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The aim of this study was to investigate the quality changes of raw ground pork with the addition of allspice, bay leaf, black seed, caraway, cardamom, cloves or nutmeg extract and stored at 4°C. Lipid oxidation was evaluated by the peroxide value (POV), conjugated diene (CD) content, oxidation induction period (IP) by differential scanning calorimetry (DSC), thiobarbituric acid reactive substances (TBARS), and hexanal content; whereas protein oxidation by the thiol group content. Moreover, total viable aerobic bacteria count (TVC), Pseudomonas, Enterobacteriaceae, and lactic acid bacteria growths, pH and finally color of all samples were determined. POV, CD, TBARS and thiol group content were found to be highly correlated. Clove extract showed the highest antioxidant activity (1443 μM/g) and total phenolic content (TPC = 167 mg/g) and was the most effective antioxidant and antimicrobial agent in raw ground pork (TBARS = 0.31 mg/kg, POV = 5.1 meq O 2 /kg, thiol group content = 49 nmol/mg, IP = 68 min, TVC = 6.74). Cardamom and caraway also increased the oxidative stability of raw pork significantly (TBARS were equal to 0.3 and 0.28 mg/kg, POVs to 4.9 and 4.5 and thiol group content to 48 and 49 nmol/mg, respectively), despite their low antioxidant activities (72 and 300-fold lower than for cloves, respectively) and TPCs (1.2 and 2.4 mg/g). The results suggested that the application of natural antioxidants like spice extracts could enhance the stability and safety of raw ground pork, thus increasing its shelf-life. ARTICLE HISTORY

The aim of the study was to compare the ability of multiple linear regression (MLR) and Artificial Neural Network (ANN) to predict the overall quality of spreadable Gouda cheese during storage at 8 °C, 20 °C and 30 °C. The ANN used five factors selected by Principal Component Analysis, which was used as input data for the ANN calculation. The datasets were divided into three subsets: a training set, a validation set, and a test set. The multiple regression models were highly significant with high determination coefficients: R 2 = 0.99, 0.87 and 0.87 for 8, 20 and 30 °C, respectively, which made them a useful tool to predict quality deterioration. Simultaneously, the artificial neural networks models with determination coefficient of R 2 = 0.99, 0.96 and 0.96 for 8, 20 and 30 °C, respectively were built. The models based on ANNs with higher values of determination coefficients and lower RMSE values proved to be more accurate. The best fit of the model to the experimental data was found for processed cheese stored at 8 °C.

Determination of the botanical origin of raw spirit used for alcoholic beverage production is of great importance for rectifying units, control laboratories, and proper product labeling. Raw spirit samples (138) produced from rye, corn, and potato were analyzed using a solid phase microextraction-mass spectrometry (SPME-MS) method, which involved volatiles preconcentration by SPME with subsequent volatile fraction characterization by MS without particular compounds separation by GC. Obtained data were treated using principal component analysis and linear discriminant analysis (LDA) to test the possibility of sample classification. SPME sampling conditions allowed rapid extraction in 2 min at 50 °C using a carboxen/divinylbenzene/polydimethylsiloxane fiber, followed by rapid MS analysis. Use of LDA made possible the classification of raw spirits based on the material they were produced from. The classification ability of the developed SPME-MS method was 100%, whereas its prediction ability was 96%.

Plant oils are a good source of compounds that decrease the risk of some diseases. Growing consumer awareness has led to more interest in natural cold-pressed plant oils, which are often considered functional foods. Many unconventional edible plant oils are available on the market, but their quality and composition are often unknown. The aim of this study was to compare the nutritional value and quality of sixteen unconventional cold-pressed edible oils. The acid value (AV), peroxide value (PV), oxidative stability, fatty acid composition, phytosterol content, and tocochromanol content were measured for both fresh and stored oils. The nutritional quality indexes were also calculated. Cluster analysis of all factors showed that the oils fell into two groups: the first contained argan oil, pine oil, apricot kernel oil, avocado fruit oil, and macadamia nut oil; these had high levels of saturated and monounsaturated fatty acids and low amounts of phytosterols and tocochromanols. The second group included oil from dill seeds, milk thistle seeds, parsley seeds, watermelon seeds, safflower, poppy seeds, black cumin seeds, hemp, blackcurrant seeds, borage, and wheat germ. The highest tocochromanol and phytosterol content were observed in wheat germ and blackcurrant seed oil, and the lowest in black cumin and dill seed oil.