Die Chemie der Proteinoxidation in Lebensmitteln

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 R² coefficients for raw meat (R² = 0.951) and heat-treated meat (R² = 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 (R² = 0.853 and 0.872 for raw and cooked meat, respectively) and ANN (R² = 0.803) models can predict thiol group changes in raw and cooked ground chicken meat during storage.

What Determines the Selectivity of Arginine Dihydroxylation by the Nonheme Iron Enzyme OrfP?

The nonheme iron enzyme OrfP reacts with l-Arg selectively to form the 3R,4R-dihydroxyarginine product, which in mammals can inhibit the nitric oxide synthase enzymes involved in blood pressure control. To understand the mechanisms of dioxygen activation of l-Arg by OrfP and how it enables two sequential oxidation cycles on the same substrate, we performed a density functional theory study on a large active site cluster model. We show that substrate binding and positioning in the active site guides a highly selective reaction through C³ -H hydrogen atom abstraction. This happens despite the fact that the C³ -H and C⁴ -H bond strengths of l-Arg are very similar. Electronic differences in the two hydrogen atom abstraction pathways drive the reaction with an initial C³ -H activation to a low-energy ⁵ σ-pathway, while substrate positioning destabilizes the C⁴ -H abstraction and sends it over the higher-lying ⁵ π-pathway. We show that substrate and monohydroxylated products are strongly bound in the substrate binding pocket and hence product release is difficult and consequently its lifetime will be long enough to trigger a second oxygenation cycle.