Prediction of Thiol Group Changes in Minced Raw and Cooked Chicken Meat with Plant Extracts—Kinetic and Neural Network Approaches
The aim of the study was to develop predictive models of thiol group (SH) level changes in minced raw and heat-treated chicken meat enriched with selected plant extracts (allspice, basil, bay leaf, black seed, cardamom, caraway, cloves, garlic, nutmeg, onion, oregano, rosemary, and thyme) during sto...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Animals |
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| Online Access: | https://www.mdpi.com/2076-2615/11/6/1647 |
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| author | Anna Kaczmarek Małgorzata Muzolf-Panek |
| author_facet | Anna Kaczmarek Małgorzata Muzolf-Panek |
| author_sort | Anna Kaczmarek |
| collection | DOAJ |
| description | The aim of the study was to develop predictive models of thiol group (SH) level changes in minced raw and heat-treated chicken meat enriched with selected plant extracts (allspice, basil, bay leaf, black seed, cardamom, caraway, cloves, garlic, nutmeg, onion, oregano, rosemary, and thyme) during storage at different temperatures. Meat samples with extract addition were stored under various temperatures (4, 8, 12, 16, and 20 °C). SH changes were measured spectrophotometrically using Ellman’s reagent. Samples stored at 12 °C were used as the external validation dataset. SH content decreased with storage time and temperature. The dependence of SH changes on temperature was adequately modeled by the Arrhenius equation with average high R<sup>2</sup> coefficients for raw meat (R<sup>2</sup> = 0.951) and heat-treated meat (R<sup>2</sup> = 0.968). Kinetic models and artificial neural networks (ANNs) were used to build the predictive models of thiol group decay during meat storage. The obtained results demonstrate that both kinetic Arrhenius (R<sup>2</sup> = 0.853 and 0.872 for raw and cooked meat, respectively) and ANN (R<sup>2</sup> = 0.803) models can predict thiol group changes in raw and cooked ground chicken meat during storage. |
| first_indexed | 2024-03-10T10:47:35Z |
| format | Article |
| id | doaj.art-269c78ff7d0d4d10b77b5e459023ca88 |
| institution | Directory Open Access Journal |
| issn | 2076-2615 |
| language | English |
| last_indexed | 2024-03-10T10:47:35Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Animals |
| spelling | doaj.art-269c78ff7d0d4d10b77b5e459023ca882023-11-21T22:28:20ZengMDPI AGAnimals2076-26152021-06-01116164710.3390/ani11061647Prediction of Thiol Group Changes in Minced Raw and Cooked Chicken Meat with Plant Extracts—Kinetic and Neural Network ApproachesAnna Kaczmarek0Małgorzata Muzolf-Panek1Department of Food Quality and Safety Management, Faculty of Food Science and Nutrition, Poznań University of Life Sciences, Wojska Polskiego 31, 60-637 Poznań, PolandDepartment of Food Quality and Safety Management, Faculty of Food Science and Nutrition, Poznań University of Life Sciences, Wojska Polskiego 31, 60-637 Poznań, PolandThe aim of the study was to develop predictive models of thiol group (SH) level changes in minced raw and heat-treated chicken meat enriched with selected plant extracts (allspice, basil, bay leaf, black seed, cardamom, caraway, cloves, garlic, nutmeg, onion, oregano, rosemary, and thyme) during storage at different temperatures. Meat samples with extract addition were stored under various temperatures (4, 8, 12, 16, and 20 °C). SH changes were measured spectrophotometrically using Ellman’s reagent. Samples stored at 12 °C were used as the external validation dataset. SH content decreased with storage time and temperature. The dependence of SH changes on temperature was adequately modeled by the Arrhenius equation with average high R<sup>2</sup> coefficients for raw meat (R<sup>2</sup> = 0.951) and heat-treated meat (R<sup>2</sup> = 0.968). Kinetic models and artificial neural networks (ANNs) were used to build the predictive models of thiol group decay during meat storage. The obtained results demonstrate that both kinetic Arrhenius (R<sup>2</sup> = 0.853 and 0.872 for raw and cooked meat, respectively) and ANN (R<sup>2</sup> = 0.803) models can predict thiol group changes in raw and cooked ground chicken meat during storage.https://www.mdpi.com/2076-2615/11/6/1647thiol contentprotein oxidationraw chickencooked chickenplant extractspredictive models |
| spellingShingle | Anna Kaczmarek Małgorzata Muzolf-Panek Prediction of Thiol Group Changes in Minced Raw and Cooked Chicken Meat with Plant Extracts—Kinetic and Neural Network Approaches Animals thiol content protein oxidation raw chicken cooked chicken plant extracts predictive models |
| title | Prediction of Thiol Group Changes in Minced Raw and Cooked Chicken Meat with Plant Extracts—Kinetic and Neural Network Approaches |
| title_full | Prediction of Thiol Group Changes in Minced Raw and Cooked Chicken Meat with Plant Extracts—Kinetic and Neural Network Approaches |
| title_fullStr | Prediction of Thiol Group Changes in Minced Raw and Cooked Chicken Meat with Plant Extracts—Kinetic and Neural Network Approaches |
| title_full_unstemmed | Prediction of Thiol Group Changes in Minced Raw and Cooked Chicken Meat with Plant Extracts—Kinetic and Neural Network Approaches |
| title_short | Prediction of Thiol Group Changes in Minced Raw and Cooked Chicken Meat with Plant Extracts—Kinetic and Neural Network Approaches |
| title_sort | prediction of thiol group changes in minced raw and cooked chicken meat with plant extracts kinetic and neural network approaches |
| topic | thiol content protein oxidation raw chicken cooked chicken plant extracts predictive models |
| url | https://www.mdpi.com/2076-2615/11/6/1647 |
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