Maja Benković

Theobroma cacao L. seeds, commonly known as cocoa beans, are the foundation for cocoa and chocolate production. Following harvest, these beans undergo a multi-step processing chain including fermentation, drying, roasting, and grinding. This process yields cocoa solids, cocoa butter, and cocoa powder—all fundamental ingredients in the food and beverage industry. Beyond its sensory appeal (flavor, aroma, and texture), cocoa has garnered significant interest for its potential health benefits attributed to a rich profile of bioactive compounds. Cocoa is a well-documented source of polyphenolics, specifically flavanols, alongside methylxanthines, phytosterols, and dietary fibers. These constituents have been associated with a diverse range of bioactivities, including antioxidant, anti-carcinogenic, anti-diabetic, anti-inflammatory, anti-obesity, and anti-allergenic properties, potentially contributing to overall health maintenance. Efficient extraction techniques are crucial for maximizing the recovery of these valuable bioactive components from cocoa plant material. Modern methods are continuously being explored to optimize this process. This review focuses on the established health benefits associated with the bioactive compounds present in cocoa. Additionally, it will explore and discuss contemporary approaches for the extraction of these bioactive compounds from this plant source.

We investigated the influence of cryogenic grinding on the quality of rapeseed cake. Rapeseed cake is a good source of valuable proteins (30%) and oil (14%), with a balanced fatty acid composition and a fair amount of sterols, which may reduce the risk of cardiovascular diseases. However, the presence of antinutritive compounds prevents its use as a food source. Grinding under cryogenic conditions is much more efficient than grinding at room temperature in terms of particle size reduction. The additional cryogenic grinding of the cake had little effect on the nutritional components, as phytosterols and soluble dietary fiber increased slightly. It had no effect on insoluble dietary fiber, polyphenols, and tannins. Prolonged grinding time, both at room and subzero temperatures, reduced the total amount of glucosinolates by 34 and 43%, respectively. However, the reduction in undesirable components is not sufficient to use cryogenic grinding as the sole treatment for the cake, but it could be a good pretreatment for chemical or biological treatments.

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.