Search results for: M. Sakač

Authors: A. Mandić, M. Sakač, A. Mišan, B. Šojić, L. Petrović, I. Lončarević, B. Pajin, I. Sedej

Abstract:

Aldehydes as secondary lipid oxidation products are highly specific to the oxidative degradation of particular polyunsaturated fatty acids present in foods. Gas chromatographic analysis of those volatile compounds has been widely used for monitoring of the deterioration of food products. Developed static headspace gas chromatography method using flame ionization detector (SHS GC FID) was applied to monitor the aldehydes present in processed foods such as bakery, meat and confectionary products. Five selected aldehydes were determined in samples without any sample preparation, except grinding for bakery and meat products. SHS–GC analysis allows the separation of propanal, pentanal, hexanal, heptanal and octanal, within 15min. Aldehydes were quantified in fresh and stored samples, and the obtained range of aldehydes in crackers was 1.62±0.05-9.95±0.05mg/kg, in sausages 6.62±0.46-39.16±0.39mg/kg; and in cocoa spread cream 0.48±0.01-1.13±0.02mg/kg. Referring to the obtained results, the following can be concluded, proposed method is suitable for different types of samples, content of aldehydes varies depending on the type of a sample, and differs in fresh and stored samples of the same type.

Keywords: lipid oxidation, aldehydes, crackers, sausage, cocoa cream spread

Procedia PDF Downloads 398

Authors: Milica Karadzic, Lidija Jevric, Sanja Podunavac-Kuzmanovic, Strahinja Kovacevic, Sinisa Markov, Aleksandar Okljesa, Andrea Nikolic, Marija Sakac, Katarina Penov Gasi

Abstract:

This study considered the microbiological activity determination and molecular docking study for selected steroid derivatives of biomedical importance. Minimal inhibitory concentration (MIC) was determined for steroid derivatives against Staphylococcus aureus using macrodilution method. Some of the investigated steroid derivatives express bacteriostatic effect against Staphylococcus aureus. Molecular docking approaches are the most widely used techniques for predicting the binding mode of a ligand. Molecular docking study was done for steroid derivatives for androgen receptor negative prostate cancer cell line (PC-3) toward Human Cytochrome P450 CYP17A1. The molecules that had the smallest experimental IC50 values confirmed their ability to dock into active place using suitable molecular docking procedure. The binding disposition of those molecules was thoroughly investigated. Microbiological analysis and molecular docking study were conducted with aim to additionally characterize selected steroid derivatives for future investigation regarding their biological activity and to estimate the binding-affinities of investigated derivatives. This article is based upon work from COST Action (TD1305), supported by COST (European Cooperation and Science and Technology).

Keywords: binding affinity, minimal inhibitory concentration, molecular docking, pc-3 cell line, staphylococcus aureus, steroids

Procedia PDF Downloads 340

Authors: Milica Karadzic, Lidija Jevric, Sanja Podunavac-Kuzmanovic, Strahinja Kovacevic, Anamarija Mandic, Katarina Penov Gasi, Marija Sakac, Aleksandar Okljesa, Andrea Nikolic

Abstract:

The present paper deals with chromatographic lipophilicity prediction of newly synthesized steroid derivatives. The prediction was achieved using in silico generated molecular descriptors and quantitative structure-retention relationship (QSRR) methodology with the artificial neural networks (ANN) approach. Chromatographic lipophilicity of the investigated compounds was expressed as retention factor value logk. For QSRR modeling, a feedforward back-propagation ANN with gradient descent learning algorithm was applied. Using the novel sum of ranking differences (SRD) method generated ANN models were ranked. The aim was to distinguish the most consistent QSRR model that can be found, and similarity or dissimilarity between the models that could be noticed. In this study, SRD was performed with average values of retention factor value logk as reference values. An excellent correlation between experimentally observed retention factor value logk and values predicted by the ANN was obtained with a correlation coefficient higher than 0.9890. Statistical results show that the established ANN models can be applied for required purpose. This article is based upon work from COST Action (TD1305), supported by COST (European Cooperation in Science and Technology).

Keywords: artificial neural networks, liquid chromatography, molecular descriptors, steroids, sum of ranking differences

Procedia PDF Downloads 293

Authors: Milica Karadžić, Lidija Jevrić, Sanja Podunavac-Kuzmanović, Strahinja Kovačević, Anamarija Mandić, Aleksandar Oklješa, Andrea Nikolić, Marija Sakač, Katarina Penov Gaši

Abstract:

Using chemometric approach, the relationships between the chromatographic lipophilicity and in silico molecular descriptors for twenty-nine selected steroid derivatives were studied. The chromatographic lipophilicity was predicted using artificial neural networks (ANNs) method. The most important in silico molecular descriptors were selected applying stepwise selection (SS) paired with partial least squares (PLS) method. Molecular descriptors with satisfactory variable importance in projection (VIP) values were selected for ANN modeling. The usefulness of generated models was confirmed by detailed statistical validation. High agreement between experimental and predicted values indicated that obtained models have good quality and high predictive ability. Global sensitivity analysis (GSA) confirmed the importance of each molecular descriptor used as an input variable. High-quality networks indicate a strong non-linear relationship between chromatographic lipophilicity and used in silico molecular descriptors. Applying selected molecular descriptors and generated ANNs the good prediction of chromatographic lipophilicity of the studied steroid derivatives can be obtained. This article is based upon work from COST Actions (CM1306 and CA15222), supported by COST (European Cooperation and Science and Technology).

Keywords: artificial neural networks, chemometrics, global sensitivity analysis, liquid chromatography, steroids

Procedia PDF Downloads 318