation (BP) algorithm was used to predict the comprehensive quality changes of whole‐grain rice noodles under different preparation conditions, which provided a new way to improve the quality of extrusion rice products.RESULTSThe results showed that the BP‐ANN using the Levenberg–Marquardt algorithm and the optimal topology (4‐11‐8) gave the best performance. The correlation coefficients (R2) for the training, validation, testing, and global data sets of the BP neural network were 0.927, 0.873, 0.817, and 0.903, respectively. In the validation test, the percentage error in the quality prediction of whole‐grain rice noodles was within 10%, indicating that the BP‐ANN could accurately predict the quality of whole‐grain rice noodles prepared under different conditions.CONCLUSIONThis study showed that the quality prediction model of whole‐grain rice noodles based on the BP‐ANN algorithm was effective, and suitable for predicting the quality of whole‐grain rice noodles prepared under different conditions. © 2024 Society of Chemical Industry.

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Predicting the shelf life of Trachinotus ovatus during frozen storage using a back propagation (BP) neural network model

Cited by 20 publications

References 32 publications

Recent advances in shelf life prediction models for monitoring food quality

Recent advances in shelf life prediction models for monitoring food quality

The quality evaluation system of ideological and political classroom teaching in universities based on GA‐BP algorithm

Quality prediction of whole‐grain rice noodles using backpropagation artificial neural network

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