Application of Weibull analysis and artificial neural networks to predict the useful life of the vacuum packed soft cheese

Oblitas, Jimy; Sánchez-González, Jesús

doi:10.17533/udea.redin.n82a07

Cited by 4 publications

(5 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the model the input data were: temperatures, failure possibility and maturation time, received coefficient R 2 = 0.9996 for shelf-life and R 2 = 0.6897 for acidity, which show better accuracy than for the model using regression. The results presented by Sánchez-González et al [22] confirm our conclusions. In the studies by Bai et al [34], ANN was used for predicting moisture content and colour changes in ginkgo biloba seeds.…”

Section: Models Verificationsupporting

confidence: 91%

“…ANN was commonly employed in studies on food changes and shelf-life with a good accuracy determined on the basis of R 2 compared with other models such as regression. This tool was applied with very good results for vacuum packed soft cheese's shelf-life and acidity prediction, where a back propagation algorithm was used with supervised training [22]. In the model the input data were: temperatures, failure possibility and maturation time, received coefficient R 2 = 0.9996 for shelf-life and R 2 = 0.6897 for acidity, which show better accuracy than for the model using regression.…”

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%

See 2 more Smart Citations

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 Verificationsupporting

confidence: 91%

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

show abstract

“…According to the literature, ANN has been effectively used for predicting the shelf life of processed cheese [4], vacuum-packed soft cheese [5], French cheeses [6], white brined cheese [7], and Gouda cheese [1].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Optimization of Herb-Fortified Fresh Kombucha Cheese: An Artificial Neural Network Approach for Enhancing Quality Characteristics

Lončar,

Pezo,

Iličić

et al. 2024

Foods

View full text Add to dashboard Cite

In this study, an Artificial Neural Network (ANN) model is used to solve the complex task of producing fresh cheese with the desired quality parameters. The study focuses on kombucha fresh cheese samples fortified with ground wild thyme, supercritical fluid extract of wild thyme, ground sage and supercritical fluid extract of sage and optimizes the parameters of chemical composition, antioxidant potential and microbiological profile. The ANN models demonstrate robust generalization capabilities and accurately predict the observed results based on the input parameters. The optimal neural network model (MLP 6-10-16) with 10 neurons provides high r2 values (0.993 for training, 0.992 for testing, and 0.992 for validation cycles). The ANN model identified the optimal sample, a supercritical fluid extract of sage, on the 20th day of storage, showcasing specific favorable process parameters. These parameters encompass dry matter, fat, ash, proteins, water activity, pH, antioxidant potential (TP, DPPH, ABTS, FRAP), and microbiological profile. These findings offer valuable insights into producing fresh cheese efficiently with the desired quality attributes. Moreover, they highlight the effectiveness of the ANN model in optimizing diverse parameters for enhanced product development in the dairy industry.

show abstract

“…In addition to computational methods, many studies in the literature have attempted to use the neural network (NN) to anticipate the parameters of the WD in many areas, such as the method developed by Jesus that applies the Weibull and ANN analysis to anticipate the shelf life and acidity of vacuum-packed fresh cheese [23]. In survival analysis, Achraf constructed a deep neural network model called DeepWeiSurv.…”

Section: Introductionmentioning

confidence: 99%

Estimating Weibull Parameters Using Least Squares and Multilayer Perceptron vs. Bayes Estimation

Aydi¹,

Al-Duais²

2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

The Weibull distribution is regarded as among the finest in the family of failure distributions. One of the most commonly used parameters of the Weibull distribution (WD) is the ordinary least squares (OLS) technique, which is useful in reliability and lifetime modeling. In this study, we propose an approach based on the ordinary least squares and the multilayer perceptron (MLP) neural network called the OLSMLP that is based on the resilience of the OLS method. The MLP solves the problem of heteroscedasticity that distorts the estimation of the parameters of the WD due to the presence of outliers, and eases the difficulty of determining weights in case of the weighted least square (WLS). Another method is proposed by incorporating a weight into the general entropy (GE) loss function to estimate the parameters of the WD to obtain a modified loss function (WGE). Furthermore, a Monte Carlo simulation is performed to examine the performance of the proposed OLSMLP method in comparison with approximate Bayesian estimation (BLWGE) by using a weighted GE loss function. The results of the simulation showed that the two proposed methods produced good estimates even for small sample sizes. In addition, the techniques proposed here are typically the preferred options when estimating parameters compared with other available methods, in terms of the mean squared error and requirements related to time.

show abstract

Application of Weibull analysis and artificial neural networks to predict the useful life of the vacuum packed soft cheese

Cited by 4 publications

References 15 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

Modeling and Optimization of Herb-Fortified Fresh Kombucha Cheese: An Artificial Neural Network Approach for Enhancing Quality Characteristics

Estimating Weibull Parameters Using Least Squares and Multilayer Perceptron vs. Bayes Estimation

Contact Info

Product

Resources

About