Figen Tokatli

Antimicrobial and Antioxidant Activities of Turkish Extra Virgin Olive Oils

Turkish extra virgin olive oils (EVOO) from different varieties/geographical origins and their phenolic compounds were investigated in terms of their antimicrobial and antioxidant properties in comparison to refined olive, hazelnut, and canola oils. Antimicrobial activity was tested against three foodborne pathogenic bacteria, Escherichia coli O157:H7, Listeria monocytogenes , and Salmonella Enteritidis. Although all EVOOs showed a bactericidal effect, the individual phenolic compounds demonstrated only slight antimicrobial activity. Moreover, refined oil samples did not show any antimicrobial activity. Among the phenolic compounds, cinnamic acid (2 mg/kg of oil) had the highest percent inhibition value with 0.25 log reduction against L. monocytogenes. The synergistic interactions of tyrosol, vanillin, vanillic, and cinnamic acids were also observed against Salmonella Enteritidis. The antioxidant activities of oils were tested by beta-carotene-linoleate model system and ABTS method. In both methods, EVOOs showed higher antioxidant activities, whereas refined oils had lower activity. The ABTS method provided a higher correlation (0.89) with total phenol content.

Effects of malaxation temperature and harvest time on the chemical characteristics of olive oils

The aim of the study was to determine the effects of harvest time and malaxation temperature on chemical composition of olive oils produced from economically important olive varieties with a full factorial experimental design. The oils of Ayvalik and Memecik olives were extracted in an industrial two-phase continuous system. The quality parameters, phenolic and fatty acid profiles were determined. Harvest time, olive variety and their interaction were the most significant factors. Malaxation temperature was significant for hydroxytyrosol, tyrosol, p-coumaric acid, pinoresinol and peroxide value. Early and mid-harvest oils had high hydroxytyrosol and tyrosol (maximum 20.7mg/kg) and pigment concentrations (maximum chlorophyll and carotenoids as 4.6mg/kg and 2.86mg/kg, respectively). Late harvest oils were characterized with high peroxide values (9.2-25meqO2/kg), stearic (2.4-3.1%) and linoleic acids (9.3-10.4%). Multivariate regression analysis showed that oxidative stability was affected positively by hydroxytyrosol, tyrosol and oleic acid and negatively by polyunsaturated fatty acids.

Characterization and Classification of Turkish Wines Based on Elemental Composition

Commercial wines from 13 native and nonnative varieties in Turkey were analyzed for their elemental composition. Wines from four vintages (2006–2009) were analyzed by inductively coupled plasma with atomic emission spectrometry and mass spectroscopy (ICP-AES and ICP-MS) followed by multivariate statistics to study vintage, varietal, and regional differences. According to the partial least squares-discriminant analysis, wines from western regions could be discriminated with their higher Pb content. The red wines of two native grapes, Boğazkere and Öküzgözü, were separated from the remaining varieties based on their high Ca and low B and Cu levels. Öküzgözü wines were different from Syrah and Cabernet Sauvignon wines. Similarly, native Emir wines showed differences from Muscat wines. The effective variables for discrimination analysis were natural minerals (Sr, Li, Al, Ba, and B) and minerals originating from agricultural activities, processing, or pollution (Ca, Cu, Mg, Co, Pb, and Ni). Characteristics of Turkish wines from native and nonnative grape varieties such as Cabernet Sauvignon, Merlot, Syrah, and Chardonnay were defined in terms of their mineral content for the first time.

Comparison of some chemical parameters of a naturally debittered olive (Olea europaea L.) type with regular olive varieties

Some olives grown in Karaburun peninsula in the west part of Turkey and mostly coming from Erkence variety lose their bitterness while still on the tree and are called Hurma among locals. This olive type does not require further processing to remove the bitter compounds. In this study, sugar, organic acid and fatty acid profiles of Hurma, Erkence (not naturally debittered) and Gemlik (commonly consumed as table olive) olives were determined throughout 8weeks of maturation period for two consecutive harvest seasons, and the results were analysed by principal component analysis (PCA). PCA of sugar and organic acid data revealed a differentiation in terms of harvest year but not on variety. Hurma olive is separated from others due to its fatty acid profile, and it has higher linoleic acid content compared to others. This might be an indication of increased desaturase enzyme activity for Hurma olives during natural debittering phase.

Phenolics profile of a naturally debittering olive in comparison to regular olive varieties

BACKGROUND Hurma, an olive variety that grows in a specific area in Turkey, loses its bitterness before harvesting, and therefore does not need further processing steps for the production of table olives. The total phenol content and phenolic profiles of (1) this naturally debittered olive type, Hurma; (2) the same olive variety, but not a naturally debittered type, Erkence; and (3) another variety, Gemlik, which is commonly consumed as table olive, were determined during their maturation period for two harvest years. RESULTS The total phenol content of Hurma is the lowest compared to the other types regardless of harvest year, which has a significant effect on the phenolic content and composition of individual components for all olive types. All three olive types can be differentiated from each other especially during the late phase of maturation using the phenolics profile in combination with principal component analysis. CONCLUSION The natural debittering phenomenon of Hurma olive on the tree involves a decrease in phenol content and a change in phenol composition. The differentiation in phenol composition especially becomes very significant in the late of period of maturation.

Modeling of polygalacturonase enzyme activity and biomass production by Aspergillus sojae ATCC 20235

Aspergillus sojae, which is used in the making of koji, a characteristic Japanese food, is a potential candidate for the production of polygalacturonase (PG) enzyme, which of a major industrial significance. In this study, fermentation data of an A. sojae system were modeled by multiple linear regression (MLR) and artificial neural network (ANN) approaches to estimate PG activity and biomass. Nutrient concentrations, agitation speed, inoculum ratio and final pH of the fermentation medium were used as the inputs of the system. In addition to nutrient conditions, the final pH of the fermentation medium was also shown to be an effective parameter in the estimation of biomass concentration. The ANN parameters, such as number of hidden neurons, epochs and learning rate, were determined using a statistical approach. In the determination of network architecture, a cross-validation technique was used to test the ANN models. Goodness-of-fit of the regression and ANN models was measured by the R2 of cross-validated data and squared error of prediction. The PG activity and biomass were modeled with a 5-2-1 and 5-9-1 network topology, respectively. The models predicted enzyme activity with an R2 of 0.84 and biomass with an R2 value of 0.83, whereas the regression models predicted enzyme activity with an R2 of 0.84 and biomass with an R2 of 0.69.

Discriminative capacities of infrared spectroscopy and e-nose on Turkish olive oils

The potentials of Fourier transform (FT) near- (NIR) and mid-infrared (IR) spectroscopy, and electronic nose (e-nose) on varietal classification of Turkish olive oils were demonstrated. A total of 63 samples were analyzed, comprising Ayvalik, Memecik, and Erkence oils. Spectra were pretreated with standard normal variate and second derivative. Classification models were built with orthogonal partial least square-discriminant analysis (OPLS-DA), considering the single data sets and also the combined FT-NIR-IR spectra. OPLS-DA models were validated both by cross validation and external prediction. All the models gave good results, being the average correct classification percentages in prediction higher than 90% for spectroscopic data and equal to 82% for e-nose data. The combined FT-NIR-IR data set gave the best results in terms of coefficients of determination (0.95 and 0.67). Different e-nose sensors discriminated Ayvalik, Memecik, and Erkence oils, explaining their distinct aromatic profiles.

Combination of visible and mid-infrared spectra for the prediction of chemical parameters of wines

Rapid and environmentally friendly methods for the prediction of chemical compositions have been an interest in the wine industry. The objective of the study was to show the potentials of combined use of visible and mid-infrared (MIR) spectroscopies to improve the prediction of various chemical compounds of wine as opposed to using mid-infrared range only. Wine samples of twelve grape varieties from two harvest years were analyzed. The chemical composition of wine samples was related to MIR and visible spectra using orthogonal partial least square (OPLS) regression technique. The prediction abilities were tested with crossvalidation and independent validation sets. The coefficient of determination of validation (R2val) for anthocyanin compounds of red wines were between 0.76 and 0.90, and that for total phenol content was 0.90. Range of R2val for glycerol, glycerol/ethanol ratio, malic acid, o-coumaric acid and °Brix were between 0.77 and 0.96. The spectral ranges that played significant roles in the predictions were also determined. The validations with independent data sets showed that the combination of visible and MIR ranges with multivariate methods improved the prediction of anthocyanin compounds and total phenols; produced comparable results for the rest of the parameters as MIR. This is the first study in the literature that shows the practical use of visible spectra along MIR. The combined use of these spectral ranges with multivariate models can be applied for the rapid, on-line determination of quality parameters and chemical profiles of wines.