Influence of Infill Level and Post-Processing on Physical Parameters and Betaine Content of Enriched 3D-Printed Sweet Snacks.

Betaine is a non-essential amino acid with proven functional properties and untapped potential for cereal food enrichment. While 3D printing represents a viable approach for manufacturing enriched cereal-based foods with novel shapes and textures, it is crucial to consider the impact of printing parameters and post-processing on the betaine content and properties of these products. The aim of this study was to investigate the influence of the infill level (20, 30 and 40%) of 3D-printed cuboid shapes and the post-processing techniques (drying oven, vacuum dryer, air fryer) of betaine-enriched oat-based snacks on the print quality, texture, and sensory properties, as well as the content of preserved betaine. The interaction of post-processing technique and infill level influenced the length deviation and texture properties, as well as the betaine content of snacks. Height stability was only influenced by post-processing technique. In general, oven-dried snacks showed the best dimensional stability, having the lowest width/length deformation (about 8%) at the infill level of 20%. Betaine was best preserved (19-31% loss) in snacks post-processed in a vacuum dryer (1281-1497 mg/g), followed by an air fryer and a drying oven, where betaine loss was in the range 28-55%. Air-fried snacks with 40% infill level had the highest values of instrumentally measured crunchiness (38.9 Nmm) as well as sensory test values for liking of texture (7.5), intensity of odor (6) and overall flavor (6). Overall, air frying proved to be a convenient and quick post-processing technique for 3D-printed snacks, but infill patterns for preserving betaine should be further explored. Vacuum drying could be used to preserve bioactive compounds, but efforts should be made to minimize its negative impact on the physical deformations of the 3D-printed products.

Legume Fingerprinting through Lipid Composition: Utilizing GC/MS with Multivariate Statistics.

This study presents a tentative analysis of the lipid composition of 47 legume samples, encompassing species such as Phaseolus spp., Vicia spp., Pisum spp., and Lathyrus spp. Lipid extraction and GC/MS (gas chromatography with mass spectrometric detection) analysis were conducted, followed by multivariate statistical methods for data interpretation. Hierarchical Cluster Analysis (HCA) revealed two major clusters, distinguishing beans and snap beans (Phaseolus spp.) from faba beans (Vicia faba), peas (Pisum sativum), and grass peas (Lathyrus sativus). Principal Component Analysis (PCA) yielded 2D and 3D score plots, effectively discriminating legume species. Linear Discriminant Analysis (LDA) achieved a 100% accurate classification of the training set and a 90% accuracy of the test set. The lipid-based fingerprinting elucidated compounds crucial for discrimination. Both PCA and LDA biplots highlighted squalene and fatty acid methyl esters (FAMEs) of 9,12,15-octadecatrienoic acid (C18:3) and 5,11,14,17-eicosatetraenoic acid (C20:4) as influential in the clustering of beans and snap beans. Unique compounds, including 13-docosenoic acid (C22:1) and γ-tocopherol, O-methyl-, characterized grass pea samples. Faba bean samples were discriminated by FAMEs of heneicosanoic acid (C21:0) and oxiraneoctanoic acid, 3-octyl- (C18-ox). However, C18-ox was also found in pea samples, but in significantly lower amounts. This research demonstrates the efficacy of lipid analysis coupled with multivariate statistics for accurate differentiation and classification of legumes, according to their botanical origins.

Recovery of Polyphenols from Rosehip Seed Waste Using Natural Deep Eutectic Solvents and Ultrasonic Waves Simultaneously.

Rosehips are processed and consumed in numerous forms, such as juice, wine, herbal tea, yogurt, preserved fruit, and canned products. The seeds share in fruit is 30-35% and they have recently been recognized as an important source of oil rich in unsaturated fatty acids. However, after defatting, seed waste may still contain some polar polyphenolic compounds, which have been scarcely investigated. The aim of this study was to examine the potential of the defatted seed waste as a source of polyphenols. For the defatting process, supercritical carbon dioxide extraction at 300 bar and 40 °C was applied. The capacity of eight different natural deep eutectic solvents (NADES) for the recovery of phenolics from defatted rosehip seed powder (dRSP) was examined. In the extracts obtained with ultrasound-assisted NADES extraction, twenty-one phenolic compounds were identified with LC-MS/MS, among which the most abundant were quinic acid (22.43 × 103 µg/g dRSP) and catechin (571.93 µg/g dRSP). Ternary NADES formulations based on lactic acid proved to be superior. Potential correlations between identified chemical compounds, solvent polarity and viscosity, as well as the compound distributions across studied solvent combinations in PCA hyperspace, were also investigated. PCA demonstrated that more polar NADES mixtures showed improved extraction potential. The established environmentally friendly process represents an approach of transforming rosehip seed waste into value-added products with the potential to be applied in the food industry and to contribute to sustainable production.

Rapid Triage of Children with Suspected COVID-19 Using Laboratory-Based Machine-Learning Algorithms.

In order to limit the spread of the novel betacoronavirus (SARS-CoV-2), it is necessary to detect positive cases as soon as possible and isolate them. For this purpose, machine-learning algorithms, as a field of artificial intelligence, have been recognized as a promising tool. The aim of this study was to assess the utility of the most common machine-learning algorithms in the rapid triage of children with suspected COVID-19 using easily accessible and inexpensive laboratory parameters. A cross-sectional study was conducted on 566 children treated for respiratory diseases: 280 children with PCR-confirmed SARS-CoV-2 infection and 286 children with respiratory symptoms who were SARS-CoV-2 PCR-negative (control group). Six machine-learning algorithms, based on the blood laboratory data, were tested: random forest, support vector machine, linear discriminant analysis, artificial neural network, k-nearest neighbors, and decision tree. The training set was validated through stratified cross-validation, while the performance of each algorithm was confirmed by an independent test set. Random forest and support vector machine models demonstrated the highest accuracy of 85% and 82.1%, respectively. The models demonstrated better sensitivity than specificity and better negative predictive value than positive predictive value. The F1 score was higher for the random forest than for the support vector machine model, 85.2% and 82.3%, respectively. This study might have significant clinical applications, helping healthcare providers identify children with COVID-19 in the early stage, prior to PCR and/or antigen testing. Additionally, machine-learning algorithms could improve overall testing efficiency with no extra costs for the healthcare facility.

Betaine as a Functional Ingredient: Metabolism, Health-Promoting Attributes, Food Sources, Applications and Analysis Methods.

Betaine is a non-essential amino acid with proven functional properties and underutilized potential. The most common dietary sources of betaine are beets, spinach, and whole grains. Whole grains-such as quinoa, wheat and oat brans, brown rice, barley, etc.-are generally considered rich sources of betaine. This valuable compound has gained popularity as an ingredient in novel and functional foods due to the demonstrated health benefits that it may provide. This review study will provide an overview of the various natural sources of betaine, including different types of food products, and explore the potential of betaine as an innovative functional ingredient. It will thoroughly discuss its metabolic pathways and physiology, disease-preventing and health-promoting properties, and further highlight the extraction procedures and detection methods in different matrices. In addition, gaps in the existing scientific literature will be emphasized.

Clinical Hematochemical Parameters in Differential Diagnosis between Pediatric SARS-CoV-2 and Influenza Virus Infection: An Automated Machine Learning Approach.

BACKGROUND: The influenza virus and the novel beta coronavirus (SARS-CoV-2) have similar transmission characteristics, and it is very difficult to distinguish them clinically. With the development of information technologies, novel opportunities have arisen for the application of intelligent software systems in disease diagnosis and patient triage. METHODS: A cross-sectional study was conducted on 268 infants: 133 infants with a SARS-CoV-2 infection and 135 infants with an influenza virus infection. In total, 10 hematochemical variables were used to construct an automated machine learning model. RESULTS: An accuracy range from 53.8% to 60.7% was obtained by applying support vector machine, random forest, k-nearest neighbors, logistic regression, and neural network models. Alternatively, an automated model convincingly outperformed other models with an accuracy of 98.4%. The proposed automated algorithm recommended a random tree model, a randomization-based ensemble method, as the most appropriate for the given dataset. CONCLUSIONS: The application of automated machine learning in clinical practice can contribute to more objective, accurate, and rapid diagnosis of SARS-CoV-2 and influenza virus infections in children.

Hemogram-based decision tree models for discriminating COVID-19 from RSV in infants.

OBJECTIVE: Decision trees are efficient and reliable decision-making algorithms, and medicine has reached its peak of interest in these methods during the current pandemic. Herein, we reported several decision tree algorithms for a rapid discrimination between coronavirus disease (COVID-19) and respiratory syncytial virus (RSV) infection in infants. METHODS: A cross-sectional study was conducted on 77 infants: 33 infants with novel betacoronavirus (SARS-CoV-2) infection and 44 infants with RSV infection. In total, 23 hemogram-based instances were used to construct the decision tree models via 10-fold cross-validation method. RESULTS: The Random forest model showed the highest accuracy (81.8%), while in terms of sensitivity (72.7%), specificity (88.6%), positive predictive value (82.8%), and negative predictive value (81.3%), the optimized forest model was the most superior one. CONCLUSION: Random forest and optimized forest models might have significant clinical applications, helping to speed up decision-making when SARS-CoV-2 and RSV are suspected, prior to molecular genome sequencing and/or antigen testing.

Starch-based noodles: Current technologies, properties, and challenges.

Starch noodles are gaining interest due to the massive popularity of gluten-free foods. Modified starch is generally used for noodle production due to the functional limitations of native starches. Raw materials, methods, key processing steps, additives, cooking, and textural properties determine the quality of starch noodles. The introduction of traditional, novel, and natural chemical additives used in starch noodles and their potential effects also impacts noodle quality. This review summarizes the current knowledge of the native and modified starch as raw materials and key processing steps for the production of starch noodles. Further, this article aimed to comprehensively collate some of the vital information published on the thermal, pasting, cooking, and textural properties of starch noodles. Technological, nutritional, and sensory challenges during the development of starch noodles are well discussed. Due to the increasing demands of consumers for safe food items with a long shelf life, the development of starch noodles and other convenience food products has increased. Also, the incorporation of modified starches overcomes the shortcomings of native starches, such as lack of viscosity and thickening power, retrogradation characteristics, or hydrophobicity. Starch can improve the stability of the dough structure but reduces the strength and resistance to deformation of the dough. Some technological, sensory, and nutritional challenges also impact the production process.

Could platelet indices have diagnostic properties in children with COVID-19?

INTRODUCTION: Viral infections are often accompanied by reactive thrombocytosis, that is, increased activity of platelets, which is especially common in infants and children. OBJECTIVE: This study aimed to test the diagnostic properties of platelet indices, plateletcrit (PCT), mean platelet volume (MPV) and platelet distribution width (PDW), in children with beta corona virus 2 (SARS-CoV-2) infection. METHODS: The study included 232 patients below the age of 18 admitted to the coronavirus disease (COVID-19) isolation wards at the Institute for Child and Youth Health Care of Vojvodina. PCT, MPV and PDW values on the day of admission were recorded. In total, 245 controls were selected from those treated for SARS-CoV-2 negative respiratory infections. Descriptive and inferential statistical analyses were performed. RESULTS: MPV and PDW were found important as independent predictors for COVID-19 in children. Furthermore, the joint effect of MPV and PDW for predicting COVID-19 was confirmed. The parameters showed better sensitivity than specificity. CONCLUSION: Our study showed that PCT is not clinically significant, while MPV and PDW have diagnostic value in predicting COVID-19 in children. In perspective, these parameters could be implemented in the various learning algorithms in order to achieve earlier diagnosis and treatment.

Black Elderberry Press Cake as a Source of Bioactive Ingredients Using Green-Based Extraction Approaches.

To study the efficiency of two green-based extraction techniques for the isolation of bioactive constituents from black elderberry press cake, changes in phenolic compounds and main anthocyanin contents were analyzed. Polyphenolic content was correlated with antioxidant and antidiabetic potential by radical-scavenging activity and monitoring of α-amylase inhibition. Black elderberry press-cake extracts were obtained by ultrasound-assisted (UAE) and microwave-assisted (MAE) extractions under different extraction conditions. High-performance liquid chromatography (HPLC) analysis revealed that cyanidin-3-sambubioside and cyanidin-3-glucoside were the principal anthocyanins in all the obtained extracts, with their content being highest in MAE obtained at 80 °C over 5 min. The same extract induced two-fold higher antioxidant activity (IC50 6.89 µg/mL) and α-amylase inhibitory potential (IC50 2.18 mg/mL) in comparison to UAE extracts. The main compositional differences between extracts obtained by the same extraction technique were assigned to the extraction temperature. A principal component analysis confirmed that the antidiabetic feature is to be attributed to the rich content of anthocyanins in black elderberry press cake. Our results indicate the great potential of underutilized black elderberry press cake for the development of novel food and herbal formulations due to notable anthocyanin contents highly correlated with antidiabetic activity.

Feasibility study of separation and purification of bile acid derivatives by HPLC on C18 and F5 columns.

Organic synthesis could be very demanding, usually due to difficulties related to the separation of main reaction products from by-products. Steroidal compounds could have similar lipophilicity, which is mostly based on the lipophilicity of the steroidal core. This causes many problems during purification, i.e. in obtaining a pure single steroidal compound. In this research, a group of bile acid derivatives were subjected to HPLC analysis using four experimental systems, which presented combinations of C18 and F5 columns with methanol-water and acetonitrile-water as mobile phases. Retention parameters and retention order of the compounds were established and indicated that all experimental systems could be applicable in order to separate and/or purify some individual compounds or a mixture of a few compounds. However, the only experimental system that could separate a mixture of all investigated derivatives proved to be a C18 column with acetonitrile-water as a mobile phase. Since complex interactions between F5 column and the analytes exist, molecular surface polarity (MSP) was tested as a lipophilicity parameter, and also compared with logP using multivariate statistics. Retention parameters obtained on F5 column were used as descriptors, both with MSP and with logP, concluding that logP has shown to be a better lipophilicity descriptor.

Bioprocessing of Wheat and Amaranth Bran for the Reduction of Fructan Levels and Application in 3D-Printed Snacks.

Bran can enrich snacks with dietary fibre but contains fructans that trigger symptoms in people with irritable bowel syndrome (IBS). This study aimed to investigate the bioprocessing of wheat and amaranth bran for degrading fructans and its application (at 20% flour-based) in 3D-printed snacks. Bran was bioprocessed with Saccharomyces cerevisiae alone or combined with inulinase, Kluyveromyces marxianus, Limosilactobacillus fermentum, or commercial starter LV1 for 24 h. Fructans, fructose, glucose, and mannitol in the bran were analysed enzymatically. Dough rheology, snack printing precision, shrinkage in baking, texture, colour, and sensory attributes were determined. The fructan content of wheat bran was 2.64% dry weight, and in amaranth bran, it was 0.96% dry weight. Bioprocessing reduced fructan content (up to 93%) depending on the bran type and bioprocessing agent, while fructose and mannitol remained below the cut-off value for IBS patients. Bran bioprocessing increased the complex viscosity and yield stress of dough (by up to 43 and 183%, respectively) in addition to printing precision (by up to 13%), while it lessened shrinkage in baking (by 20-69%) and the hardness of the snacks (by 20%). The intensity of snack sensory attributes depended on the bran type and bioprocessing agent, but the liking ("neither like nor dislike") was similar between samples. In conclusion, snacks can be enriched with fibre while remaining low in fructans by applying bioprocessed wheat or amaranth bran and 3D printing.

Multivariate analysis of water quality parameters in Lake Palic, Serbia.

This study presents a comprehensive investigation of water quality parameters in the fourth sector of Lake Palic in Serbia, which has a regional strategic importance. Namely, it is designated as a tourist destination. What is perhaps even more important is that its surplus water ends up in Lake Ludas, a significant habitat for migrating and aquatic bird species, and it is a RAMSAR site. The conducted analysis points to the major conclusion that the reasons for very high Chlorophyll-a values can be found in considerable anthropogenic pressures exerted on the studied area. Due to these pressures, the lake is not in ecological equilibrium. To support this conclusion, an in-depth analysis was conducted using water quality measurements for 9 years, from 2011 to 2019. The data was subject to principal component analysis (PCA) and machine learning classification algorithms that identified a seasonal character regarding the lake's water quality. Water quality indexes (WQI) were determined using two approaches to provide a more general insight into the lake's overall quality. Keeping in mind the large number of data gathered monthly within the Palic-Ludas Lake system, fitted models for estimating certain water quality parameters were also developed. This was accomplished via multivariate regression, resulting in a number of equations that can, using a few basic input parameters, predict values of ammonium nitrogen, Chlorophyll-a, and 5-day biological oxygen demand. The fitted models were obtained for relatively homogeneous periods within a year identified by cluster analysis.

Supercritical Carbon Dioxide Extraction of Allium ursinum: Impact of Temperature and Pressure on the Extracts Chemical Profile.

The aim of this study was to extract Allium ursinum L. for the first time by supercritical carbon dioxide (SC-CO2 ) as green sustainable method. The impact of temperature in the range from 40 to 60 °C and pressure between 150 and 400 bar on the quality of the obtained extracts and efficiency of the extraction was investigated. The highest extraction yield (3.43 %) was achieved by applying the extraction conditions of 400 bar and 60 °C. The analysis of the extracts was performed by gas chromatography and mass spectrometry (GC/MS). The most dominant sulfur-containing constituent of the extracts was allyl methyl trisulfide with the highest abundance at 350 bar and 50 °C. In addition, the presence of other pharmacologically potent sulfur compounds was recorded including S-methyl methanethiosulfinate, diallyl trisulfide, S-methyl methylthiosulfonate, and dimethyl trisulfide. Multivariate data analysis tool was utilized to investigate distributions of the identified compounds among the extracts obtained under various extraction conditions and yields. It was determined that the SC-CO2 extraction can by efficiently used for A. ursinum.

Application of Deep Eutectic Solvents for the Extraction of Rutin and Rosmarinic Acid from Satureja montana L. and Evaluation of the Extracts Antiradical Activity.

Satureja montana L. was used in the current research as the plant exhibits numerous health-promoting benefits due to its specific chemical composition. The extraction method based on deep eutectic solvents (DESs) was used for the extraction of rutin and rosmarinic acid from this plant. Five different choline chloride-based DESs with different volumes of water (10%, 30%, and 50% (v/v)) were used for the extraction at different temperatures (30, 50, and 70 °C) to investigate the influence on rosmarinic acid and rutin content obtained by high-performance liquid chromatography with diode-array detector (HPLC-DAD) in the obtained extracts. A principal component analysis was employed to explore and visualize the influence of applied parameters on the efficiency of the extraction procedure of rutin and rosmarinic acid. Among the tested DESs, choline chloride:lactic acid (mole ratio 1:2) and choline chloride:levulinic acid (mole ratio 1:2) were the most suitable for the extraction of rutin, while for rosmarinic acid choline chloride:urea (mole ratio 1:2) was the most effective solvent. The extract showing the best antiradical activity was obtained with choline chloride:urea (mole ratio 1:1) at 30 °C and 50% H2O (v/v).