Comparison between the Arrhenius model and the radial basis function neural network (RBFNN) model for predicting quality changes of frozen shrimp (<i>Solenocera melantho</i>)

Xu, Zhen; Liu, Xiaochang; Wang, Huiyi; Hong, Hui; Luo, Yongkang

doi:10.1080/10942912.2016.1248292

Cited by 21 publications

(15 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Comparing the results obtained using ANN models and kinetic models proves that the ANN models give the highest correlation of prediction. Moreover, the unique feature of the artificial neural network is found independently and eliminates error, making the ANN models more useful as a prediction tool [27,36].…”

Section: Models Verificationmentioning

confidence: 99%

See 1 more Smart Citation

Multiple regression models and Artificial Neural Network (ANN) as prediction tools of changes in overall quality during the storage of spreadable processed Gouda cheese

Stangierski

Weiss

Kaczmarek

2019

Eur Food Res Technol

View full text Add to dashboard Cite

The aim of the study was to compare the ability of multiple linear regression (MLR) and Artificial Neural Network (ANN) to predict the overall quality of spreadable Gouda cheese during storage at 8 °C, 20 °C and 30 °C. The ANN used five factors selected by Principal Component Analysis, which was used as input data for the ANN calculation. The datasets were divided into three subsets: a training set, a validation set, and a test set. The multiple regression models were highly significant with high determination coefficients: R 2 = 0.99, 0.87 and 0.87 for 8, 20 and 30 °C, respectively, which made them a useful tool to predict quality deterioration. Simultaneously, the artificial neural networks models with determination coefficient of R 2 = 0.99, 0.96 and 0.96 for 8, 20 and 30 °C, respectively were built. The models based on ANNs with higher values of determination coefficients and lower RMSE values proved to be more accurate. The best fit of the model to the experimental data was found for processed cheese stored at 8 °C.

show abstract

Section: Models Verificationmentioning

confidence: 99%

“…ANNs have been used to define shelf-life for predicting food quality, e.g. soft cheese [22], spreadable processed cheese [8], UHT milk [23], soybean and soya milk [11,24], sensory attributes of noodles [25], fruit and fruit juice [26] and perishable products [23,27].…”

Section: Introductionmentioning

confidence: 99%

Multiple regression models and Artificial Neural Network (ANN) as prediction tools of changes in overall quality during the storage of spreadable processed Gouda cheese

Stangierski

Weiss

Kaczmarek

2019

Eur Food Res Technol

View full text Add to dashboard Cite

show abstract

“…e electrical conductivity value of all samples increased significantly (p < 0.05), while the CK and T0 samples showed higher values during storage (Figure 2(b)). e RPPE could slow the electrical conductivity increase, which may be due to ionic substances produced by bacteria and decomposed muscle tissues [33].…”

Section: Ph and Electrical Conductivity Analysismentioning

confidence: 99%

Application of Gelatin Incorporated with Red Pitaya Peel Methanol Extract as Edible Coating for Quality Enhancement of Crayfish (Procambarus clarkii) during Refrigerated Storage

Liu

Shen

et al. 2019

Journal of Food Quality

View full text Add to dashboard Cite

China is one of the largest producers of red pitaya in the world and responsible for disposal of the huge amount of peel generated as a waste. The objective of this research was to evaluate the effect of the addition of red pitaya peel extract (RPPE, 1.0%, 2.0%, or 3.0% (w/v)) and 0.1% ε-polylysine (ε-PL) to a fish gelatin edible coating on the preservation of deshelled crayfish (Procambarus clarkii) during refrigerated storage. The physicochemical and water migration of the samples were determined during 8-day storage. Deshelled crayfish packaged in edible coatings exhibited significantly (p<0.05) lower values for total volatile basic nitrogen (TVB-N), K value maintenance, and free amino acids (FAAs). This study shows that application of an edible coating incorporated with RPPE and ε-PL is an effective strategy in retarding the quality deterioration in deshelled crayfish during storage.

show abstract

“…Xu et al. (2017) assessed the changes in the quality of frozen shrimp ( Solenocera melantho ) by using the radial basis function neural network (RBFNN) model. Yang et al.…”

Section: Introductionmentioning

confidence: 99%

Modeling quality changes in Pacific white shrimp ( Litopenaeus vannamei ) during storage: Comparison of the Arrhenius model and Random Forest model

Chen

Tao

Pan

et al. 2020

J Food Process Preserv

View full text Add to dashboard Cite

To investigate the quality changes of Pacific white shrimp stored at 4, −3, and −20°C, indicators including sensory assessment, pH, texture (hardness, springiness, gumminess, chewiness), thiobarbituric acid (TBA), total sulfhydryl content, Ca2+‐ATPase activity, and total viable counts (TVC) were studied in this work. The Random Forest model was chosen to estimate the quality changes of these indicators in comparison with the Arrhenius model. During different temperatures, pH, TBA, and TVC increased with the extension of storage time, while the other indicators decreased. Compared with the Arrhenius model, the relative errors of quality indicators of the Random Forest model were below 10%, r2 was close to 1, and root mean square error was mostly below 0.1, which meant a better fitting property for these indicators. Thus, the Random Forest model with higher prediction accuracy is a hopeful method for predicting the changes in the quality of Pacific white shrimp. Practical applications The Random Forest model provides a more accurate and convenient model to predict the quality changes of Pacific white shrimp under the temperature range from −20°C to 4°C, which shows a potential use for shrimp preservation and processing. Random forest model cannot only be used for estimating soil calcium carbonate and other regression issues, but also assessing the shelf life by predicting the values of quality indicators of aquatic products during its storage.

show abstract

Comparison between the Arrhenius model and the radial basis function neural network (RBFNN) model for predicting quality changes of frozen shrimp (Solenocera melantho)

Cited by 21 publications

References 31 publications

Multiple regression models and Artificial Neural Network (ANN) as prediction tools of changes in overall quality during the storage of spreadable processed Gouda cheese

Multiple regression models and Artificial Neural Network (ANN) as prediction tools of changes in overall quality during the storage of spreadable processed Gouda cheese

Application of Gelatin Incorporated with Red Pitaya Peel Methanol Extract as Edible Coating for Quality Enhancement of Crayfish (Procambarus clarkii) during Refrigerated Storage

Modeling quality changes in Pacific white shrimp ( Litopenaeus vannamei ) during storage: Comparison of the Arrhenius model and Random Forest model

Contact Info

Product

Resources

About