Screening of the most consumed beverages and spices for their bioactive non-nutrient contents

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

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.

Comprehensive study on the antioxidant capacity and phenolic profiles of black seed and other spices and herbs: effect of solvent and time of extraction

The effects of solvent and time of extraction on the antioxidant properties and phenolic profiles of 13 herbs and spices used for food seasoning and preservation were investigated. The profiles of phenolics in plant material were analysed by UPLC, and the chemometric approach was used for the deeper evaluation of relationships between phenolic compounds and the antioxidant properties of the samples. The best extraction conditions enabling to obtain the highest total phenolics and the antioxidant activity were: long time extraction (24 h) with 50% aqueous ethanol. The most potent antioxidant was clove with the ABTS—2495.85 µmol/g, DPPH—1443.35 µmol/g and FRAP—1310.91 µmol/g, which resulted from the high total phenolic content (TPC—167.22 mg GAE/g) and total flavonoid content (TFC—26.22 mg QE/g). Wide variations in the antioxidant activity (ABTS: 7.31—2495.85 µmol/g) and TPC (0.87–167.22 mg GAE/g) were observed with black seed extract showing one of the lowest values of the parameters studied. High linear correlation (above 0.95) calculated for the parameters showed significant contribution of phenolics to the antioxidant activity of spices. UPLC analysis together with PCA (principal component analysis) confirmed this relation and ABTS, FRAP, DPPH, TFC, TPC, total phenolic acids, protocatechuic acid, t-cinnamic acid, p-hydroxybenzoic acid determined sample distribution along PC1 (principal component 1), whereas total flavonoids determined by UPLC, kaempferol, catechin and luteolin along PC2. All samples were discriminated with 100% classification’s propriety according to GDA (general discrimination analysis) which proved huge diversity among phenolic profiles and the antioxidant properties of tested samples.

Predictive Modeling of Thiol Changes in Raw Ground Pork as Affected by 13 Plant Extracts-Application of Arrhenius, Log-logistic and Artificial Neural Network Models

In this study, predictive models of protein oxidation, expressed as the content of thiol groups (SH), in raw ground pork were established and their accuracy was compared. The SH changes were monitored during, maximum, 11 days of storage at five temperature levels: 4, 8, 12, 16, and 20 °C. The effect of 13 plant extracts, including spices such as allspice, black seed, cardamom, caraway, cloves, garlic, nutmeg, and onion, and herbs such as basil, bay leaf, oregano, rosemary, and thyme, on protein oxidation in pork was studied. The zero-order function was used to described SH changes with time. The effect of temperature was assessed by using Arrhenius and log–logistic equations. Artificial neural network (ANN) models were also developed. The results obtained showed very good acceptability of the models for the monitoring and prediction of protein oxidation in raw pork samples. High average R² coefficients equal to 0.948, 0.957, and 0.944 were obtained for Arhhenius, log-logistic and ANN models, respectively. Multiple linear regression (MLR) was used to assess the influence of plant extracts on protein oxidation and showed oregano as the most potent antioxidant among the tested ones in raw ground pork.

Predictive Modeling of Changes in TBARS in the Intramuscular Lipid Fraction of Raw Ground Beef Enriched with Plant Extracts

The aim of the study was to develop and compare the predictive models of lipid oxidation in minced raw beef meat enriched with selected plant extracts (allspice, basil, bay leaf, black seed, cardamom, caraway, cloves, garlic, nutmeg, onion, oregano, rosemary and thyme) expressed as value changes of TBARS (thiobarbituric acid reactive substances) in various time/temperature conditions. Meat samples were stored at the temperatures of 4, 8, 12, 16 and 20 °C. The value changes of TBARS in samples stored at 12 °C were used as the external validation dataset. Lipid oxidation increased significantly with storage time and temperature. The rate of this increase varied depending on the addition of the plant extract and was the most pronounced in the control sample. The dependence of lipid oxidation on temperature was adequately modeled by the Arrhenius and log-logistic equation with high average R2 coefficients (≥0.98) calculated for all extracts. Kinetic models and artificial neural networks (ANNs) were used to build the predictive models. The obtained result demonstrates that both kinetic Arrhenius (R2 = 0.972) and log-logistic (R2 = 0.938) models as well as ANN (R2 = 0.935) models can predict changes in TBARS in raw ground beef meat during storage.

Consumers' Perceptions of Coffee Health Benefits and Motives for Coffee Consumption and Purchasing

Coffee is popular worldwide and consumption is increasing, particularly in non-traditional markets. There is evidence that coffee consumption may have beneficial health effects. Consumers' beliefs in the health benefits of coffee are unclear. The study aimed at analyzing consumers' perceptions of coffee health benefits, consumption and purchasing motives of coffee consumers with positive perceptions of coffee health benefits, and willingness to pay for coffee with associated health claims. Data were collected through a face-to-face survey with consumers, resulting in a convenience sample of 250 questionnaires valid for data elaboration. Results were elaborated with factor analysis and logistic regression analysis. Findings revealed that a relevant minority of consumers believed that coffee could have positive health effects. The consumer with a positive perception of coffee health benefits is mostly male, young, works, is familiar with non-espresso-based coffee, consumes a limited amount of coffee (generally not for breakfast and often in social settings), and buys coffee at retail outlets. Consumers drink coffee for its energetic and therapeutic effects. Coffee consumption is still price-driven, but consumers are interested in purchasing coffee with associated health claims. There is the opportunity to improve the perception of coffee health benefits in consumers' minds.