Jasenka Gajdoš Kljusurić

Obesity stands out as an ongoing pandemic today, and it is crucial to recognize the basic factors that influence it in the observed group and to intervene through lifestyle changes. Therefore, in this work, the k ketogenic diet (E = 6280 ± 210 kJ) was used in a weight loss program for two regionally different groups (including 200 participants) from southeastern European countries (Republic of North Macedonia (n = 100) and Kosovo (n = 100)). The applied data analysis revealed similarities and differences in (ii) the consumption of certain food groups (e.g., 0.5–1 kg Nuts/week; in region 1 is consumed by 11.3% of participants while in region 2 by 37.8%, respectively) and (ii) anthropometric indicators of excess body mass (body mass index and waist-to-hip ratio). Nutritional intervention with a ketogenic diet also reduces the intake of sweet and salty snacks that are rich in carbohydrates. The average expected time to reach the target body mass was 112 days, and the results of the progress of all participants were presented after 120 days. The results show regional differences, especially in women; in group 1, 73.91% achieved a body mass index in the healthy range (<25 kg/m2), while in group 2, the success rate was 81.69%. Understanding the different eating habits in the mentioned regions is key here, and it was shown that in region 2, over 40% of the participants consume 500–1000 g of seeds per week. The above indicates that the results of this study and regional differences can be considered when designing strategies and intervention programs in the lifestyle of overweight and obese people in similar environments. The study also shows that the ketogenic diet is one of the useful dietary intervention approaches used to change eating habits that will show results relatively quickly.

Dietary supplement users tend to have a better diet quality and overall prudent lifestyle. The main goals of this research were to report the prevalence and type of dietary supplements among Croatian adolescents and to examine the differences in the diet quality among dietary supplement users vs. non-users at the beginning (15/16 y) and at the end of high school education (18/19 y). This research is based on results of the longitudinal CRO-PALS study in which 607 adolescents participated, who had complete dietary, anthropometric, and physical activity data at the beginning (15/16 y) and at the end of their high school education (18/19 y). The dietary assessment method used was a single multi-pass 24 h recall. Dietary supplement users were divided into two groups for the purposes of statistical analysis—users of vitamin and multivitamin preparations (VMV) and users of mineral and multivitamin preparations (MMV). As they aged, there was an increase in the consumption of dietary supplements, and the most frequently used preparation in both age groups was vitamin C (23.7% of users). Dietary supplement users had a higher intake of non-carbonated sweetened drinks and a lower intake of fruits and vegetables in both genders and both age groups. Fast food intake was higher among dietary supplement girl users and boys who were not dietary supplements users in both age groups. Dietary supplement users had a higher achieved average intake of most micronutrients (values obtained only from food) in both genders and both age groups (with exceptions for certain vitamins and minerals). By observing other parameters for assessing the diet quality in this research, we can conclude that girls who do not use dietary supplements have a better diet quality in both age groups.

The potential of applying Artificial Neural Network (ANN) models based on near-infrared (NIR) spectra for the characterization of physical and chemical features of oil-in-aqueous oregano/rosemary extract emulsions was explored in this work. Emulsions were prepared using a batch emulsification process, with pea protein as the emulsifier. NIR spectral data were connected to the results of the analysis of physical and chemical properties of the emulsions (zeta potential, Feret droplet diameter, total polyphenolic content, and antioxidant capacity) with the final aim of quantitative prediction of the physical and chemical features. For that purpose, robust non-linear multivariate analysis (Artificial Neural Network modeling) was applied. The spectra themselves were preprocessed using several approaches (raw spectra, Savitzky–Golay smoothing, standard normal variate, and multiplicative scatter corrections) after which the impact of NIR spectral preprocessing on the ANN model’s efficiency was evaluated. The results show that NIR spectroscopy integrated with ANN computation can be employed to quantitatively predict the physical and chemical properties of oil-in-plant extract emulsions (R2 > 0.9).

Bromate formation is a complex process that depends on the properties of water and the ozone used. Due to fluctuations in quality, surface waters require major adjustments to the treatment process. In this work, we investigated how the time of year, ozone dose and duration, and ammonium affect bromides, bromates, absorbance at 254 nm (UV254), near-infrared (NIR) spectra, and fluorescent components (humic-like and tyrosine-like) during surface water ozonation. Linear and non-linear models were used to determine and predict the relationships between input and output variables. Season, ozonation dose and time were correlated with the output variables, while ammonium affected only bromates. All coefficients of determination (R2) for the multiple linear regression models were >0.64, while R2 for the piecewise linear regression models was >0.89. The season had no effect on bromate formation in either model, while ammonium only affected bromides and bromates. Three input variables influenced UV254 in both models. The artificial neural network (ANN) model with the season, ozonation dose and time, ammonium, and NIR spectra was an effective way to describe water ozonation results. The multilayer perception neural network 14-14-5 had the lowest errors and was the best ANN model with R2 values for training, testing, and validation of 0.9916, 0.9826, and 0.9732, respectively.