Eco-Friendly Extraction: A green approach to maximizing bioactive extraction from pumpkin (Curcubita moschata L.)

The research focused on optimizing the accelerated solvent extraction (ASE) of carotenoids and polyphenols from pumpkin powder. The study optimized accelerated solvent extraction (ASE) of carotenoids and polyphenols from pumpkin powder. Using a mix of standard score (SS) and artificial neural network (ANN) methods, the extraction process was fine-tuned. The ANN model assessed extraction parameters' significance, achieving high predictability for total carotenoid content (TCC), total phenolic content (TPC), and free radical scavenging capacity (DPPH and ABTS methods). The analysis highlighted the most effective extraction at 50 % concentration, 120 °C temperature, 5 min duration, and 2 cycles, yielding high carotenoid and phenolic content (TCC 571.49 µg/g, TPC 7.85 mg GAE/g). HPLC-DAD profiles of the optimized ASE extract confirmed major carotenoids and phenolic compounds. Strong correlations were found between bioactive compounds and antioxidant activity, emphasizing potential health benefits.

Enhancing Cookie Formulations with Combined Dehydrated Peach: A Machine Learning Approach for Technological Quality Assessment and Optimization

This study focuses on predicting and optimizing the quality parameters of cookies enriched with dehydrated peach through the application of Support Vector Machine (SVM) and Artificial Neural Network (ANN) models. The purpose of the study is to employ advanced machine learning techniques to understand the intricate relationships between input parameters, such as the presence of dehydrated peach and treatment methods (lyophilization and lyophilization with osmotic pretreatment), and output variables representing various quality aspects of cookies. For each of the 32 outputs, including the parameters of the basic chemical compositions of the cookie samples, selected mineral contents, moisture contents, baking characteristics, color properties, sensorial attributes, and antioxidant properties, separate models were constructed using SVMs and ANNs. Results showcase the efficiency of ANN models in predicting a diverse set of quality parameters with r2 up to 1.000, with SVM models exhibiting slightly higher coefficients of determination for specific variables with r2 reaching 0.981. The sensitivity analysis underscores the pivotal role of dehydrated peach and the positive influence of osmotic pretreatment on specific compositional attributes. Utilizing established Artificial Neural Network models, multi-objective optimization was conducted, revealing optimal formulation and factor values in cookie quality optimization. The optimal quantity of lyophilized peach with osmotic pretreatment for the cookie formulation was identified as 15%.

Modeling and Optimization of Herb-Fortified Fresh Kombucha Cheese: An Artificial Neural Network Approach for Enhancing Quality Characteristics

In this study, an Artificial Neural Network (ANN) model is used to solve the complex task of producing fresh cheese with the desired quality parameters. The study focuses on kombucha fresh cheese samples fortified with ground wild thyme, supercritical fluid extract of wild thyme, ground sage and supercritical fluid extract of sage and optimizes the parameters of chemical composition, antioxidant potential and microbiological profile. The ANN models demonstrate robust generalization capabilities and accurately predict the observed results based on the input parameters. The optimal neural network model (MLP 6-10-16) with 10 neurons provides high r2 values (0.993 for training, 0.992 for testing, and 0.992 for validation cycles). The ANN model identified the optimal sample, a supercritical fluid extract of sage, on the 20th day of storage, showcasing specific favorable process parameters. These parameters encompass dry matter, fat, ash, proteins, water activity, pH, antioxidant potential (TP, DPPH, ABTS, FRAP), and microbiological profile. These findings offer valuable insights into producing fresh cheese efficiently with the desired quality attributes. Moreover, they highlight the effectiveness of the ANN model in optimizing diverse parameters for enhanced product development in the dairy industry.

Mathematical Modeling Approach and Simulation in Food Drying Applications

Recent developments in the branch of food drying involve advancements in the development of mathematical models [...].

Reduction of Alternaria Toxins via the Extrusion Processing of Whole-Grain Red Sorghum Flour

This study delved into the impact of two extrusion processing parameters-screw speed (SS at 400, 600, 800 RPM) and material moisture content in the extruder barrel (M at 12, 15, 18%) at constant feed rate (50 kg/h)-on reducing the content of alternariol (AOH), alternariol monomethyl ether (AME), tenuazonic acid (TeA), and tentoxin (TEN) in whole-grain red sorghum flour. Ultra-performance liquid chromatography combined with a triple-quadrupole mass spectrometer (UPLC-MS/MS) was employed for the determination of Alternaria toxin levels. The extruder die temperature fluctuated between 136 and 177 °C, with die pressures ranging from 0.16 to 6.23 MPa. The specific mechanical energy spanned from 83.5 to 152.3 kWh/t, the torque varied between 88 and 162.8 Nm, and the average material retention time in the barrel ranged from 5.6 to 13 s. The optimal parameters for reducing the concentration of all Alternaria toxins with a satisfactory quality of the sorghum snacks were: SS = 400 RPM, M = 12%, with a reduction of 61.4, 76.4, 12.1, and 50.8% for AOH, AME, TeA, and TEN, respectively.

Fruit and Vegetable Consumption during the COVID-19 Lockdown in Serbia: An Online Survey

The global impact of the COVID-19 pandemic has significantly influenced individuals' dietary choices and food-buying behavior. Present research specifically delves into the alterations in fruit and vegetable (F&V) consumption among consumers in Serbia during the pandemic. The study employs an extended model of the theory of planned behavior (TPB), incorporating the construct of consumers' knowledge to comprehensively explore behavioral changes. Conducted in the first half of 2020, the study engaged 479 participants from Serbia, using an online questionnaire for data collection. Structural equation modelling was employed for a thorough analysis of the gathered responses. The main outcome revealed a noteworthy connection between consumers' knowledge and their attitudes, subjective norms, and intentions regarding F&V consumption. Subjective norms significantly influenced consumers' desire to increase F&V intake during and after the outbreak. These intentions aligned with actual behavior changes, indicating a shift toward higher F&V consumption. Consumer knowledge also notably shaped attitudes and behaviors regarding F&V intake. Notably, the TPB proved valuable in predicting and understanding these dietary shifts during global crises like the pandemic. These insights not only bolster healthier eating habits but also have broader implications for public health. Understanding consumer behavior dynamics during crises like COVID-19 is crucial for crafting effective strategies to promote overall human health and well-being.

Unlocking the Potential of the ANN Optimization in Sweet Potato Varieties Drying Processes

This study explores the unexploited potential of Artificial Neural Network (ANN) optimization techniques in enhancing different drying methods and their influence on the characteristics of various sweet potato varieties. Focusing on the intricate interplay between drying methods and the unique characteristics of white, pink, orange, and purple sweet potatoes, the presented experimental study indicates the impact of ANN-driven optimization on food-related characteristics such as color, phenols content, biological activities (antioxidant, antimicrobial, anti-hyperglycemic, and anti-inflammatory), chemical, and mineral contents. The results unveil significant variations in drying method efficacy across different sweet potato types, underscoring the need for tailored optimization strategies. Specifically, purple sweet potatoes emerge as robust carriers of phenolic compounds, showcasing superior antioxidant activities. Furthermore, this study reveals the optimized parameters of dried sweet potato, such as total phenols content of 1677.76 mg/100 g and anti-inflammatory activity of 8.93%, anti-hyperglycemic activity of 24.42%. The upgraded antioxidant capability is presented through DPPH●, ABTS●+, RP, and SoA assays with values of 1500.56, 10,083.37, 3130.81, and 22,753.97 μg TE/100 g, respectively. Additionally, the moisture content in the lyophilized sample reached a minimum of 2.97%, holding favorable chemical and mineral contents. The utilization of ANN optimization proves instrumental in interpreting complex interactions and unlocking efficiencies in sweet potato drying processes, thereby contributing valuable insights to food science and technology.

Prototype of an Innovative Vacuum Dryer with an Ejector System: Comparative Drying Analysis with a Vacuum Dryer with a Vacuum Pump on Selected Fruits

The following article describes new research about the design, construction and installation of the new prototype of a vacuum dryer with an ejector system. Moreover, the testing of this new prototype involved comparing the qualities of fruit dried in a vacuum drier with an ejector system to fruit dried in a convectional vacuum drier. The data obtained were then analyzed and presented. Due to their economic relevance and highly valuable nutritional value and sensory properties, sour cherries and apricots have been chosen to be the subjects for the testing. The most appropriate quality indicators for analyzing were moisture content, aw value, share and penetration force, total phenol, flavonoid and anthocyanin content and antioxidant activity (FRAP, DPPH and ABTS test). The main results of this study were achieved by designing, constructing, installing and testing the usage of the innovative prototype of a vacuum dryer with an ejector system in the laboratory of the Technology of fruit and vegetable products of the Faculty of Technology Novi Sad, University of Novi Sad. Based on our analyses of the obtained data, it was concluded that vacuum dryer with an ejector system are similar to vacuum dryer with a vacuum pump in terms of all tested physical, chemical and biological properties of dried samples. We observed similarities in some of the most important parameters, including product safety and quality, such as the aw value and the total phenol content, respectively. For example, in dried sour cherry, the aw values ranged from 0.250 to 0.521 with the vacuum pump and from 0.232 to 0.417 with the ejector system; the total phenol content ranged from 2322 to 2765 mg GAE/100 g DW with the vacuum pump and from 2327 to 2617 mg GAE/100 g DW with the ejector system. In dried apricot, the aw ranged from 0.176 to 0.405 with the vacuum pump and from 0.166 to 0.313 with the ejector system; total phenol content ranged from 392 to 439 mg GAE/100 g DW with the vacuum pump and from 378 to 428 mg GAE/100 g DW with the ejector system.

Thermal Characterisation and Isoconversional Kinetic Analysis of Osmotically Dried Pork Meat Proteins Longissimus dorsi

The kinetic properties and thermal characteristics of fresh pork meat proteins (Longissimus dorsi), as well as osmotically dehydrated meat proteins, were investigated using differential scanning calorimetry. Two isoconversional kinetical methods, namely the differential Friedman and integral Ortega methods, were employed to analyze the data. The obtained kinetic triplet, activation energy, pre-exponential factor, and extent of conversion, has been discussed. The resulting activation energy for proteins of fresh meat ranges between 751 kJ·mol-1 for myosin, 152 kJ·mol-1 for collagen and sarcoplasmic proteins, and 331 kJ·mol-1 for actin at a conversion degree of 0.1 to 0.9. For osmotically dried pork meat proteins, the values range from 307 kJ·mol-1 for myosin 272 kJ·mol-1 for collagen and sarcoplasmic proteins, and 334.83 kJ·mol-1 for actin at a conversion degree from 0.1 to 0.9. The proteins of the dry meat obtained by osmotic dehydration in molasses could be described as partly unfolded as they retain the characteristic protein denaturation transition. Concerning the decrease in enthalpies of proteins denaturation, thermodynamic destabilization of dried meat proteins occurred. On the contrary, dried meat proteins were thermally stabilized with respect to increase in the temperatures of denaturation. Knowledge of the nature of meat protein denaturation of each kind of meat product is one of the necessary tools for developing the technology of meat product processing and to achieve desired quality and nutritional value. The kinetic analysis of meat protein denaturation is appropriate because protein denaturation gives rise to changes in meat texture during processing and directly affects the quality of product.

Investigation of oxidative characteristics, fatty acid composition and bioactive compounds content in cold pressed oils of sunflower grown in Serbia and Argentina

Background

In this work, the chemical composition analysis was performed for cold pressed oils obtained from the 15 sunflower hybrids grown in Serbia and Argentina, as well as the determination of their oxidative quality. The fatty acid composition and bioactive compounds including total tocopherols, phenols, carotenoids, and chlorophyll contents were investigated. The oxidation products were monitored through the peroxide value (PV), anisidine value (AnV), conjugated dienes (CD) and conjugated trienes (CT) content, and total oxidation index (TOTOX) under accelerated oxidation conditions by the oven method.

Results

Linoleic acid was the most abundant fatty acid in investigated oil samples, followed by oleic and palmitic acids. The mean contents of total tocopherols, phenols, carotenoids, and chlorophyll were 518.24, 9.42, 7.54 and 0.99 mg/kg, respectively. In order to obtain an overview of sample variations according to the tested parameters Principal Component Analysis (PCA) was applied.

Conclusion

PCA indicated that phenols, chlorophyll, linoleic and oleic acid were the most effective variables for the differentiation of sunflower hybrids grown in Serbia and Argentina. Furthermore, based on the fatty acid composition and bioactive compounds content in the oils, a new Artificial Neural Network (ANN) model was developed to predict the oxidative stability parameters of cold pressed sunflower oil.

Investigation of oxidative characteristics, fatty acid composition and bioactive compounds content in cold pressed oils of sunflower grown in Serbia and Argentina

BACKGROUND

In this work, the chemical composition analysis was performed for cold pressed oils obtained from the 15 sunflower hybrids grown in Serbia and Argentina, as well as the determination of their oxidative quality. The fatty acid composition and bioactive compounds including total tocopherols, phenols, carotenoids, and chlorophyll contents were investigated. The oxidation products were monitored through the peroxide value (PV), anisidine value (AnV), conjugated dienes (CD) and conjugated trienes (CT) content, and total oxidation index (TOTOX) under accelerated oxidation conditions by the oven method.

RESULTS

Linoleic acid was the most abundant fatty acid in investigated oil samples, followed by oleic and palmitic acids. The mean contents of total tocopherols, phenols, carotenoids, and chlorophyll were 518.24, 9.42, 7.54 and 0.99 mg/kg, respectively. In order to obtain an overview of sample variations according to the tested parameters Principal Component Analysis (PCA) was applied.

CONCLUSION

PCA indicated that phenols, chlorophyll, linoleic and oleic acid were the most effective variables for the differentiation of sunflower hybrids grown in Serbia and Argentina. Furthermore, based on the fatty acid composition and bioactive compounds content in the oils, a new Artificial Neural Network (ANN) model was developed to predict the oxidative stability parameters of cold pressed sunflower oil.

Comparison of the Efficiency of Selected Disinfectants against Planktonic and Biofilm Populations of Escherichia coli and Staphylococcus aureus

The aim of this study is to compare the efficacy of selected food disinfectants on planktonic populations of Staphylococcus aureus and Escherichia coli and on the same microorganisms (MOs) incorporated in a biofilm. Two disinfectants were used for treatment: peracetic acid-based disinfectant (P) and benzalkonium chloride-based disinfectant (D). Testing of their efficacy on the selected MO populations was performed using a quantitative suspension test. The standard colony counting procedure was used to determine their efficacy on bacterial suspensions in tryptone soy agar (TSA). The germicidal effect (GE) of the disinfectants was determined based on the decimal reduction ratio. For both MOs, 100% GE was achieved at the lowest concentration (0.1%) and after the shortest exposure time (5 min). Biofilm production was confirmed with a crystal violet test on microtitre plates. Both E. coli and S. aureus showed strong biofilm production at 25 °C with E. coli showing significantly higher adherence capacity. Both disinfectants show a significantly weaker GE on 48 h biofilms compared to the GE observed after application of the same concentrations on planktonic cells of the same MOs. Complete destruction of the viable cells of the biofilms was observed after 5 min of exposure to the highest concentration tested (2%) for both disinfectants and MOs tested. The anti-quorum sensing activity (anti-QS) of disinfectants P and D was determined via a qualitative disc diffusion method applied to the biosensor bacterial strain Chromobacterium violaceum CV026. The results obtained indicate that the disinfectants studied have no anti-QS effect. The inhibition zones around the disc therefore only represent their antimicrobial effect.

Sunflower Oil Winterization Using the Cellulose-Based Filtration Aid-Investigation of Oil Quality during Industrial Filtration Probe

Waxes, phospholipids, free fatty acids, peroxides, aldehydes, soap, trace metals and moisture present in crude sunflower oil have a negative effect on the oil quality and are, therefore, removed during the refining process. Waxes crystallizing at low temperatures are removed during winterization by cooling and filtration. Waxes have poor filtration characteristics and an industrial filtration process must be enhanced by the use of filtration aids, which improve filter cake structure and properties, and consequently prolong the filtration cycle. Today, traditional filtration aids (diatomite, perlite, etc.) being used in the industry are frequently replaced by cellulose-based aids. The aim of this study is to examine the effect of oil filtration assisted by two cellulose-based filtration aids on the chemical parameters (wax, moisture, phospholipids, soaps, and fatty acids), oil transparency, carotenoids, and Fe and Cu content of sunflower oil obtained in an industrial horizontal pressure leaf filter. In order to investigate the mentioned parameters, the following techniques were used: gravimetric (waxes and moisture content), spectrophotometric (phospholipids and carotenoid content and oil transparency), volumetric (soaps and free fatty acids content) as well as inductively coupled plasma mass spectrometry (ICP-MS) for Fe and Cu content. An artificial neural network model (ANN) was employed for the prediction of removal efficiency based on the chemical quality, oil transparency, Fe and Cu content in oils before filtration, as well as filtration aid quantity and filtration time. Cellulose-based filtration aids had multiple beneficial effects; on average, 99.20% of waxes, 74.88% of phospholipids, 100% of soap, 7.99% of carotenoids, 16.39% of Fe and 18.33% of Cu were removed.

Artificial Neural Network Prediction of Antiadhesion and Antibiofilm-Forming Effects of Antimicrobial Active Mushroom Extracts on Food-Borne Pathogens

The problem of microbial biofilms has come to the fore alongside food, pharmaceutical, and healthcare industrialization. The development of new antibiofilm products has become urgent, but it includes bioprospecting and is time and money-consuming. Contemporary efforts are directed at the pursuit of effective compounds of natural origin, also known as "green" agents. Mushrooms appear to be a possible new source of antibiofilm compounds, as has been demonstrated recently. The existing modeling methods are directed toward predicting bacterial biofilm formation, not in the presence of antibiofilm materials. Moreover, the modeling is almost exclusively targeted at biofilms in healthcare, while modeling related to the food industry remains under-researched. The present study applied an Artificial Neural Network (ANN) model to analyze the anti-adhesion and anti-biofilm-forming effects of 40 extracts from 20 mushroom species against two very important food-borne bacterial species for food and food-related industries-Listeria monocytogenes and Salmonella enteritidis. The models developed in this study exhibited high prediction quality, as indicated by high r² values during the training cycle. The best fit between the modeled and measured values was observed for the inhibition of adhesion. This study provides a valuable contribution to the field, supporting industrial settings during the initial stage of biofilm formation, when these communities are the most vulnerable, and promoting innovative and improved safety management.

Prediction of the Impact of Land Use and Soil Type on Concentrations of Heavy Metals and Phthalates in Soil Based on Model Simulation

The main objective of this study is to determine the possibility of predicting the impact of land use and soil type on concentrations of heavy metals (HMs) and phthalates (PAEs) in soil based on an artificial neural network model (ANN). Qualitative analysis of HMs was performed with inductively coupled plasma-optical emission spectrometry (ICP/OES) and Direct Mercury Analyzer. Determination of PAEs was performed with gas chromatography (GC) coupled with a single quadrupole mass spectrometry (MS). An ANN, based on the Broyden-Fletcher-Goldfarb-Shanno (BFGS) iterative algorithm, for the prediction of HM and PAE concentrations, based on land use and soil type parameters, showed good prediction capabilities (the coefficient of determination (r²) values during the training cycle for HM concentration variables were 0.895, 0.927, 0.885, 0.813, 0.883, 0.917, 0.931, and 0.883, respectively, and for PAEs, the concentration variables were 0.950, 0.974, 0.958, 0.974, and 0.943, respectively). The results of this study indicate that HM and PAE concentrations, based on land use and soil type, can be predicted using ANN.

Analysis of antioxidant potential of fruit and vegetable juices available in Serbian markets

Antioxidants in fruit and vegetable juices have become increasingly popular because of their potential health benefits. Nowadays, juice mixes made from berries present frequent consumer choices, due to their nutritive value and high content of bioactive compounds. Commercial fruit and vegetable juices available in Serbian markets (n = 32) were analyzed for the physicochemical properties, chemical composition, and antioxidant activity. Relative antioxidant capacity index was used for the ranking of the juices according to antioxidant capacity, while antioxidant effectiveness of phenolic compounds contained in juice samples was investigated depending on phenolic antioxidant coefficients. Principal component analysis was applied to study the data structure. In addition, a multi-layer perceptron model was used for modeling an artificial neural network model (ANN) for prediction antioxidant activity (DPPH, reducing power, and ABTS) based on total phenolic, total pigments, and vitamin C content. The obtained ANN showed good prediction capabilities (the r2 values during training cycle for output variables were 0.942). Phenolic, pigments, and vitamin C contents showed a positive correlation with the investigated antioxidant activity. The consumption of commercial berry fruit juices available in Serbian markets may deliver great health benefits through the supply of natural antioxidants.

A comprehensive approach to chitosan-gelatine edible coating with β-cyclodextrin/lemongrass essential oil inclusion complex - Characterization and food application

Biopolymer-based films present an ideal matrix for the incorporation of active substances such as antimicrobial agents, giving active packaging a framework of green chemistry and a step forward in food packaging technology. The chitosan-gelatine active coating has been prepared using lemongrass oil as an antimicrobial compound applying a different approach. Instead of surfactants, to achieve compatibilization of compounds, β-cyclodextrin was used to encapsulate lemongrass oil. The antimicrobial effect was assessed using the dip-coating method on freshly harvested cherry tomatoes artificially contaminated by Penicillium aurantiogriseum during 20 days of cold storage. According to the evaluation of the antimicrobial effect of coating formulation on cherry tomato samples, which was mathematically assessed by predictive kinetic models and digital imaging, the applied coating formulation was found to be very effective since the development of fungal contamination for active-coated samples was observed for 20 days.

Green Infrastructure Designed through Nature-Based Solutions for Sustainable Urban Development

With the goal of enhancing the quality of the environment, urban green infrastructure (UGI) is an essential element in sustainable cities, and nature-based solutions (NBS) are being carried out as new infrastructure solutions that increase the resilience of cities. In this research, the method of theoretical analysis and the content analysis as the basic fact-gathering technique was applied to answer to following questions: What are the hindrances and bottlenecks in implementing NBS? Are the current decision-making mechanisms helping NBS get in route to shape cities? Is there any binding policy in practice that promotes NBS? In Belgrade is planned Type 3 of the degree of intervention/level and engineering type-Creation and new ecosystem management in the classifications of intensive urban green space management; urban planning strategies; urban water management; ecological restoration of degraded terrestrial ecosystems; and restoration and creation of semi-natural water bodies and hydrographic networks. In the future, it is essential to implement policies and incentives on national, regional, and local scales that help encourage the usage of NBS in the development of urban infrastructure.

Screening of Antifungal Activity of Essential Oils in Controlling Biocontamination of Historical Papers in Archives

The main challenge in controlling the microbiological contamination of historical paper is finding an adequate method that includes the use of cost-effective, harmless, and non-toxic biocides whose effectiveness is maintained over time and without adverse effects on cultural heritage and human health. Therefore, this study demonstrated the possibility of using a non-invasive method of historical paper conservation based on plant essential oils (EOs) application. Evaluation of antimicrobial effects of different EOs (lemongrass, oregano, rosemary, peppermint, and eucalyptus) was conducted against Cladosporium cladosporoides, Aspergillus fumigatus, and Penicillium chrysogenum, which are commonly found on archive papers. Using a mixture of oregano, lemongrass and peppermint in ratio 1:1:1, the lower minimal inhibition concentration (0.78%) and better efficiency during a vapour test at the highest tested distance (5.5 cm) compared with individual EOs was proven. At the final step, this EOs mixture was used in the in situ conservation of historical paper samples obtained from the Archives of Vojvodina. According to the SEM imaging, the applied EOs mixture demonstrates complete efficiency in the inhibition of fungi colonization of archive papers, since fungal growth was not observed on samples, unlike the control samples.

Formation of Predictive-Based Models for Monitoring the Microbiological Quality of Beef Meat Processed for Fast-Food Restaurants

Consumption of raw or undercooked meat is responsible for 2.3 million foodborne illnesses yearly in Europe alone. The greater part of this illness is associated with beef meat, which is used in many traditional dishes across the world. Beneath the low microbiological quality of beef lies (pathogenic) bacterial contamination during primary production as well as inadequate hygiene operations along the farm-to-fork chain. Therefore, this study seeks to understand the microbiological quality pathway of minced beef processed for fast-food restaurants over three years using an artificial neural network (ANN) system. This simultaneous approach provided adequate precision for the prediction of a microbiological profile of minced beef meat as one of the easily spoiled products with a short shelf life. For the first time, an ANN model was developed to predict the microbiological profile of beef minced meat in fast-food restaurants according to meat and storage temperatures, butcher identification, and working shift. Predictive challenges were identified and included standardized microbiological analyses that are recommended for freshly processed meat. The obtained predictive models (an overall r² of 0.867 during the training cycle) can serve as a source of data and help for the scientific community and food safety authorities to identify specific monitoring and research needs.

Variability in Biological Activities of Satureja montana Subsp. montana and Subsp. variegata Based on Different Extraction Methods

Winter savory (Satureja montana L.) is a well-known spice and medicinal plant with a wide range of activities and applications. Two subspecies of S. montana, subsp. montana and subsp. variegata, were used for the preparation of seven different extracts: steam distillation (essential oil (EO) and hydrolate (HY)), subcritical water (SWE), ultrasound-assisted (UAE-MeOH and UAE-H₂O), and microwave-assisted (MAE-MeOH and MAE-H₂O) extraction. The obtained EOs, HYs, and extracts were used for an in vitro evaluation of the antioxidant activity (DPPH, ABTS, reducing power, and superoxide anion methods) and in vitro antimicrobial activity against Bacillus cereus, Staphylococcus aureus, Enterococcus faecalis, Escherichia coli, Salmonella Typhimurium, Saccharomyces cerevisiae, and Candida albicans. The antimicrobial screening was conducted using disk-diffusion assessment, minimal inhibitory concentration, time–kill kinetics modeling, and pharmacodynamic study of the biocide effect. The total phenolic content (TPC) was highest in EO, followed by SWE, MAE, and UAE, and the lowest was in HY. The highest antimicrobial activity shows EO and SWE for both varieties, while different UAE and MAE extracts have not exhibited antimicrobial activity. The natural antimicrobials in the S. montana extract samples obtained by green extraction methods, indicated the possibility of ecologically and economically better solutions for future in vivo application of the selected plant subspecies.

Development of a Novel Rice-Based Snack Enriched with Chicory Root: Physicochemical and Sensory Properties

A novel rice-based snack enriched with chicory root flour (CRF) was developed by twin-screw extrusion. Chicory (Cichorium intybus L.) is one of the promising medicinal plants for the development of innovative food and may be considered a functional food ingredient. Central composite design (CCD) was employed to generate snack formulations by varying feed moisture (M, 16.3–22.5%), screw speed (SS, 500–900 rpm) and CRF content (20–40%). The optimization according to artificial neural network modeling and a genetic algorithm was applied to define optimal process conditions (17.6% moisture, 820 rpm and 24.1% of CRF) for obtaining the product with the highest expansion (3.34), crispiness (3.22 × 10−3), volume (2040 m3), degree of gelatinization (69.70%) and good color properties. Bulk density (110.33 g/L), density (250 kg/m3), and hardness (98.74 N) resulted in low values for the optimal sample. The descriptive sensory analysis evaluated low hardness and bitterness, with high crispiness for the optimal extrudate. This study points to the possibility of a novel chicory enriched extrudate production with desirable physicochemical and sensory properties.

Polyphenolics and Chemical Profiles of Domestic Norwegian Apple (Malus × domestica Borkh.) Cultivars

Using modern analytical techniques, a comprehensive study of the chemical composition of fruits from apple cultivars grown in Western Norway during 2019 and 2020 was done. Metals, sugars, organic acids, antioxidant tests, and polyphenol content have been observed. In all investigated samples, the most dominant sugars were glucose, fructose, and sucrose. Among 11 tested organic acids, the dominant was malic acid, followed by citric and maleic acid. The most common metal was potassium, followed by magnesium and zinc. The quantification of polyphenols showed that among the 11 quantified polyphenols, chlorogenic acid, quercetin 3-O-rhamnoside, quercetin 3-O-glucoside, quercetin, and phlorizin were the most abundant. A detailed study of the polyphenolic profile of nine investigated apple samples provided 30 identified polyphenolic compounds from the class of hydroxybenzoic and hydroxycinnamic acids, flavonoids, and dihydrochalcones. In addition to the identified 3-O-caffeoylquinic acid, its two isomers of 5-O-caffeoylquinic acid and three esters were also found. Present polyphenols of the tested apples provided significant data on the quality of Norwegian apples, and they contribute to the distinguishing of these apple samples.

Celery Root Phenols Content, Antioxidant Capacities and Their Correlations after Osmotic Dehydration in Molasses

The osmotic dehydration (OD) of celery root in sugar beet molasses was studied at three temperatures (20, 35, and 50 °C) and three immersion periods (1, 3, and 5 h) in order to examine the changes in antioxidant potential and phenolic profile of celery root throughout the process. The antioxidant capacity (AOC) of dehydrated samples was evaluated by spectrophotometric and polarographic assays, the total phenolic content by the Folin-Ciocalteu method, and the individual phenolic compounds by HPLC-DAD. As a result of OD in molasses, the AOC and phenols content in samples increased proportionally to the augmentation of temperature and the immersion time. Vanillic acid, syringic acid, and catechin were detected in dehydrated samples as a result of transfer from molasses. Compared to fresh celery root, the content of identified phenols in osmodehydrated samples was improved from 1.5 to 6.2 times. Strong correlations between applied assays were obtained, except for the DPPH. Based on the correlation analysis chlorogenic acid, gallic acid, chrysin, catechin, and kaempferol showed the greatest contribution to the overall AOC of osmodehydrated celery root. Molasses, an agro-industrial waste from sugar production, could be valorized as a valuable osmotic solution.

Polyphenolic and Chemical Profiles of Honey From the Tara Mountain in Serbia

This study presents a detailed characterization of 27 honey samples from the Tara Mountain region in Serbia using different comprehensive techniques and methods. The types of the honey samples were defined as monofloral (4 samples), honeydew (5 samples) and polyfloral (18 samples) honey based on determined polyphenol content, antioxidant activity, electrical conductivity and melissopalynological analyses. Physicochemical parameters such as pH (4.13–4.94), diastase activity (24.20–41.70 DN), acidity (14.60–29.70 meq/kg), content of 5-(hydroxymethyl)furfural (in range below 5, up to 16.90 mg/kg), sucrose (0.20–3.90 g/100 g), and moisture content (15.01–19.23%) confirmed the required quality of the honey samples. Sensory analysis revealed honey characteristics favorable to consumers. Analyses of 19 phenolic compounds using ultra-high-performance liquid chromatography with a diode-array detection and triple quadrupole mass spectrometry (UHPLC-DAD-MS/MS) revealed six phenolic acids and 13 other compounds from the group of flavonoids and their glycosides. In all the samples the highest content was determined for p-coumaric acid, followed by caffeic acid and pinocembrin. Besides total phenolic content and radical scavenging activity, antimicrobial activity was also examined. Most honey samples showed bactericidal activity against Staphylococcus aureus and bacteriostatic activity against Escherichia coli, while none of the honey samples inhibited the growth of Candida albicans. Chemometric analyses were applied for an in-depth study of the results to further evaluate the characteristics of the honey samples studied. Principal component analysis (PCA) was used for assessing the differences in physicochemical parameters, polyphenols content and antioxidant capacity between honey samples. The unrooted cluster tree was used to group the samples based on the melissopalynological analyses.

Utilization of Sugar Beet Pulp as Biosorbent for Molassigenic Metal Ions: Kinetic Study of Batch Biosorption

The sugar industry is facing problems with high amount of molassigenic metal ions remained after the purification step in sugar juice. In this investigation the application of unmodified sugar beet pulp as a weak monofunctional cation-exchange biosorbent for molassigenic metal ions (Na+, K+ and Ca2+) removal from the alkalized sugar juice was studied. The batch biosorption experiments were performed at temperature (70 °C) and pH (10.5) of alkalized sugar juice similar to industrial conditions. The highest removal efficiency was noticed for divalent Ca2+ (30.2%), while monovalent Na+ and K+ ions were removed with 10.9 and 9.1% efficiency, respectively. Biosorption equilibrium was established in 90 min for all tested metals. Sugar beet pulp characterization from the perspective of cation-exhange material was conducted. The structure of the biosorbent and an insight of the functional groups were also characterized by scanning electron microscopy and Fourier transform infrared spectroscopy. The biosorption data were analyzed using four non-linear kinetic (pseudo-first order, pseudo-second order and Elovich) and diffusion models (Weber-Morris). The time course data of biosorption processes fitted well to the pseudo-first and the pseudo-second-order kinetic models indicating ion-exchange and chemisorption as dominant mechanisms for metal ions removal from the alkalized juice. HNO3 as a desorption reagent showed the highest average molassigenic metal ions desorption efficiency (54.4%). Utilization of sugar beet biomass as cation-exchange material imposes as a potential solution for more successful sugar juice purification.

Green Techniques for Preparation of Red Beetroot Extracts with Enhanced Biological Potential

Red beetroot is well known for its high proportion of betalains, with great potential as functional food ingredients due to their health-promoting properties. The objective of this study was to investigate the influence of processing techniques such as Soxhlet, cold, ultrasound and supercritical fluid extraction on the betalains content and its antioxidant, anti-inflammatory and antihyperglycemic activities. Whilst Soxhlet extraction with water has provided the highest yield, the highest content of total phenolics was found in an extract prepared using Soxhlet extraction with 50% ethanol. Amongst eight phenolic compounds detected in the extracts, protocatechuic acid was the most abundant. The concentrations of total phenolics ranged from 12.09 mg/g (ultrasound extraction with 30% methanol) to 18.60 mg/g (Soxhlet extraction with 50% ethanol). The highest anti-inflammatory activity was observed for cold extraction with 50% methanol extract. The high radical scavenging activity of supercritical fluid extracts could be a consequence of nonphenolic compounds. The chemometrics approach was further used to analyse the results to find the “greenest” method for further possible application in the processing of beetroot in the food and/or pharmaceutical industry. According to the standard score, the best extraction method was determined to be Soxhlet extraction with 50% ethanol.

Chemical Composition, Antioxidant, and Antimicrobial Activity of Dracocephalum moldavica L. Essential Oil and Hydrolate

Steam distillation was used for the isolation of Dracocephalum moldavica L. (Moldavian dragonhead) essential oil (DMEO). This aromatic herbaceous plant is widespread across the Northern Hemisphere regions and has been utilized in health-improving studies and applications. In addition to the DMEO, the hydrolate (DMH), a byproduct of the distillation process, was also collected. The DMEO and DMH were analyzed and compared in terms of their chemical composition, as well as their in vitro biological activities. The main component in DMEO was geranyl acetate, while geranial was dominant in DMH. The DMEO demonstrated better antioxidant and antimicrobial activities compared with the DMH against Staphylococcus aureus, Escherichia coli, Salmonella Typhimurium, and Listeria monocytogenes, which represent sources of food-borne illness at the global level. The DMEO and DMH show promise as antioxidant and antimicrobial additives to various products.

Recovery of biologically active compounds from stinging nettle leaves part I: Supercritical carbon dioxide extraction

Stinging nettle is annual plant from Urticaceae family used as food and medicine. Due to the nonsufficient data, this work aimed to isolate the bioactive compounds from the stinging nettle leaves by supercritical carbon dioxide. Extracts were analyzed and assessed for antioxidant and cytotoxic activities. Main fatty acids were α-linolenic (31.06-58.42 mg/g E), palmitic (9.17-13.12 mg/g E), and linoleic (10.93-16.51 mg/g E) acids. Chlorophylls (33.00-7365.11 mg/100 g E) and carotenoids (166.88-722.62 mg/100 g E) were also found in all samples. Four empirical kinetic equations were effectively utilized for kinetic modeling of supercritical fluid extraction. As per proper statistical features, empirical models show good concurrence with experimental data. The numerical modeling of a process is gainful to foresee the process conduct and furthermore extend the methodology from laboratory to industrial scales. The principal component analysis was used to visualize the fatty acids profile, antioxidant capacity, and cytotoxic activity of extract.

Comparative Study of the Essential Oil and Hydrosol Composition of Sweet Wormwood (Artemisia annua L.) from Serbia

The most abundant volatile compounds of sweet wormwood (Artemisia annua L.) essential oil were artemisia ketone (25.4 %) and trans-caryophyllene (10.2 %), followed by 1,8-cineole, camphor, germacrene D and β-selinene. The major volatile compounds in the hydrosol were camphor (25.1 %), 1,8-cineole (20.5 %) and artemisia ketone (10.7 %), followed by trans-pinocarveol and yomogi alcohol. Tested essential oil was rich in oxygenated monoterpenes and sesquiterpene hydrocarbons, while the former were identified as the major class of volatile compounds in the hydrosol, due to higher water solubility. Classification of all sweet wormwood chemotypes, according to essential oil composition, in available literature (17 studies and 61 accessions) could be done according to four chemotypes: artemisia ketone+artemisia alcohol (most abundant), artemisia ketone, camphor and nonspecific chemotype. According to this classification, essential oil of sweet wormwood from this study belongs to artemisia ketone (content varied between 22.1 and 55.8 %). Bearing in mind that hydrosols are a by-product of industrial production of essential oils, and the fact that sweet wormwood hydrosol has high contents of camphor, 1,8-cineole and artemisia ketone, there is a great potential for the use of this aromatic plant primary processing waste product as a water replacement in cosmetic industry, beverages flavoring, for food preservation, as well as in post-harvest pre-storage treatments in organic agriculture.

Diversity of amino acids composition in cereals

The quality of protein is based on their amino acid composition, especially on the content and availability of essential amino acids. Cereals are important sources of protein for human nutrition, but are limited in the amounts of essential amino acids, notably lysine. The aim of this study was to analyze the chemical composition and amino acid profiles of different cereals that are important for nutritional purposes in human diet. The content of protein, moisture and crude fat in cereals varied significantly from 7.83 to 13.22%, 11.45 to 13.80%, and from 1.67 to 6.35%, respectively. The obtained results showed that oat had the highest contents of crude protein (13.22%), crude fat (6.35%) and crude cellulose (9.42%) compared to other cereals. Significant (p < 0.05) variation existed in the content of essential and nonessential amino acids among samples with the highest level in oat and wheat. Essential amino acids accounted for one-third of the total amino acids in the tested cereals. Glutamic acid was found to be the most abundant amino acid. It could be concluded that the amino acid composition of oat is the most favorable among cereals due to its high protein content and the content of lysine which can be found in limited amounts in most of the cereals.

Modeling fruit and vegetable consumption in Serbia

Although regular intake of fruits and vegetables has an essential role in a healthy diet and well-being, a majority of consumers in Serbia have a suboptimal intake of these groceries. To understand the main determinants of this unsatisfactory situation, the study tested an extended model of the theory of planned behavior intending to suggest necessary steps for improving fruits and vegetables daily intake. This theory, extended for the role of knowledge, was tested using structural equation modeling. Fit indices confirmed the utility of this extended model of the theory of planned behavior in explaining consumers' behavior as well as the mediating role of behavioral intentions. Serbia, as one of the central developing countries in the Balkans, was chosen to test the model with the possibility of applying it to other developing countries facing malnutrition. Data were collected in North Serbia, through an online survey (n=688). Despite consumers' high awareness of fruits and vegetables' beneficial health effects, the influence of consumers' knowledge only is not sufficient to trigger behavioral changes. Consumers' intentions and behavior should be influenced indirectly, by changing their attitudes and subjective norms. All custom-made activities promoting a higher fruit and vegetable intake should consider the present findings to achieve a bigger effect on behavioral changes among consumers.

Modeling fruit and vegetable consumption in Serbia

Although regular intake of fruits and vegetables has an essential role in a healthy diet and well-being, a majority of consumers in Serbia have a suboptimal intake of these groceries. To understand the main determinants of this unsatisfactory situation, the study tested an extended model of the theory of planned behavior intending to suggest necessary steps for improving fruits and vegetables daily intake. This theory, extended for the role of knowledge, was tested using structural equation modeling. Fit indices confirmed the utility of this extended model of the theory of planned behavior in explaining consumers' behavior as well as the mediating role of behavioral intentions. Serbia, as one of the central developing countries in the Balkans, was chosen to test the model with the possibility of applying it to other developing countries facing malnutrition. Data were collected in North Serbia, through an online survey (n=688). Despite consumers' high awareness of fruits and vegetables' beneficial health effects, the influence of consumers' knowledge only is not sufficient to trigger behavioral changes. Consumers' intentions and behavior should be influenced indirectly, by changing their attitudes and subjective norms. All custom-made activities promoting a higher fruit and vegetable intake should consider the present findings to achieve a bigger effect on behavioral changes among consumers.

Diversity of amino acids composition in cereals

The quality of protein is based on their amino acid composition, especially on the content and availability of essential amino acids. Cereals are important sources of protein for human nutrition, but are limited in the amounts of essential amino acids, notably lysine. The aim of this study was to analyze the chemical composition and amino acid profiles of different cereals that are important for nutritional purposes in human diet. The content of protein, moisture and crude fat in cereals varied significantly from 7.83 to 13.22%, 11.45 to 13.80%, and from 1.67 to 6.35%, respectively. The obtained results showed that oat had the highest contents of crude protein (13.22%), crude fat (6.35%) and crude cellulose (9.42%) compared to other cereals. Significant (p < 0.05) variation existed in the content of essential and nonessential amino acids among samples with the highest level in oat and wheat. Essential amino acids accounted for one-third of the total amino acids in the tested cereals. Glutamic acid was found to be the most abundant amino acid. It could be concluded that the amino acid composition of oat is the most favorable among cereals due to its high protein content and the content of lysine which can be found in limited amounts in most of the cereals.

Correspondence analysis of fruit and vegetable waste among consumers in Vojvodina

Fresh fruit and vegetables contribute to almost 50% of food wasted by households in the EU. To understand the main reasons for wasteful behaviour among the consumers in Vojvodina (Serbia) cross-sectional data were collected through a self-administrated online survey of 711 correspondents. The questions presented in the study focused on the method of preserving the fruits and vegetables in domestic conditions before their consumption. Self-estimation of wasteful behaviour in the household and socio-demographic factors, including the respondents' self-estimated health status, were analysed using the correspondence analysis. The results showed that the first two dimensions explained for 95.06% of the total per cent of inertia (statistically significant at p<0.001 level) indicating the strong relationship among variables. According to the survey, the fruit and vegetables are most likely used fresh (54.43% and 48.95%, respectively), whereas the use of fruits and vegetables after storage in a refrigerator (17.16% and 29.96%, respectively) or in a deep freezer (2.81% or 5.20%, respectively) is significantly lower. Most of the survey correspondents claimed that they regularly discard the fruits and vegetables to waste (53.02% and 39.66%, respectively). The results pointed out that the storage and the amounts of discarded waste are affected by gender, income, and health conditions of the respondents'. Also, the study emphasized the growing need for educating and better planning of fresh fruit and vegetable management to reduce waste production.

Correspondence analysis of fruit and vegetable waste among consumers in Vojvodina

Fresh fruit and vegetables contribute to almost 50% of food wasted by households in the EU. To understand the main reasons for wasteful behaviour among the consumers in Vojvodina (Serbia) cross-sectional data were collected through a self-administrated online survey of 711 correspondents. The questions presented in the study focused on the method of preserving the fruits and vegetables in domestic conditions before their consumption. Self-estimation of wasteful behaviour in the household and socio-demographic factors, including the respondents' self-estimated health status, were analysed using the correspondence analysis. The results showed that the first two dimensions explained for 95.06% of the total per cent of inertia (statistically significant at p<0.001 level) indicating the strong relationship among variables. According to the survey, the fruit and vegetables are most likely used fresh (54.43% and 48.95%, respectively), whereas the use of fruits and vegetables after storage in a refrigerator (17.16% and 29.96%, respectively) or in a deep freezer (2.81% or 5.20%, respectively) is significantly lower. Most of the survey correspondents claimed that they regularly discard the fruits and vegetables to waste (53.02% and 39.66%, respectively). The results pointed out that the storage and the amounts of discarded waste are affected by gender, income, and health conditions of the respondents'. Also, the study emphasized the growing need for educating and better planning of fresh fruit and vegetable management to reduce waste production.

Prediction of mechanical extraction oil yield of new sunflower hybrids: artificial neural network model

BACKGROUND

Sunflower seeds are in the top five most abundant oilseeds in the world, as well as sunflower oil in the edible oils group. Recently, increasing attention has been paid to cold-pressed sunflower oil because less processing is involved and no solvent is used. The present study was carried out to investigate dimensions (length, width, thickness), firmness, general (moisture content and hull content, mass of 1000 seeds), gravimetric (true and bulk density, porosity) and geometric characteristics (equivalent diameter, surface area, seed volume, sphericity) of 20 new sunflower hybrid seeds. Steps to determine most of these parameters are quite simple and easy since the process does not require long time or special equipment.

RESULTS

Principal component analysis and cluster analysis confirmed differences in the mentioned characteristics between oily and confectionary sunflower hybrid seeds. One of the major differences between two groups of samples was in extraction oil yield. Mechanical extraction oil yield of the oily hybrid seeds was significantly (P ˂ 0.05) higher (from 68.72 ± 4.21% to 75.61 ± 1.99%) compared to confectionary hybrids (from 20.10 ± 2.82% to 39.91 ± 6.23%). Extraction oil yield values are known only after oil extraction.

CONCLUSION

Knowledge of the extraction oil yield value before the mechanical extraction enables better management of the process. By application of the artificial neural network approach, an optimal neural network model was developed. The developed model showed a good generalization capability to predict the mechanical extraction oil yield of new sunflower hybrids based on the experimental data, which was a main goal of this paper. © 2021 Society of Chemical Industry.

The characterization and pollution status of the surface sediment in the Boka Kotorska Bay, Montenegro

Surface sediments collected from twelve stations in the Boka Kotorska Bay were analyzed for the level and distribution of twenty-six elements and ten oxides, grain sizes, organic matter, and carbonate content. Potentially toxic elements (Al, Fe, Mn, Cr, Zn, Ni, Cu, Pb, As, Co, U) were determined to assess the contamination status and potential environmental risk according to the single-element indices (enrichment factor (EF), geoaccumulation index (Igeo), contamination factor (CF)), and combined index (pollution load index (PLI)). The single-element indices EF and CF revealed that the surface marine sediment was moderately polluted with Pb, Cu, and Cr, while Igeo indicated moderate pollution with Ni > Cr > Zn > Cu > As and moderate to heavy pollution with Pb, as a result of the anthropogenic factors. The method of the combined effect of toxic elements, PLI, showed the highest pollution rate at the shipyard location in the Bay of Tivat. Pearson's correlation coefficient (r), principal component analysis (PCA), and cluster analysis (CA) were applied to highlight similarities and differences in the distribution of the investigated elements in the Bay, confirming the claim obtained by the pollution indices. The sediment contamination with most heavy metals, such as Cr, Zn, Ni, Cu, Pb, and As, has been identified in the Tivat Bay area.

A Validation Model for Prediction of Kovats Retention Indices of Compounds Isolated from Origanum spp. and Thymus spp. Essential Oils

Abstract. This work aimed to obtain a validated model for the prediction of retention times of compounds isolated from Origanum heracleoticum, Origanum vulgare, Thymus vulgaris, and Thymus serpyllum essential oils. In total 68 experimentally obtained retention times of compounds, which were separated and detected by GC-MS were further used to build the prediction models. The quantitative structure–retention relationship was employed to foresee the Kovats retention indices of compounds acquired by GC-MS analysis, using eight molecular descriptors selected by a genetic algorithm. The chosen descriptors were used as inputs for the four artificial neural networks, to construct a Kovats retention indices predictive quantitative structure–retention relationship model. The coefficients of determination in the training cycle were 0.830; 0.852; 0.922 and 0.815 (for compounds found in O. heracleoticum, O. vulgare, T. vulgaris and T. serpyllum essential oils, respectively), demonstrating that these models could be used for prediction of Kovats retention indices, due to low prediction error and high r2.   Resumen. El objetivo de este trabajo es la obtención de modelos validados para la predicción del tiempo de retención de los compuestos aislados de aceites esenciales de Origanum heracleoticum, Origanum vulgare, Thymus vulgaris y Thymus serpyllum. Se han obtenido un total de 68 tiempos de retención de compuestos, separándose y detectándose por cromatografía de gases con detección por espectrometría de masas (GC-MS) con posterior desarrollo de modelos de predicción.  La relación cuantitativa estructura-retención ha sido utilizada para predecir el índice de retención Kovats de los compuestos obtenidos por análisis de GC-MS, utilizando ocho descriptores moleculares seleccionados mediante algoritmo genético. Los descriptores seleccionados han sido utilizados como entrada para las cuatro redes neuronales artificiales y así elaborar los índices predictivos del modelo de relación cuantitativa estructura-retención.  Los coeficientes de determinación en el ciclo de entrenamiento fueron de 0.830; 0.852; 0.922 y 0.815 (para los compuestos identificados en los aceites esenciales del O. heracleoticum, O. vulgare, T. vulgaris y T. serpyllum respectivamente) demostrando así que estos modelos son útiles en la predicción de los índices de retención de Kovats con un error de bajo predicción y alta r2.

Comprehensive Profiling of Microbiologically Induced CaCO3 Precipitation by Ureolytic Bacillus Isolates from Alkaline Soils

Microbiologically induced CaCO3 precipitation (MICP) is a well-known bio-based solution with application in environmental, geotechnical, and civil engineering. The significance of the MICP has increased explorations of process efficiency and specificity via natural bacterial isolates. In this study, comprehensive profiling of five soil ureolytic Bacillus strains was performed through a newly formed procedure that involved six steps from selection and identification, through kinetic study, to the characterization of the obtained precipitates, for the first time. To shorten the whole selection procedure of 43 bioagents with the MICP potential, Standard Score Analysis was performed and five selected bacteria were identified as Bacillus muralis, B. lentus, B. simplex, B. firmus, and B. licheniformis by the MALDI-TOF mass spectrometry. Despite following the targeted activity, kinetic studies were included important aspects of ureolysis and the MICP such as cell concentration, pH profiling, and reduction in calcium ion concentration. At the final step, characterization of the obtained precipitates was performed using FTIR, XRD, Raman, DTA/TGA, and SEM analysis. Although all tested strains showed significant potential in terms of precipitation of calcite or calcite and vaterite phase, the main differences in the MICP behavior can be observed at the bacterial strain level. B. licheniformis showed favorable behavior compared to the reference Sporosarcina pasteurii DSM 33.

Contribution of bacterial cells as nucleation centers in microbiologically induced CaCO3 precipitation-A mathematical modeling approach

The microbiologically induced calcite precipitation (MICP) has been extensively studied for geotechnical engineering through simultaneous action of natural phenomena and engineering processes. The focus of bacterial contribution to the MICP has been directed to calcium carbonate productivity, while the additional bacterial role as a crystal nucleation center was not explained especially from a mathematical prediction modeling point of view. Therefore, this study provides explanations and a mathematical modeling approach of bacterial influence on the MICP induced by newly-isolated ureolytic Bacillus strains and Sporosarcina pasteurii DSM 33. Using the obtained results of low-cost, rapid, and simple assays, artificial neural network modeling was applied for cell surface predispositions, pH changes as well as calcium-involved function in biofilm formation during the MICP, for the first time. Based on the obtained contribution of the alkalophilic/alkaloresistant bacteria, calcite precipitation can be significantly directed by the presence, of ureolytic bacterial cells as nucleation centers during CaCO₃ precipitation as well as their morphology, surface characteristics, potential to form a biofilm, and/or generate pH changes.

Spatial and temporal distribution of pollution indices in marine surface sediments-a chemometric approach

Concentrations of As, Cd, Cr, Cu, Hg, Mn, Ni, Pb, Zn, and Fe measured at forty locations in the Boka Kotorska Bay were used to monitor the spatial and temporal quality of sediment and to assess surface sediment contamination over the last 15 years. This ecological geochemistry assessment was made using two classes of pollution indices: single indices concerning the investigated elements (contamination factor (Cf)) and integrated indices concerning the locations (pollution load index (PLI), potential ecological risk index (RI), mean effects range median quotient (MERMQ), toxic risk index (TRI), contamination severity index (CSI)). The distribution of all indices was geostatistically mapped and several hotspots were identified. Based on the indices applied in the risk assessment, the mean contribution of individual metal species to the total risk was determined and presented in the following order: Cd > Hg > As > Pb > Ni > Cu > Cr > Zn. This study revealed that Cd and Hg were at the top of the risk list among the examined elements. The temporal distribution of Hg has shown a decreasing trend during the period 2005-2019, while the presence of Cd in the Bay environment is of major concern. The results revealed that the most polluted part of the investigated area was Tivat bay within the Boka Kotorska Bay. Multivariate statistical analysis of pollution indices resulted in multicollinearity, which enabled the use of a reduced number of indices with an acceptable risk estimation.

Recovery of β-carotene from pumpkin using switchable natural deep eutectic solvents

The aim of the present research was to develop green and sustainable extraction procedure for β-carotene recovery from pumpkin. A series of hydrophobic natural deep eutectic solvents (NADESs) based onfatty acids were prepared to establish high extraction efficiency of β-carotene and to increase stability of extracted carotenoids from the pumpkin. To intensify extraction process, NADES composed of C8 and C10 fatty acids (3:1) was selected and coupled with ultrasound assisted extraction. Response surface methodology and artificial neural network model (ANN) model was adopted to analyze significance of extraction parameters demonstrating high prediction levels of the β-carotene yield, experimentally confirming the maximum β-carotene content of 151.41 µg/mL at the optimal process condition. Extracted carotenoids in the optimal NADES extract have shown high stability during the storing period of 180 days. A switchable-hydrophilicity eutectic solvent system has been introduced as a successful way to recover extracted carotenoids from the NADES solvent. It was capable of precipitating 90% of carotenoids present in the extract. The proposed procedure is simple, easily scalable and has minimal impact on operators and the environment.

EXPLORING CONSUMER PREFERENCES AND FACTORS ASSOCIATED WITH VEGETABLE CONSUMPTION

The consumers’ preferences and their reasons for vegetable consumption were examined with the aim to provide useful information to increase the consumption of these food items.  Consumers’ general viewpoints about vegetable consumption were examined using an online questionnaire. The study was conducted on a sample of 711 respondents chosen randomly and obtained data were analyzed by the correspondence analysis. Results suggest that consumers consider vegetables as tasty, easy to use and healthy for consumption. The main drawbacks for vegetable consumption are safety issues, short shelf-life and high price. In order to increase the consumption of these foodstuffs, each type of fresh and processed vegetable should have a specifically designed campaign, emphasizing its specific attribute. Obtained results might provide an insight into the current state of consumers’ behavior in Serbia and might be useful for creating specific promotional programs and more appropriate communication strategies aiming to increase consumers’ knowledge about the importance of regular vegetable consumption. They should raise consumers’ awareness by emphasizing the importance of adequate daily vegetable consumption. This, in turn, should improve public health and reduce the health and economic costs of massive chronic diseases caused by inadequate diet.