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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.

With the increase in life expectancy, expectation of a longer healthy life is also increasing. Importance of consumption of certain foods is confirmed to have a strong effect on quality of life. One of the healthiest dietary patterns consistently associated with a range of beneficial health outcomes is the Mediterranean diet (MD). The aim of this study was to assess MD adherence in the population over 50 years of age, in Europe, with special reference to Croatia, and to assess regional differences and investigate the association with health-related indicators (disease incidence, body mass index (BMI), grip strength measure, control, autonomy, self-realization, and pleasure scale (CASP-12)). This research uses data from the SHARE project for the population over 50 years of age. The frequency of individual responses was analyzed (frequencies, cross tables, and appropriate tests of significance, depending on the data set), and logistic regression was used to connect adherence to the Mediterranean diet with health indicators. The results of the study indicate a positive correlation between adherence to the principles of the Mediterranean diet with CASP and self-perception of health, which the followers of the MD pattern predominantly rate as “very good” or “excellent” (37.05%) what is significantly different (p < 0.05) from individuals which do not follow the patterns of MD (21.55%). The regression models indicate significant changes in the measure of maximum grip strength also among MD followers (ORMEDIUM = 1.449; ORHIGH = 1.293). Data for EU countries are also classified by regions (Central and Eastern; Northern, Southern and Western Europe), additionally allocating Croatia, and the trends in meat, fish and egg consumption showed the greatest differences for Croatian participants (39.6% twice a week) versus participants from four European regions. Data for Croatia deviates from the European average also in terms of the proportion of overweight and obese persons in all observed age groups, of which the largest proportion is in the 50–64 age group (normal BMI: only 30.3%). This study extended the currently available literature covering 27 European countries, placing the findings in a wider geographical context. The Mediterranean diet has once again proven to be an important factor related to health-related behavior. The presented results are extremely important for public health services, indicating possible critical factors in preserving the health of the population over 50 years old.

Ketogenic diet is often used as diet therapy for certain diseases, among other things, its positive effect related to weight loss is highlighted. Precisely because of the suggestion that KD can help with weight loss, visceral obesity, and appetite control, 100 respondents joined the weight loss program (of which 31% were men and 69% were women). The aforementioned respondents were interviewed in order to determine their eating habits, the amount of food consumed, and the time when they consume meals. Basic anthropometric data (body height, body mass, chest, waist, hips, biceps, and thigh circumferences) were also collected, in order to be able to monitor their progress during the different phases of the ketogenic diet. Important information is the expected body mass during the time frame of a certain keto diet phase. This information is important for the nutritionist, medical doctor, as well as for the participant in the reduced diet program; therefore, the model was developed that modified the original equation according to Wishnofsky. The results show that women lost an average of 22.7 kg (average number of days in the program 79.5), and for men the average weight loss was slightly higher, 29.7 kg (with an average of 76.8 days in the program). The prediction of expected body mass by the modified Wishnofsky’s equation was extremely well aligned with the experimental values, as shown by the Bland-Altman graph (bias for women 0.021 kg and −0.697 kg for men) and the coefficient of determination of 0.9903. The modification of the Wishnofsky equation further shed light on the importance of controlled energy reduction during the dietetic options of the ketogenic diet.

Background: The COVID-19 pandemic has left substantial consequences on the mental health of the student population, primarily through an increase in the symptoms of acute stress and anxiety. Aim: This study evaluated diet quality and its association with mental state among Croatian students during the spring and winter COVID-19 lockdowns in 2020. Methods: Data were collected using an anonymous online self-reported questionnaire taken on two occasions. During the spring lockdown (from May 18 to June 7, 2020) and the winter lockdown (from December 14–22, 2020), 751 and 1188 students completed the questionnaire, respectively. Diet quality was determined using the pro-healthy diet index (pHDI) and non-healthy diet index (nHDI). Results: Although to the greatest extent associated with increased white meat, fish, and legumes intake, an increase in pHDI was also linked to increased consumption of water and sweets and decreased intake of processed meat products and nuts. Increased nHDI was mainly due to increased intake of processed meat products, alcohol, and sweets. Moreover, diet quality was higher during the lockdowns than in the periods before. However, diet quality increased to a lesser extent during the winter lockdown and was associated with a poorer mental state, shown as type D (Distressed) personality. Type D personality positively correlated with nHDI and sweets intake but negatively with pHDI, fruit, and vegetable consumption. Conclusion: These results indicate the need to alleviate further students’ diet quality and mental state impairment. Also, further investigation of the association between diet quality and mental state should be provided.

Microreactors have become an efficient tool for many enzymatic reactions because the laminar fluid flow within the microchannel enables precise process control, rapid mixing, and short residence time. This paper provides a systematic overview of the application of reaction kinetics and the mathematical modeling of enzymatic processes in microreactors. Rapid heat and mass transfer and a high surface-to-volume ratio are usually the reasons why reactions in microchannels proceed faster and with higher yields and productivity compared to conventional macroreactors. Since there are no radial diffusion limitations, microreactors are also an effective tool for determining the kinetic parameters of enzyme-catalyzed reactions. By eliminating the mass transfer effect on the reaction rate, the kinetics estimated in the microreactor are closer to the intrinsic kinetics of the reaction. In this review, the advantages and disadvantages of using microreactors are highlighted and the potential of their application is discussed. Advances in microreactors result in process intensification and more efficient biocatalytic processes in line with the advantages offered by the application of microreactors, such as (i) higher yields, (ii) a cleaner and improved product profile, (iii) scale-independent synthesis, (iv) increased safety, and (v) the constant quality of the output product through (vi) accelerated process development. Furthermore, microreactors are an excellent tool for kinetic studies under specified mass transfer conditions, enhancing the capabilities of other methods.

In the last twenty years, the application of microreactors in chemical and biochemical industrial processes has increased significantly. The use of microreactor systems ensures efficient process intensification due to the excellent heat and mass transfer within the microchannels. Monitoring the concentrations in the microchannels is critical for a better understanding of the physical and chemical processes occurring in micromixers and microreactors. Therefore, there is a growing interest in performing in-line and on-line analyses of chemical and/or biochemical processes. This creates tremendous opportunities for the incorporation of spectroscopic detection techniques into production and processing lines in various industries. In this work, an overview of current applications of ultraviolet–visible, infrared, Raman spectroscopy, NMR, MALDI-TOF-MS, and ESI-MS for monitoring (bio)catalytic processes in continuously operated microreactor systems is presented. The manuscript includes a description of the advantages and disadvantages of the analytical methods listed, with particular emphasis on the chemometric methods used for spectroscopic data analysis.

Lipid metabolism is a complex process and it is extremely helpful to simulate its performance with different models that explain all the biological processes that comprise it, which then enables its better understanding as well as understanding the kinetics of the process itself. Typically, kinetic parameters are obtained from a number of sources under specific experimental conditions, and they are a source of uncertainty. Sensitivity analysis is a useful technique for controlling the uncertainty of model parameters. It evaluates a model’s dependence on its input variables. In this work, hepatic lipid metabolism was mathematically simulated and analyzed. Simulations of the model were performed using different initial plasma glucose (GB) and plasma triacylglyceride (TAG) concentrations according to proposed menus for different meals (breakfast, lunch, snack and dinner). A non-stationary Fourier amplitude sensitivity test (FAST) was applied to analyze the effect of 78 kinetic parameters on 24 metabolite concentrations and 45 reaction rates of the biological part of the hepatic lipid metabolism model at five time points (tf = 10, 50, 100, 250 and 500 min). This study examined the total influence of input parameter uncertainty on the variance of metabolic model predictions. The majority of the propagated uncertainty is due to the interactions of numerous factors rather than being linear from one parameter to one result. Obtained results showed differences in the model control regarding the different initial concentrations and also the changes in the model control over time. The aforementioned knowledge enables dietitians and physicians, working with patients who need to regulate fat metabolism due to illness and/or excessive body mass, to better understand the problem.

Microencapsulation is a frequently used method for protection of functional properties of bioactives. In this study, alginate was used for microencapsulation of the bioactives from five Lamiaceae family plants: lavender, lemon balm, peppermint, sage and thyme. Analysis of the adsorption and release processes of Lamiaceae bioactives on alginate microbeads was also performed. Based on the analysis of the extract used for the adsorption process, it was concluded that the highest amount of polyphenols (30 % of the polyphenols contained in the extract) was transferred to the microbeads from the thyme extract. Also, a period of bead shrinkage during the adsorption process was detected which lasted until 30 minutes for thyme, 50 minutes for lavender, lemon balm and peppermint, while the shrinkage for the sage extract lasted for 60 minutes. During the release process, a simultaneous rise of conductivity, total dissolved solids (TDS), polyphenolic (TPC) and antioxidant capacity values in the release media was detected at regular time intervals. The best suited model for the description of the release kinetics was the Korsmeyer-Peppas model, based on which it was concluded that the lemon balm polyphenols entrapped in the alginate matrix were released the fastest (k = 0.326 ±0.048 min-1) and that the release was governed by a pseudo-Fickian diffusion mechanism, since the calculated release exponent values (n) were lower than 0.5.

Research background. The application of high power ultrasound combined with a slightly increased temperature on raw cow’s milk, skimmed cow’s milk and skimmed cow’s milk that passed the bactofugation process was analysed. We combined ultrasound with bactofugation of milk to achieve the microbiological accuracy that is equivalent to pasteurization. Experimental approach. The milk samples (200 mL) were treated for 2.5, 5, 7.5 and 10 min with high-power ultrasound (200 and 400 W) with a frequency of 24 kHz. The treatments were conducted with a constant duty cycle of 100 %. Temperatures during the treatments were 20 and 55 °C. The somatic cell count of the aerobic mesophilic bacteria, as well the number of Enterobacteriaceae, Escherichia coli and Staphylococcus aureus cells were analysed. Results and conclusions. From the perspective of the reduction of the total count of bacteria, the best result was achieved by high-power ultrasound at 400 W treated for 10 min. High reduction of Enterobacteriaceae, E. coli and S. aureus cells was achieved with ultrasound treatment of raw, skimmed and skimmed cow’s milk that passed the bactofugation with a power of 200 and 400 W regardless of the treatment time. Novelty and scientific contribution. This work combines bactofugation and high-power ultrasound for the inactivation of microoganisms. This combination was used at a slightly increased temperature (up to 55 °C), which is much more economical than pasteurization, while it preserves the sensory and physicochemical properties of milk.

The indigenous vineyard mycobiota contribute both to wine quality and vineyard sanitary status. Wines made from same grape variety but from different geographical locations are appreciated for their diversity. Because no information on indigenous mycobiota of Croatian grapevines is available, the aim of the present study was to start filling this knowledge gap by characterizing the indigenous mycobiota of Maraˇstina variety. The use of metataxonomic approach has enabled the identification of 25 different fungal genera present on Maraˇstina grape berries collected from 11 vineyards located within the Croatian coastal winegrowing region of Dalmatia (Northern Dalmatia, Dalmatian hinterland, Central and Southern Dalmatia). The substantial regional and local scale differences in their distribution were observed. Overall, Aureobasidium was the dominant genus followed by Cladosporium and Metschnikowia. Botrytis and Plenodomus were associated with the vineyards located in Central and Southern Dalmatia, whereas Pichia was associated with Northern Dalmatia vineyards. The largest abundance of Buckleyzyma, Cladosporium, Eremothecium, Fusarium, Papiliotrema, and Rhodotorula was observed in Dalmatian hinterland. Moreover, data suggested that climate conditions and soil type partially influenced the distribution of fungal communities. The local-scale differences emerged also for the physicochemical characteristics of fresh musts. The high malic acid content supported the development of Metschnikowia, and inhibited Fusarium growth, whereas a positive correlation between Erysiphe and pH values was observed. Sporobolomyces and Cystobasidium were negatively associated with high glucose concentration. The revealing of Maraˇstina indigenous mycobiota provided information on the members of fungal community negatively influencing the grapevine sanitary status as well as those which could be employed in disease biocontrol.

This study investigates the colour and standard chemical composition of must and wines produced from the grapes from Vitis vinifera L., ‘Maraština’, harvested from 10 vineyards located in two different viticultural subregions of the Adriatic region of Croatia: Northern Dalmatia and Central and Southern Dalmatia. The aim was to explore the use of NIR spectroscopy combined with chemometrics to determine the characteristics of Maraština wines and to develop calibration models relating NIR spectra and physicochemical/colour data. Differences in the colour parameters (L*, a*, hue) of wines related to the subregions were confirmed. Colour difference (ΔE) of must vs. wine significantly differed for the samples from the Maraština grapes grown in both subregions. Principal component regression was used to construct the calibration models based on NIR spectra and standard physicochemical and colour data showing high prediction ability of the 13 studied parameters of must and/or wine (average R2 of 0.98 and RPD value of 6.8). Principal component analysis revealed qualitative differences of must and wines produced from the same grape variety but grown in different subregions.

The effect of different drying methods (conventional at T = 50 °C and T = 70 °C and lyophilisation) on different types of root vegetables (celery, carrot, fennel, purple carrot, parsley and yellow carrot) was studied. All samples were subjected to 13 extraction procedures (12 with organic solvents and one with water) prior to and after drying. The obtained results for physical (colour, total dissolved solids and conductivity) and chemical properties (total polyphenolic content, antioxidant activity, vitamin C and β-carotene content) were analysed by artificial neural network models. Artificial neural network modelling showed that (i) the colour parameters, vitamin C concentrations and β-carotene concentrations in root vegetables after different drying methods and (ii) physical and chemical characteristics of the root vegetable extracts prepared after different drying methods can be predicted from the type of root vegetables, drying method and the extraction procedure.

Because of its high market value, pumpkin seed oil is occasionally adulterated by cheaper refined oils, usually sunflower oil. The standard method for detecting its authenticity is based on expensive and laborious determination of the sterol composition. Therefore, the objective of this study was to determine the sterol content and authenticity of retail oils labelled as pumpkin seed oil and also to investigate the potential of near-infrared spectroscopy (NIR) and colourimetry in detecting adulteration. The results show that due to the significant decrease in Δ7-sterols and increase in Δ5-sterols, 48% of the analysed oils can be declared as adulterated blends of pumpkin seed and sunflower oil. Significant differences in NIR spectroscopy data, in the range of 904–922 nm and 1675–1699 nm, and colourimetric data were found between the control pumpkin seed oil and sunflower oil, but only the NIR method had the potential to detect the authenticity of pumpkin seed oil, which was confirmed by principal component analysis. Orthogonal projection on latent structures (OPLS) discriminant analysis, resulted in working classification models that were able to discriminate pure and adulterated oil. OPLS models based on NIR spectra were also able to successfully predict the content of β-sitosterol and Δ7,22-stigmastadienol in the analysed oils.

This study investigates the colour and standard chemical composition of must and wines produced from the grapes from Vitis vinifera L., ‘Maraština’, harvested from 10 vineyards located in two different viticultural subregions of the Adriatic region of Croatia: Northern Dalmatia and Central and Southern Dalmatia. The aim was to explore the use of NIR spectroscopy combined with chemometrics to determine the characteristics of Maraština wines and to develop calibration models relating NIR spectra and physicochemical/colour data. Differences in the colour parameters (L*, a*, hue) of wines related to the subregions were confirmed. Colour difference (ΔE) of must vs. wine significantly differed for the samples from the Maraština grapes grown in both subregions. Principal component regression was used to construct the calibration models based on NIR spectra and standard physicochemical and colour data showing high prediction ability of the 13 studied parameters of must and/or wine (average R2 of 0.98 and RPD value of 6.8). Principal component analysis revealed qualitative differences of must and wines produced from the same grape variety but grown in different subregions.

There is a growing need for production of iron particles due to their possible use in numerous systems (e.g., electrical, magnetic, catalytic, biological and others). Although severe reaction conditions and heavy solvents are frequently used in production of nanoparticles, green synthesis has arisen as an eco-friendly method that uses biological catalysts. Various precursors are combined with biological material (such as enzymes, herbal extracts, biomass, bacteria or yeasts) that contain chemicals from the main or secondary metabolism that can function as catalysts for production of nanoparticles. In this work, batch (“one-pot”) biosynthesis of iron nanoparticles is reviewed, as well as the possibilities of using microfluidic systems for continuous biosynthesis of iron nanoparticles, which could overcome the limitations of batch synthesis.

The aim of the study was to assess the value and applicability of multivariate tools for hematological and plasma biochemical responses of fish living in treated wastewater. Physicochemical water properties and heavy metals concentration of water in spring and fall were used as determinants of multiple fish stressors. Three methods of data analysis (Agglomerative Hierarchical Clustering, Factor Analysis, Principal Component Analysis) and one method of data modeling (Partial Least Square Regression) were applied. These methods enabled identification of clustering based on observed parameters, identification of significant variables in the observed data set, and correlation of observed variables with samples collected in different places and at different seasons. Prediction of total leukocytes, lymphocytes, granulocytes, hematocrit, glucose, alanine aminotransferase, triglycerides and cholesterol from fish blood (R2 > 0.9) was better for fall than for spring variables, regardless the sampling site (R2 > 0.98). For hematocrit and glucose (determination coefficient over 0.99), prediction was successful regardless the season and the sampling site. The effectiveness of prediction models was also evaluated using ratio of standard error of performance to standard deviation (RPD), and range error ratio (RER). High applicability of these models was found for multiple purposes (RPD > 8 and RER > 15), including prediction of parameters from fish blood with regard to water quality.

In this study, the potential of microfluidic systems with different microchannel geometries (microchannel with teardrop micromixers and microchannel with swirl micromixers) for the preparation of oil-in-water (O/W) emulsions using two different emulsifiers (2% and 4% Tween 20 and 2% and 4% PEG 2000) at total flow rates of 20-280 L/min was investigated. The results showed that droplets with a smaller average Feret diameter were obtained when a microfluidic device with tear drop micromixers was used. To predict the average Feret diameter of O/W emulsion droplets, near- infrared (NIR) spectra of all prepared emulsions were collected and coupled with partial least squares (PLS) regression and artificial neural network modelling (ANN). The results showed that PLS models based on NIR spectra can ensure acceptable qualitative prediction, while highly non-linear ANN models are more suitable for predicting the average Feret diameter of O/W droplets. High R2 values (R2validation > 0.8) confirm that ANNs can be used to monitor the emulsification process.

The aim of this study was to compare the effectiveness of gas chromatography (GC) and near- infrared spectroscopy (NIR) spectroscopy in detecting butter adulteration. Butter samples with a known percentage of added margarine or pork fat were prepared. Chemometric techniques (principal component analysis and partial least squares regression) were applied on the NIR spectra and GC data, which enabled the qualitative separation of samples depending on the added proportion of adulterant, and the quantitative prediction of the proportion of added adulterant or certain fatty acid content. C12:0 ; C16:0 ; C18:0 ; C16:1 ; C18:1n9 ; C18:2n6 showed significant trend changes depending on the adulteration degree. NIR has proven to be a suitable technique for the rapid determination of butter adulteration.

Chemical characteristics of raw and processed Istarski pršut (PDO) produced from two different pig genotypes were studied with special emphasis on amino and fatty acid composition and factors of lipid stability. Raw hams of Large White (LW)xLandrace (L), and (LWxL)xDuroc (D) pig genotypes were used in the study (20 hams of each genotype). All left raw hams from each carcass were processed in accordance with the PDO specification of Istarski pršut, and other half (the right ones) of LWxL)xD genotype were used for analyses of raw hams (fresh muscles). Istarski pršut was evaluated on the basis of the chemical parameters of the raw and matured lean ham. The process of dry curing significantly influenced the chemical properties of Istarski pršut. Despite the higher content of intramuscular fat and polyunsaturated fatty acids, the fat of (LWxL)xD ham was much more resistant to hydrolysis and oxidation, suggesting that fatty acid profile and other factors, also play a significant role. Significant differences between pig genotypes in the amino acid and fatty acid profiles were found. The analyzed Istarski pršut may be distinguished by prints of multivariate chemometric statistical analysis, based on their amino acid and fatty acid compositions.

The objective of this work was to evaluate the ability of artificial neural networks (ANN) in near infrared (NIR) spectra calibration models to predict the total polyphenolic content, antioxidant activity, and extraction yield of the olive leaves aqueous extracts prepared with three extraction procedures (conventional extraction, microwave-assisted extraction, and microwave-ultrasound-assisted extraction). Partial least squares (PLS) models were developed from principal component analyses (PCA) scores of NIR spectra of olive leaf aqueous extracts in terms of total polyphenols concentration, antioxidant activity, and extraction yield for each extraction procedure. PLS models were used to view which PCA scores are the best suited as input for ANN based on three output variables. ANN showed very good correlation of NIRs and all tested variables, especially in the case of total polyphenolic content (TPC). Therefore, ANN can be used for the prediction of total polyphenol concentrations, antioxidant activity, and extraction yield of plant extracts based on the NIR spectra. This work is licensed under a Creative Commons Attribution 4.0 International License.

The foam mat drying process is a simple and economical alternative to spray and freeze drying. It is considerably cheaper due to rapid drying at low temperatures, preservation of nutritional quality and easy reconstitution. Basic principle of the process is conversion of a liquid or a semi-liquid foodstuff to foam and thin layer drying of the foam. This study explores the possibility of the foam mat drying for production of instant cocoa powder enriched with lavender (Lavandula × hybrida L.) extracts. The aqueous extraction of lavender was optimized using Taguchi orthogonal array design. Extracts obtained under optimal conditions were added to a mixture of egg white, cocoa powder, sugar and gelatine. Mixtures were blended for 4 min to obtain stable foam which was dried at three different temperatures (t=50, 60 and 70 °C) and milled into a powdered product. Drying rates were obtained from the experimental data using nonlinear model estimation. Flow properties, bulk density, particle size distribution, reconstitution and sensory properties of the final product were also assessed. Based on the obtained data, the drying process was best described by Page’s drying model. Samples dried at lower temperature (t=50 °C) exhibited the best powder flow and reconstitution properties. Sensory analysis resulted in similar findings: powder samples dried at lower temperatures had better appearance, colour and taste. The obtained data confirm the suitability of the foam mat drying for the production of instant cocoa powder enriched with lavender extract.

During the ageing period wine spirits are changing their color, chemical composition and sensory characteristics. These changes should be simply monitored. The aim of this study was to develop partial least squares regression (PLS) models for higher alcohols and phenols in wine spirits as well as to show the feasibility of the NIR spectroscopy combined with chemometric tools to distinguish wine spirits and brandies with different ageing degree. To get the reference values, the usual methods for the analysis of spirits drinks were used. Ethanol, esters, acids, methanol and higher alcohols were studied. Wine spirits and brandies phenol composition was determined by liquid chromatography. Principal component analysis (PCA) was used to classify the wine spirits and brandies according to their phenolic and higher alcohols composition. Moreover, the Partial least squares regression (PLS regression) was used to calibrate and predict expected contents of higher alcohols and phenols in the wine spirits. Success of the classification of samples by ageing based on individual alcohols was 93.8 %, while success of the classification based on individual phenols raised to 100 %. This efficiency of the prediction was evaluated by use of linear discriminator analysis (LDA).

Research background. Two methods of milk treatment were used, ultrasound (innovative method) and bactofugation, after which the physicochemical and sensory properties of the milk were examined, with the primary aim of achieving the quality and consistency of the pasteurized milk. Experimental approach. Ultrasound power of 200 and 400 W and frequency of 24 kHz with constant wave cycle were used. Milk treatment duration was 2.5, 5, 7.5 and 10 min with sonification at temperature of 20 °C (room temperature) and thermosonification (ultrasound at temperature higher than room temperature) at temperature of 55 °C. The purpose of this study is to investigate the effect of high-power ultrasound combined with a slightly increased temperature on whole, skimmed and skimmed cow's milk pretreated with bactofugation. Results and conclusions. The best sensory grade was achieved when milk was treated with ultrasound power of 200 W at 20 °C for max. 7.5 min. This research shows the potential of the applications of high-power ultrasound in dairy industry combined with bactofugation as a pretreatment of milk at a slightly increased temperature (up to 55 °C). Novelty and scientific contribution. The application of these two treatments requires milder processing conditions than pasteurization, it is economical and more environmentally friendly technological process that preserves better nutritional values of milk, which is preferred by costumers.

In this work, teardrop micromixer and swirl micromixer were used for preparation of oil-in-water (O/W) emulsions with Tween 20 and PEG 2000 as emulsifiers (concentrations: 2 % and 4 %) at different total flow rates (20 - 280 µL/min). Stability of the prepared O/W emulsions was evaluated based on the droplet size of the dispersed phase. For determination of the droplet size, the average Feret diameter was used. Furthermore, near infrared (NIR) spectra of all prepared samples were collected. Obtained results showed that the change in the droplet size followed the same trend for both micromixers used in the experiment. At higher total flow rates, emulsification resulted in smaller values of the average Feret diameter. Values of the average Feret diameter were higher for emulsions prepared in the swirl micromixer, compared to the teardrop micromixer. Artificial Neural Network (ANNs) models, based on the recorded NIR spectra of emulsions, were developed to predict the droplet size of the dispersed phase. The obtained ANN models have high values of R2 for training, test, and validation, with small error values and show that NIR spectroscopy, in combination with ANNs, could be efficiently used for evaluation of the stability of oil-in-water emulsions.

In the battle with nutrient deficiency, the production of food enriched with bioactive compounds is becoming a modern trend. There are numerous types and sources of bioactive compounds used for this purpose, ranging from compounds isolated from medicinal plants to those extracted from food waste. Although many foods are marketed as functional foods, the problem with bioactive compounds, in and from food sources, is that the health claims and their bioavailability are still not fully explored. There are many examples of bioactive’s functionalization health claims connected to their functional properties and their interactions in foods. This chapter leads the reader from the basic steps of acquiring bioactive compounds to their bioavailability analysis, protection and further improvement of their functional properties. The chapter also takes into account the fortification of foods with bioactive compounds as a strategy to reduce the occurrence of chronic illness as well as challenges that lie ahead for scientists dealing with all the aspects of bioactives, from processing to health claims.

RSM and ANN were evaluated for prediction of physical and chemical properties of industrial hemp extracts prepared using MAE. The effects of liquid to solid ratio, ethanol content, extraction time, extraction temperature and microwave power were analysed. ANN had better prediction performance for both physical and chemical properties. Optimal conditions based on RSM were L/S = 30 mL/g, v/v = 25%, t = 25 min, T = 45° C and P = 600 W. Under those conditions dynamic of the extraction process was analysed using six kinetic models and showed that the extraction rate reached constant value after approximately 15 min.

Honey is usually adulterated by cheaper, commercially available sugar syrups with similar chemical composition. Adulterated honeys are frequently marked as natural and are valued in the same way as pure honeys and as a result, there is a growing need for rapid, simple, and accurate analytical methods to evaluate their characteristics and authenticity.In this work potential of UV-VIS and NIR spectroscopy coupled with PLS modelling and ANN modelling was analysed for the detection of adulteration and quantification of the physical and chemical proprieties of pure and adulterated honey samples. Acacia honey samples from Krapina-Zagorje County (Croatia) were adulterated by corn syrup (10-90%). The results indicate that PLS models developed in this work, except that for moisture content, can be used efficiently for screening (7

Dehydration or drying of grapes is one of the most important steps in the production of Croatian traditional dessert wine Pro&scaron, ek. The natural sun drying of grapes is the traditionally used method in Pro&scaron, ek production. Alternative methods, such as dehydration under controlled conditions, have been studied as safer and faster methods than the traditional sun drying but without precise knowledge of the effect on volatile compounds. The objective of this work was to study how dehydration of grapes carried out in a greenhouse and an environmentally controlled chamber impacts on the free and glycosidically bound volatile compounds of native grape cv. &lsquo, Mara&scaron, tina&rsquo, The 36 volatile compounds were identified and quantified using headspace solid-phase micro extraction coupled with gas chromatography-mass spectrophotometry (HS-SPME-GC/MS). The results showed that the aroma profile of dehydrated grapes was significantly different from that of fresh grapes. Regarding free forms, significant increases in the concentration of 2-methyl-1-propanol, 1-butanol, 2-hexen-1-ol, 1-hexanol, ethyl hexanoate, hexyl acetate, o-cymene, linalool oxide, and terpinen-4-ol and geraniol were found in greenhouse-dried grapes, whereas increases in cis-limonene-epoxide, trans-limonene epoxide, and &gamma, hexalactone were higher in chamber-dried grapes compared to greenhouse-dried grapes. Glycosidically bound forms of o-cymene, linalool oxide, linalool, and terpinen-4-ol were increased in both types of drying, whereas &beta, damascenone was increased only in greenhouse-dried grapes.

The implementation of near-infrared (NIR) spectroscopy in the wastewater treatment has been continuously expanding. As an alternative to the conventional analytical methods for monitoring constituents in wastewater treatment processes, the use of NIR spectroscopy is considered cost-effective and less time- consuming. NIR spectroscopy does not in any way adulterate measured sample as no prior treatment is needed thus making it a waste-free technique. On the negative side, one must be very well acquainted with chemometric techniques to interpret results. In this study, filtered and centrifuged wastewater and sludge samples obtained from a lab-scale membrane bioreactor (MBR) were analysed. Essential wastewater constituents were determined and compared, employing two analytical methods (conventional and NIR spectroscopy). Special attention was given to the soluble microbial products (SMP) and the extracellular polymeric substances (EPS) which are known to promote membrane fouling. The measured parameters through NIR spectroscopy were analysed and processed by partial least squares regression (PLSR) and artificial neural networks (ANN) models to assess whether the evaluated wastewater constituents could be monitored by NIR spectroscopy. Very good results were obtained with PLSR models except for the determination of SMP thus limiting the model for their monitoring to qualitative rather than quantitative. ANN exhibited a better performance in terms of correlating NIR spectra with all the measured parameters, resulting in correlation coefficients in most cases higher than 0.97 for training, test, and validation. Based on the results achieved by this research, the combination of NIR spectra and chemometric modelling offers advantages compared to conventional analytical methods.

Publisher's version (útgefin grein), Consumer demand for food of high quality has driven research for alternative methods of food preservation on the one hand, and the development of new and rapid quality assessment techniques on the other hand. Recently, there has been a growing need and interest in healthier food products, which has led to an increased interest in natural preservatives, such as essential oils, plant extracts, and edible films and coatings. Several studies have shown the potential of using biopreservation, natural antimicrobials, and antioxidant agents in place of other processing and preservation techniques (e.g., thermal and non-thermal treatments, freezing, or synthetic chemicals). Changes in food quality induced by the application of natural preservatives have been commonly evaluated using a range of traditional methods, including microbiology, sensory, and physicochemical measurements. Several spectroscopic techniques have been proposed as promising alternatives to the traditional time-consuming and destructive methods. This review will provide an overview of recent studies and highlight the potential of spectroscopic techniques to evaluate quality changes in food products following the application of natural preservatives., The research leading to these results was funded by FEDER under the program Interreg V Spain-Portugal by POPTEC, IBERPHENOL Project (ref. 0377-Iberphenol-6-E) and by MICINN supporting the Ramón & Cajal grant for M.A. Prieto (RYC-2017-22891); by EcoChestnut Project (Erasmus+ KA202) supporting the work of M. Carpena and by Ibero-American Program on Science and Technology (CYTED—AQUA-CIBUS, P317RT0003).

Research background. Cheese in a sack is a traditional cheese produced in Croatia. Types of cheese with similar production technology are made in other countries but chemical and microbiological composition varies between regions. Traditionally, cheese in a sack is produced without the addition of starter cultures. Addition of beneficial probiotic cultures to numerous dairy products has documented advantages. Effects that the addition of probiotic bacteria to traditional cheese have on aroma compounds and sensory properties have not been fully investigated. The aim of this study is to determine the sensory properties and differences in the aromatic profiles between cheese samples ripened in a lambskin sack, produced traditionally without the addition of any starter culture, or with the addition of probiotic bacteria. Experimental approach. In this study, cheese in a sack was produced with the addition of probiotic cultures Lactobacillus plantarum B and L. lactis ssp. lactis S1. During ripening volatile aroma compounds were analysed with a solid-phase microextraction gas chromatography-mass spectrometry. Sensory properties were evaluated by trained tasters who are familiar with the traditional taste of the cheese from a sack. The results of aroma composition and taste scores were then compared using factorial and principal component analyses. Results and conclusions. Chromatography showed differences in the composition of aroma compounds and the sensory properties between the cheese produced with Lactobacillus starter cultures and the control cheese, traditionally produced without a starter culture. The addition of probiotic cultures L. plantarum B and L. lactis ssp. lactis S1 resulted in products with better sensory properties and chemical profile of volatile aromatic compounds. Novelty and scientific contribution. This study investigates the usage of naturally present probiotic cultures as starter cultures in cheese in a sack production. Their effects on aroma profiles and sensory characteristics have been compared for the first time using factorial and principal component analyses.

Objectives The aim of this study was to develop artificial neural network (ANNs) models for prediction of physical (total dissolved solids, extraction yield) and chemical (total polyphenolic content, antioxidant activity) properties of industrial hemp extracts, prepared by two different extraction methods (solid-liquid extraction and microwave-assisted extraction) based on combined UV-VIS-NIR spectra. Spectral data were gathered for 46 samples per extraction method. Results The PCA analysis ensured efficient separation of the samples based on the amount of ethanol in extraction solvent using NIR spectra for both conventional and microwave-assisted extraction. Conclusions Results showed that reliable ANN models (R2 >0.7000) for describing physical, chemical, and simultaneously physical and chemical characteristics can be developed based on combined UV-VIS-NIR spectra of industrial hemp extracts without spectra pre-processing.

Food processing is of great importance when obtaining products with specific organoleptic, physical and chemical properties, and is the most important step to assure food safety. Chemometrics in food processing is useful for determining the influence of the production procedure on changes in the chemical composition of foods. The main limitation is the fact that microorganisms are dynamic living entities and the food is a very complex matrix. Modelling and Simulation in food processing is used as a tool in designing and optimizing the operational parameters and is an important base of product quality and safety control as well as a useful tool for developing innovative food processing technologies. The full or fractional factorial design is used for screening and separation of effects since it is based on the numbers of factors (k) which most commonly have 2 values.

The stability of lavender, lemon balm, mint, sage, and thyme teas was investigated using in-vitro simulation of the digestive system. Kinetics of changes in the total polyphenolic content (TPC) and the antioxidant activity during the in-vitro trials were also evaluated. Results showed that TPC of mint teas decreases the fastest. Mathematical models for prediction of the TPC and the antioxidant activity of prepared teas based on UV-VIS and NIR spectra collected before, during, and after simulation showed that the best prediction was obtained for the wavelength ranges from 410 to 900 nm, 904 to 928 nm, and 1,399 to 1699 nm. It was concluded that the NIR can be used for calibration, validation, and classification of teas prepared from Lamiaceae plants. PRACTICAL APPLICATIONS: The bioactives' in-vitro digestion process can successfully be characterized by chemical, spectroscopic, and mathematical analysis. Application of NIR spectroscopy, in combination with multivariate analysis, leads to a reduction of time, costs, and chemical consumption and gives reliable results that pharmaceutical, food, and chemical industries can benefit from.

Nonthermal processing technologies have been researched in lab scale for more than 15 years. In this study, the influence of sonification and high voltage electrical discharge—plasma treatment of water on the formation and stabilization of hydroxyl radicals has been investigated by electron paramagnetic resonance (EPR) spectroscopy and spin‐trapping method. As analysis tool, an agglomerative hierarchical clustering was used in grouping samples recorded by EPR and near infrared spectroscopy (NIR) spectroscopy. Also, the multivariate analysis was performed using the principal component analysis (PCA) and Principal Regression models (PCR). Results for ultrasound‐treated samples showed that number of radicals is increasing with treatment time and increase in amplitude. High‐ voltage electrical discharge treatment at 120 Hz resulted in the formation of the largest number of hydroxyl radicals. Developed PCR models are highly effective in prediction of NIR spectra, regardless the sample treatment what is an additional step forward in green technology.

Hemp (Cannabis sativa L.) contains a wide range of biocompounds with different beneficial properties such as anti-inflammatory, antithrombotic, antiarrhythmic, hypolipidemic and antioxidative. Response Surface Methodology (RSM) coupled with Box-Behnken design (BBD) was applied to determine the influence of extraction temperature, liquid to solid ratio, extraction time, rotational speed and ethanol/water solvent ratios at three levels on the solid-liquid extraction of the bioactives from the hemp (flowers, leaves, seeds, stems). Based on the obtained results, liquid to solid ratio, temperature and ethanol/water solvent ratio had statistically significant effects on the total polyphenolic content (TPC), while extraction time and rotational speed had no influence on the TPC extraction. Regarding antioxidant activity (AOX) determined by the DPPH method, only liquid to solid ratio had a statistically significant effect. Liquid to solid ratio, ethanol/water solvent ratio, temperature and rotational speed significantly influenced AOX determined by the FRAP method. According to BBD, the optimum extraction conditions were as follows: extraction temperature 45 °C, liquid to solid ratio 30 mL/g, extraction time 25 min, rotational speed 500 rpm, ethanol/water solvent ratio 25%. RSM coupled with a BBD model was shown to be effective for optimization the solid-liquid extraction of hemp.

Shifting of energy intake towards a later time in the day is associated with an increased risk of obesity in adults. However, there is a lack of data for adolescents. The aim of this study was to investigate adolescents eveningness in energy intake (EV) and its association with anthropometric indicators of nutritional status. This investigation was based on results from the Croatian physical activity in adolescence longitudinal study (CRO-PALS). The cohort included 607 adolescents (50.25% females and 49.75% males) who were assessed at the age of 15/16 and 18/19. A single multi-pass 24-h recall was used as a dietary assessment method, while anthropometric indicators of nutritional status included body mass index (BMI), waist to hip ratio (WHR) and the sum of four skinfolds. The School Health Action, Planning and Evaluation System (SHAPES) questionnaire was used to assess active daily energy expenditure and sedentary behaviors. EV was significantly higher at 18/19 years compared to 15/16 years in whole population (p &lt, 0.01), among male adolescents (p &lt, 0.01), but not among female adolescents (p &gt, 0.05). Although a significant correlation between EV and WHR was found in females at the age of 15/16 (p &lt, 0.01), the results of this study suggest that EV has no or a minor effect on anthropometric indicators of nutritional status in adolescence.

This chapter introduces the application of near-infrared spectroscopy (NIRS) related to the management of detection and quality control of fruit adulteration and contents of macro- and microcomponents in fruits. Nutritive composition of fruits is one of the main facts why nutritionists recommend their daily consumption. NIRS is a nondestructive measurement technique for detection of chemical composition that is used in the management of fruit harvesting, qualitative selection of fruits, and for the prediction of expected content of their constituents. This chapter gives an overview of how effective the application of NIRS can be in the prediction of constituents of fruits, especially in the management of bioactive components, antioxidant activity, and macronutrients in fruits. NIRS efficiency is coupled with multivariate analysis, for example, chemometrics and data modeling. Also is explained how and why are conducted qualitative and quantitative analyses by use of principal component analysis and partial least squares regression. To evaluate the modeling efficiency, some standard parameters are used, and their meaning is also explained.

In recent years, multivariate modelling techniques have been employed with the aim of analysing, describing, and generally interpreting multidimensional data obtained from experiments. The objective of this study was to evaluate the applicability of multiple linear regression, nonlinear regression, piecewise linear regression, and artificial neural network modelling for the prediction of the physical properties (total dissolved solids, extraction yield), and chemical properties (total phenolic content and antioxidant activity) of the aqueous extracts of nine medicinal plants (dandelion, camomile, lavender, lemon balm, marigold, mint, nettle, plantain, and yarrow), prepared in dynamic experiments based on the extraction conditions (time and temperature), and plant species. Results indicated that simple multivariate regression models could be used for prediction of physical and chemical properties of medicinal plants aqueous extracts (the highest R2 were obtained for total phenolic content), while the artificial neural network proved a very effective tool (R2 > 0.9) for simultaneous prediction of both physical and chemical properties of medicinal plants aqueous extracts.

Study explored high voltage electrical discharge (HVED) for recovery of bioactive compounds from Dalmatian sage. HVED parameters were: treatment time (3, 9 min), ethanol content (0, 25, 50%, v/v), voltage for argon (15, 20 kV) and nitrogen (20, 25 kV). Findings were compared to conventional extraction (CE) with assessment of total phenolic content (TPC), antioxidant activity (EPR), NIR spectroscopy, colorimetric parameters, Fourier-transform infrared spectroscopy (FTIR) and differential scanning calorimetry (DSC). Raw sage was analyzed for pesticide residues and heavy metals. The optimal HVED extraction expressed as TPC 57.21 mg GAE/g was obtained with nitrogen/9 min/25 kV/33% of ethanol. Sage is safe for extraction in terms of pesticide residues and heavy metals. HVED electroporation facilitated the extraction of bioactive compounds and produced extracts with high antioxidant potential, thus confirmed HVED as “green” extraction for sustainable functional food production.

The aim of the present study was to determine and compare the levels of acrylamide in different types of bread and bakery products using a LC– MS/MS method, before and after the new European regulation on acrylamide reduction (Commission Regulation (EU) 2017/2158) became valid. Also, one of the aim was to estimate the average exposure to acrylamide through this food category. Of the total of 100 analysed samples, acrylamide content ranged from below the limit of quantification (LOQ) to 237 μg/kg in the period before the application of a new European Regulation, and from

In this work, the emulsification of oil-in-water two-phase systems with three emulsifiers (PEG1500, 6000 and 20000) was studied in a batch macro system and in a continuously operated microfluidic system. The effect of emulsifier concentration, oil concentration and mixing rate on zeta potential and average Feret diameter was analyzed for the macro-batch system, while the effect of emulsifier concentration, oil concentration and total flow rate on zeta potential and average Feret diameter was analyzed for the microfluidic system. The emulsions prepared in batch system were more stable, had smaller droplet diameter but higher values of polydispersity index (PDI) compared to those prepared by continuous method. In both cases, batch and continuous, the use of PEG with higher molecular weight resulted in emulsions with lower zeta potential values. In batch emulsification, all three optimization parameters (emulsifier concentration, oil concentration and mixing rate) had a significant influence on the average Feret diameter and zeta potential (depending on the PEG, used for emulsification), while the emulsifier concentration had the most significant influence on the zeta potential and average Feret diameter of emulsions prepared in the continuous microsystem process.

Composting is a more environmentally friendly and cost-effective alternative to digesting organic waste and turning it into organic fertilizer. It is a biological process in which polymeric waste materials contained in organic waste are biodegraded by fungi and bacteria. Temperature, pH, moisture content, C/N ratio, particle size, nutrient content and oxygen supply all have an impact on the efficiency of the composting process. To achieve optimal composting efficiency, all of these variables and their interactions must be considered. To this end, statistical optimization techniques and mathematical modeling approaches have been developed over the years. In this paper, an overview of optimization and mathematical modeling approaches in the field of composting processes is presented. The advantages and limitations of optimization and mathematical modeling for improving composting processes are also addressed.

The determination of gluten is of critical importance when food screening is intended for special groups such as food ingredient intolerant and allergic persons. Cross-contamination of food that does not contain gluten is also possible in the sales chain. The aim of this study was to determine the applicability of Near-Infrared Spectroscopy (NIRs) for the detection of gluten traces in rice, rice flour, corn flour and corn grits. In the cross-contamination simulation, two types of wheat flour were used. They were added to rice, rice flour, corn flour and corn grits in a range from 5 % to 30 %. Apart from the spectra of pure and contaminated samples, conductivity and total dissolved solids were monitored to determine changes in the samples. NIR spectroscopy was combined with chemometric techniques to determine at which wavelengths a glutenfree fingerprint can be detected. Although experiments were carried out with a NIR instrument that monitors molecular vibrations in the range of λ= 904-1699 nm, the gluten fingerprint was successfully determined, regardless of the type of flour that was added to the rice, rice flour, corn flour and corn grits. All concentrations of the added flours were successfully determined and models were developed to detect the concentrations of the added flours. Even the conductivity showed good prediction potential in gluten determination. Regardless if the investigated samples were contaminated or not, the determination coefficient R2 was over 0.9. Developed models could be used to predict possible wheat flour contamination of any rice or corn product samples or samples prepared for cooking in water.

Artificial neural networks (ANN) were developed for prediction of total dissolved solids, polyphenol content and antioxidant capacity of root vegetables (celery, fennel, carrot, yellow carrot, purple carrot and parsley) extracts prepared from the (i) fresh vegetables, (ii) vegetables dried conventionally at 50 °C and 70 °C, and (iii) the lyophilised vegetables. Two types of solvents were used: organic solvents (acetone mixtures and methanol mixtures) and water. Near-infrared (NIR) spectra were recorded for all samples. Principal Component Analysis (PCA) of the pre-treated spectra using Savitzky-Golay smoothing showed specific grouping of samples in two clusters (1st: extracts prepared using methanol mixtures and water as the solvents; 2nd: extracts prepared using acetone mixtures as the solvents) for all four types of extracts. Furthermore, obtained results showed that the developed ANN models can reliably be used for prediction of total dissolved solids, polyphenol content and antioxidant capacity of dried root vegetable extracts in relation to the recorded NIR spectra.

Histamine fish poisoning is a foodborne illness caused by the consumption of fish products with high histamine content. Although intoxication mechanisms and control strategies are well known, it remains by far the most common cause of seafood-related health problems. Since conventional methods for histamine testing are difficult to implement in high-throughput quality control laboratories, simple and rapid methods for histamine testing are needed to ensure the safety of seafood products in global trade. In this work, the previously developed SERS method for the determination of histamine was tested to determine the influence of matrix effect on the performance of the method and to investigate the ability of different chemometric tools to overcome matrix effect issues. Experiments were performed on bluefin tuna (Thunnus thynnus) and bonito (Sarda sarda) samples exposed to varying levels of microbial activity. Spectral analysis confirmed the significant effect of sample matrix, related to different fish species, as well as the extent of microbial activity on the predictive ability of PLSR models with R2 of best model ranging from 0.722–0.945. Models obtained by ANN processing of factors derived by PCA from the raw spectra of the samples showed excellent prediction of histamine, regardless of fish species and extent of microbial activity (R2 of validation &gt, 0.99).