Essential and toxic element bioaccumulations in fishes of Gala and Siğirci lakes (Meriç River Delta, Turkey)

Meriç River Delta is located in the Thrace Region of Turkey, and it is one of the most important wetlands worldwide. Gala and Sığırcı Lakes, which are known as significant lakes in Turkey in terms of especial biodiversity, are located in the Meriç River Delta and they are the main lentic factors of the system. The aim of this study was to evaluate the essential and toxic element bioaccumulation levels in fishes of Gala and Sığırcı Lakes from a statistical perspective by investigating a total of 25 macro- and micro-element bioaccumulations. One-Way ANOVA Test (OWAT) was applied to detected data in order to determine the statistical differences of element bioaccumulations among the fish species. Cluster analysis (CA) was also applied to detected data in order to classify the investigated elements in terms of bioaccumulation levels in fish tissues. According to the results of OWAT, although statistical differences were not recorded among the fish species in terms of essential element levels, significant statistical differences were recorded in terms of toxic element levels (P<0.05). According to the results of CA, 5 statistically significant clusters were formed, which were named as “Most intense elements”, “Second most intense elements”, “Moderate intense elements”, “Second rarest elements”, and “Rarest elements”. It was also found that toxic element bioaccumulation rates in fishes of Gala Lake were significantly higher than in fishes of Sığırcı Lake (P<0.05)

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