Prediction of Thiol Group Changes in Minced Raw and Cooked Chicken Meat with Plant Extracts—Kinetic and Neural Network Approaches

Kaczmarek, Anna; Muzolf-Panek, Małgorzata

doi:10.3390/ani11061647

Cited by 3 publications

(1 citation statement)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, Xu et al [7] presented a neural networkbased approach to anticipate changes in the quality of frozen shrimp (Solenocera melantho). In their recent work, Kaczmarek and Muzolf-Panek [8] used predictive models to monitor changes in the levels of the thiol group (SH) in raw and thermally processed ground chicken meat that had been enriched with selected plant extracts during storage at different temperatures. Other researchers, including Taheri-Garavand et al [9] and Lalabadi et al [10], have also used various ANN models to analyze the quality, production optimization, and sensory freshness of various food products.…”

Section: Introductionmentioning

confidence: 99%

Storage Time prediction of Frozen Meat using Artificial Neural Network modeling with Color values

Lakehal

2023

RC FCV-LUZ

View full text Add to dashboard Cite

Among the various methods available to determine the storage time of frozen meat, including analyses based on physical and chemical properties, sensory analysis, particularly color changes, is an important aspect of meat acceptability for consumers. In this study, an artificial neural network (ANN) was employed to predict the storage time of the meat based on the CIELAB color space, represented by the Lab* (L*), (a*), and (b*) values measured by a computer vision system at two–month intervals over a period of up to one year. The ANN topology was optimized based on changes in correlation coefficients (R2) and mean square errors (MSE), resulting in a network of 60 neurons in a hidden layer (R2 = 0.9762 and MSE = 0.0047). The ANN model's performance was evaluated using criteria such as mean absolute deviation (MAD), MSE, root mean square error (RMSE), R2, and mean absolute error (MAE), which were found to be 0.0344, 0.0047, 0.0687, 0.9762, and 0.0078, respectively. Overall, these results suggest that using a computer vision–based system combined with artificial intelligence could be a reliable and nondestructive technique for evaluating meat quality throughout its storage time.

show abstract

Section: Introductionmentioning

confidence: 99%

Storage Time prediction of Frozen Meat using Artificial Neural Network modeling with Color values

Lakehal

2023

RC FCV-LUZ

View full text Add to dashboard Cite

show abstract

Predictive modelling of TBARS changes in the intramuscular lipid fraction of raw ground pork enriched with plant extracts

Kaczmarek

Muzolf-Panek

2021

J Food Sci Technol

Self Cite

View full text Add to dashboard Cite

The aim of the study was to develop and compare the predictive models of lipid oxidation in minced raw pork meat enriched with selected plant extracts (allspice, basil, bay leaf, black seed, cardamom, caraway, cloves, garlic, nutmeg, onion, oregano, rosemary and thyme) by investigation TBARS values changes during storage at different temperatures. Meat samples with extract addition were stored under various temperatures (4, 8, 12, 16, and 20°C). TBARS values changes in samples stored at 12°C were used as external validation dataset. Lipid oxidation was evaluated by the TBARS content. Lipid oxidation increased with storage time and temperature. The dependence of lipid oxidation on temperature was adequately modelled by the Arrhenius and log-logistic equation with high R2 coefficients (0.98–0.99). Kinetic models and artificial neural networks (ANNs) were used to build the predictive models. The obtained result demonstrates that both kinetic Arrhenius (R2 = 0.83) and log-logistic (R2 = 0.84) models as well as ANN (R2 = 0.99) model can predict TBARS changes in raw ground pork meat during storage.

show abstract

Effect of chia seeds ( Salvia hispanica l .) inclusion in poultry diet on n-3 enrichment and oxidative status of meat during retail display

Terevinto

Puerto

Silva

et al. 2023

CyTA - Journal of Food

View full text Add to dashboard Cite

Prediction of Thiol Group Changes in Minced Raw and Cooked Chicken Meat with Plant Extracts—Kinetic and Neural Network Approaches

Cited by 3 publications

References 45 publications

Storage Time prediction of Frozen Meat using Artificial Neural Network modeling with Color values

Storage Time prediction of Frozen Meat using Artificial Neural Network modeling with Color values

Predictive modelling of TBARS changes in the intramuscular lipid fraction of raw ground pork enriched with plant extracts

Effect of chia seeds ( Salvia hispanica l .) inclusion in poultry diet on n-3 enrichment and oxidative status of meat during retail display

Contact Info

Product

Resources

About