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

SIMPLE SUMMARY: Demand for poultry meat (chickens and turkeys) is constantly increasing. The upward trend in the production and consumption of poultry meat has two reasons. The first is the financial aspect because chicken meat is relatively cheap. The second reason is the nutritional and health aspect. Although the meat has high nutritional, dietary, culinary, technological, and sensory values, it is very susceptible to undesirable changes during storage, mainly due to the growth of microflora but also due to lipid and protein oxidation. The use of plant extracts in food technology is multifunctional, as they exhibit antioxidant and antibacterial effects and have a beneficial effect on the texture of meat and meat products. Moreover, the antioxidant effect of compounds isolated from plants may influence consumer health. Antioxidants of plant origin can be used as an additive to animal feed, as well as a component of stuffing or marinating mixes for meat. In addition, they are used in the coating of raw materials or in active packaging for food products. So far, many studies have shown the positive effect of plant and plant extract addition to meat on the oxidative status of its protein. However, the predictive approach to protein oxidation in raw meat is still little described. This study has demonstrated the potential usefulness of the kinetic model as well as models based on artificial neural networks (ANNs) to the realistic prediction of protein oxidation expressed as thiol group (SH) changes in raw and cooked chicken meat during storage. Such predictive models allow us to predict oxidative changes in minced meat under different time and temperature conditions as minced meat is particularly susceptible to oxidation through exposure to oxygen during the mincing process itself and through the increased contact surface with oxygen. This knowledge is very useful in designing food products and predicting their shelf-life. Additionally, the effectiveness of various spices in the raw and cooked meat system were compared. Meat is a very complex system and, according to the research, there is no direct correlation between the anti-oxidant activity of the spice itself and its antioxidant effectiveness in the product. ABSTRACT: 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(2) coefficients for raw meat (R(2) = 0.951) and heat-treated meat (R(2) = 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(2) = 0.853 and 0.872 for raw and cooked meat, respectively) and ANN (R(2) = 0.803) models can predict thiol group changes in raw and cooked ground chicken meat during storage.