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Simplified validation and optimization methods for determination polychlorinated biphenyls in tuna fish
No full textThe Synthesis of ZnMn2O4 by Glycine Nitrate Combustion and an Examination of Its Electrochemical Properties in Aqueous Solutions of ZnCl2 and NaNO3
No full textTo research potential replacements for LiMn2O4, this work carries out a synthesis of ZnMn2O4 (ZMO) through the glycine-nitrate combustion. XRPD validates its phase characterization; electron microscopy confirmed the presence of single phase ZMO in the form of curvy and rod-shaped sub-micron sized particles at an average of 113 nm. This work is novel for its electrochemical measurements of ZMO as a cathodic material for alkali-ion batteries, performed by cyclic voltammetry in aqueous solutions of ZnCl2 and NaNO3 ranging from 5 to 100 mV s(-1) polarization rates, yielding stable capacities for both electrolytes. The highest capacities were obtained for the polarization speed of 5 mV s(-1) for both electrolytes. Their cathode capacities were 11.3 and 27.2 mA h g(-1) for ZnCl2 and NaNO3, respectively. Concerning Na-ions batteries, the surface storage is the deciding factor due to the adsorption of Na+ ions on the lambda-MnO2 phase. Unlike Zn-ions, Na+ ions cannot intercalate into the spinel structured materials
Food Recognition and Food Waste Estimation Using Convolutional Neural Network
No full textIn this study, an evaluation of food waste generation was conducted, using images taken before and after the daily meals of people aged between 20 and 30 years in Serbia, for the period between 1 January and 31 April in 2022. A convolutional neural network (CNN) was employed for the tasks of recognizing food images before the meal and estimating the percentage of food waste according to the photographs taken. Keeping in mind the vast variates and types of food available, the image recognition and validation of food items present a generally very challenging task. Nevertheless, deep learning has recently been shown to be a very potent image recognition procedure, while CNN presents a state-of-the-art method of deep learning. The CNN technique was implemented to the food detection and food waste estimation tasks throughout the parameter optimization procedure. The images of the most frequently encountered food items were collected from the internet to create an image dataset, covering 157 food categories, which was used to evaluate recognition performance. Each category included between 50 and 200 images, while the total number of images in the database reached 23,552. The CNN model presented good prediction capabilities, showing an accuracy of 0.988 and a loss of 0.102, after the network training cycle. The average food waste per meal, in the frame of the analysis in Serbia, was 21.3%, according to the images collected for food waste evaluation
Synthesis, physicochemical, and antimicrobial characteristics of novel poly(urethane-siloxane) network/silver ferrite nanocomposites
No full textIn situ polymerization was used to produce novel AgFeO2"PEG/polyurethane network nanocomposites (NP-PUs) with 30-60 wt% of soft poly(dimethylsiloxane) segments in polyurethane (PU), containing 1 wt% of PEG-coated AgFeO2 nanoparticles, AgFeO2"PEG. Physicochemical properties and in vitro biological activity of the NP-PUs were systematically evaluated in terms of AgFeO2"PEG (NP) addition and soft segment content. High-angle annular dark-field transmission electron microscopy showed that the nanoparticles were generally uniformly distributed in the PU matrix. Increased soft segment content caused significantly increased intensity of the broad, amorphous X-ray diffraction peaks of crystalline AgFeO2, probably because the chemical composition of PU affected the distribution of nanoparticles. The Young modulus, hardness, and plasticity of the NP-PUs were higher than for pure PU and increased with decreasing soft segment content. Decreased soft segment content induced higher microphase separation, increased hydrophilicity and swelling ability, but decreased cross-linking density. Additionally, NP-PUs had higher glass transition temperatures, improved thermal stability, and enhanced nanomechanical performance over pure PU. The NP-PUs demonstrated good selective inhibition of Candida albicans and Candida parapsilosis (30-55%) and no pronounced cytotoxicity to MRC5 human lung fibroblasts. Among the investigated AgFeO2"PEG/PUs, the best antifungal activity was shown by composites with 30 and 40 wt% soft segments. Consequently, the novel AgFeO2"PEG/polyurethane network nanocomposites could be further optimized to be used as biocompatible surfaces that also prevent formation of fungal biofilms. [GRAPHICS]
Quality and Sensory Profile of Durum Wheat Pasta Enriched with Carrot Waste Encapsulates
No full textConsumer knowledge about pasta quality differs around the world. Modern consumers are more sophisticated compared to past times, due to the availability of information on pasta types and quality. Therefore, this study investigated the nutritional, physical, textural, and morphological quality of durum wheat pasta enriched with carrot waste encapsulates (10 and 20% freeze-dried encapsulate (FDE) and 10 and 20% spray-dried encapsulate (SDE)), as well as determining consumer preferences for this type of product. Replacement of semolina with FDE and SDE contributed to changes in the pasta nutritional quality, which was reflected in the increased protein, fat, and ash content. Additionally, changes in cooking quality, color, and texture were within satisfactory limits. The uncooked pasta enriched with 10 and 20% SDE was characterized by a lighter yellow intensity with color saturation, as well as an imperceptible waxy appearance compared to the control and enriched pasta with 10 and 20% FDE. After cooking, the yellow color was more intense in all the enriched pasta samples which can be linked to the raw cereal which was significantly greater in the control in comparison to the FDE and SDE containing samples. Overall, carrot waste can be a promising material for the food industry to produce high-quality pasta
Utilization of Sugar Beet Pulp as Biosorbent for Molassigenic Metal Ions: Kinetic Study of Batch Biosorption
No full textThe 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 cationexhange 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 nonlinear 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. sugar beet pulp, alkalized juice, biosorption, molassigenic metal ion
Yield and Quality Prediction of Winter Rapeseed-Artificial Neural Network and Random Forest Models
No full textAs one of the greatest agricultural challenges, yield prediction is an important issue for producers, stakeholders, and the global trade market. Most of the variation in yield is attributed to environmental factors such as climate conditions, soil type and cultivation practices. Artificial neural networks (ANNs) and random forest regression (RFR) are machine learning tools that are used unambiguously for crop yield prediction. There is limited research regarding the application of these mathematical models for the prediction of rapeseed yield and quality. A four-year study (2015-2018) was carried out in the Republic of Serbia with 40 winter rapeseed genotypes. The field trial was designed as a randomized complete block design in three replications. ANN, based on the Broyden-Fletcher-Goldfarb-Shanno iterative algorithm, and RFR models were used for prediction of seed yield, oil and protein yield, oil and protein content, and 1000 seed weight, based on the year of production and genotype. The best production year for rapeseed cultivation was 2016, when the highest seed and oil yield were achieved, 2994 kg/ha and 1402 kg/ha, respectively. The RFR model showed better prediction capabilities compared to the ANN model (the r(2) values for prediction of output variables were 0.944, 0.935, 0.912, 0.886, 0.936 and 0.900, for oil and protein content, seed yield, 1000 seed weight, oil and protein yield, respectively)
Monitoring of the Wines' Quality by Gas Chromatography: HSS-GC/FID Method Development, Validation, Verification, for Analysis of Volatile Compounds
No full textOne of the most common techniques for wine analysis is gas chromatography coupled with the flame ionization detector and headspace autosampler (HSS-GC/FID) for the analysis of the volatile compounds in the wine samples. The main goal of this thesis was to develop the method for the analysis of volatiles (methanol, higher alcohols, and esters) in wine samples made of Cabernet Sauvignon and Merlot. Validation parameters were: r(2) > 0.995; LOD (0.2-1.0 mg/L); CV (2.7-6.3%), and recovery (92-106%). Average contents of the methanol (198.0 mg/L and 150.5 mg/L), higher alcohols (398.5 mg/L and 335.8 mg/L), ethyl acetate (42.0 mg/L and 55.6 mg/L), and acetaldehyde (23.3 mg/L and 16.1 mg/L) were determined for Merlot and Cabernet Sauvignon, respectively. Based on the obtained results, it was concluded that the content of methanol is in direct connection with the type of grape used for preparation of the wine. It was also found that the duration of the maceration directly influenced the content of the methanol and higher alcohols. On the other hand, type of grape appeared not to have influence on the content of ethyl acetate and acetaldehyde in wines. The post hoc Tukey's HSD test at 95% confidence limit showed significant differences between observed samples. Principal Component Analysis (PCA) was used for assessing the effect of different genotypes and extraction methods on wine samples. Using PCA of observed samples, the possible directions for improving the quality of product can be realized
Modeling fruit and vegetable consumption in Serbia
No full textUprkos tome što voće i povrće imaju ključnu ulogu u pravilnoj ishrani i dobrom zdravstvenom stanju, većina potrošača u Srbiji ne konzumira dovoljne količine ovih namirnica. U cilju boljeg razumevanja ovog nezadovoljavajućeg stanja, istraživanje testira prošireni model teorije planiranog ponašanja, kako bi se predložili potrebni koraci za unapređenje svakodnevne konzumacije voća i povrća. Ova teorija, proširena za ulogu znanja, testirana je upotrebom strukturnih jednačina. Indeksi podesnosti su potvrdili korisnost proširenog modela teorije planiranog ponašanja za bolje razumevanje ponašanja potrošača, kao i za medijatorsku ulogu namera potrošača da povećaju konzmaciju ovih namirnica. Republika Srbija, kao jedna od ključnih zemalja u razvoju na Balkanu, izabrana je za testiranje modela, uz mogućnosti njegove primene i na druge zemlje u razvoju koje se suočavaju sa neadekvatnom ishranom stanovništva. Podaci su prikupljeni u Vojvodini putem online upitnika (n=688). I pored visokog nivoa svesti potrošača u Vojvodini o značaju voća i povrća na zdravlje, njihovo znanje, samo po sebi, nije dovoljno da dovede do promena u njihovom ponašanju vezanom za konzumaciju voća i povrća. Na namere i ponašanje potrošača treba uticati indirektno, kroz promenu njihovih stavova i subjektivnih normi. Upravo zbog toga, rezultate ove studije treba uzeti u obzir prilikom kreiranja aktivnosti usmerenih na promenu ponašanja potrošača u pravcu povećanja konzumacije voća i povrća.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
Applications of biomaterials in wound healing management: from fundamental physiology to advanced technology
No full textThe process of wound healing is still considered as a thought-provoking clinical topic that requires adequate interventions to properly accomplish the major objective. In the last few years, significant work has been made with prominence on the development of novel therapeutic tactics and technologies applied to acute and chronic phases of wound healing. In turn, this phenomenon does require several populations of cells and the presence of an extracellular matrix with concomitant action of cytokines and growth factors to balance the processes of inflammation and regeneration. This healing process comprises four continuous phases: coagulation and hemostasis (phase I), inflammation (phase II), proliferation (phase III), and wound renovation accompanied by scar formation (phase IV). The use of the most accurate methodology for this purpose elicits different clinical results. Several materials in nature possess advantageous properties concerning their capability to induce proliferation of cells and biocompatible profile.Although synthetic materials are by far the most expensive than natural ones, they hold outstanding characteristics while natural compounds elicit some immune reactions. These materials are nowadays also obtained using synthetic methods and are widely applied in medicine in the field of tissue regeneration and, subsequently, in wound healing. This chapter is intended to expose, detail, and combine a variety of knowledge concerning wound healing mechanisms, critical points in the treatment of wounds, advances during the last decades on the medicinal products for this objective, and the extensive role of biomaterials concerning their function, origin, constitution, and application in such subject. The great potential and restrictions for using these materials are further explored in the following chapter