Synthetic surfactants are used in several industries, including manufacturing, pharmaceutical and cosmetic’s, food and feed, agriculture, petroleum and environmental remediation for their ability to adsorb to fluid and solid-water interfaces. However, their widespread use and their synthetic preparation through environmentally unfavorable processes counterbalances the value of this class of reagents. This fact has stimulated new efforts to exploit natural sources of surfactants, such as new classes of bacterial systems or manipulation of existing biological systems, that may produce, through an environmentally friendly process, new biodegradable surfactants and emulsifiers of high commercial value. A downside of microbial production of biobased chemicals such as these types of chemicals, is that their fermentation often yields crude materials consisting of several bioproducts with complex physical and chemical properties. Extraction, identification, and efficient characterization of biosurfactants from a crude mixture of biomolecules requires carefully designed, and detailed analytical processes using state-of-the-art methods. The purpose of this review article is to present the current state-of-the-art and future outlook on the various multidisciplinary biophysical methods applied in the discovery, extraction identification, and in-depth characterization of microbially-produced surface‐active compounds.

/pdf/multi-method-biophysical-analysis-in-discovery-2a20rrkz.pdf

Multi-method biophysical analysis in discovery, identification, and in-depth characterization of surface‐active compounds

Quality assessment of traditional food by NMR analysis

Urine metabolomics is gaining traction as a means of identifying metabolic signatures associated with health and disease states. Thirty-one (31) late preterm (LP) neonates admitted to the neonatal intensive care unit (NICU) and 23 age-matched healthy LPs admitted to the maternity ward of a tertiary hospital were included in the study. Proton nuclear magnetic resonance (1H NMR) spectroscopy was employed for urine metabolomic analysis on the 1st and 3rd days of life of the neonates. The data were analyzed using univariate and multivariate statistical analysis. A unique metabolic pattern of enhanced metabolites was identified in the NICU-admitted LPs from the 1st day of life. Metabolic profiles were distinct in LPs presenting with respiratory distress syndrome (RDS). The discrepancies likely reflect differences in the gut microbiota, either due to variations in nutrient intake or as a result of medical interventions, such as the administration of antibiotics and other medications. Altered metabolites could potentially serve as biomarkers for identifying critically ill LP neonates or those at high risk for adverse outcomes later in life, including metabolic risks. The discovery of novel biomarkers may uncover potential targets for drug discovery and optimal periods for effective intervention, offering a personalized approach.

/pdf/identification-of-novel-biomarkers-in-late-preterm-neonates-1q4pxfwe.pdf

Identification of Novel Biomarkers in Late Preterm Neonates with Respiratory Distress Syndrome (RDS) Using Urinary Metabolomic Analysis

There is a plethora of food products with geographical indications registered in the European Union without any study about their discrimination from other similar products. This is also the case for Greek currants. This paper aims to analyze if stable isotope analysis of C, N, and S could discriminate the Greek currants “Vositzza”, registered as a product of Protected Designation of Origin, from two other currants registered as products of Protected Geographical Indication coming from neighboring areas. The first results show that the stable isotope ratio of sulfur is not detectable due to the very low sulfur content in the samples, and the analysis should be based on the stable isotope ratios of carbon and nitrogen to discriminate these products. The mean value of δ15N (1.38‰) of PDO “Vostizza” currants is lower than that of currants grown outside the PDO zone (2.01‰), while the mean value of δ13C of PDO “Vostizza” currants is higher (−23.93‰) in comparison to that of currants grown outside the PDO zone (−24.83‰). Nevertheless, the results indicate that with only two isotopic ratios, discrimination could not be achieved, and further analysis is required.

/pdf/application-of-stable-isotope-analysis-for-detecting-the-3ugl572a.pdf

Application of Stable Isotope Analysis for Detecting the Geographical Origin of the Greek Currants “Vostizza”: A Preliminary Study

Respiratory distress syndrome (RDS) is a major respiratory morbidity associated with prematurity. In this case-control study, we prospectively evaluated whether targeted metabolomic analysis (gas chromatography–mass spectrometry) of the gastric fluid obtained from very preterm neonates (&amp;lt; 32 weeks’ gestation) during the first hour after birth could predict the need for exogenous surfactant for the treatment of RDS. 43 infants with RDS necessitating surfactant (cases) were compared with 30 infants with mild or no RDS (controls) who were not treated with surfactant. Perinatal and neonatal characteristics were recorded. Univariate analysis showed significant differences between the two groups in gastric fluid metabolites (L-glycine and acetyl-L-serine) and clinical parameters (gestational age, 1- and 5-minute Apgar scores, and intubation in the delivery room). Moreover, multivariate analysis revealed intubation in the delivery room as the most potent clinical predictor of surfactant administration [AUC of 0.69 (95% CI 0.57-0.81)]. The combination of metabolomic and clinical data allowed for the construction of a model with good accuracy [AUC 0.76 (95% CI 0.64-0.86)] in predicting surfactant replacement therapy. Further research is required to explore the role of gastric fluid metabolomics in the prediction of RDS and other neonatal outcomes. 

Predicting the Need for Surfactant Replacement Therapy in Very Preterm Infants by Combining Gastric Fluid Metabolomics with Neonatal Data

In this study, non-targeted 1 H NMR fingerprinting was used in combination with multivariate statistical analyses for the classification of Greek currants based on their geographical origins (Aeghion, Nemea, Kalamata, Zante, and Amaliada). As classification techniques, Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were carried out. To elucidate different components according to PDO (Protected Designation of Origin), products from Aeghion (Vostizza) were statistically compared with each one of the four other regions. PLS-DA plots ensure that currants from Kalamata, Nemea, Zante, and Amaliada are well classified with respect to the PDO currants, according to differences observed in metabolites. Results suggest that composition differences in carbohydrates, amino, and organic acids of currants are sufficient to discriminate them in correlation to their geographical origin. In conclusion, currants metabolites which mostly contribute to classification performance of such discriminant analysis model present a suitable alternative technique for currants traceability. The study results contribute information to the currants' metabolite fingerprinting by NMR spectroscopy and their geographical origin. PRACTICAL APPLICATION: This study presents an analytical approach for a high nutritional value Greek PDO product, Vostizza currant. A further research and implementation of this method in food industry, can be the key to food fraud incidents. Thus, application of this work opens up posibilities to "farm to table" mission.

Nontargeted 1H NMR fingerprinting and multivariate statistical analysis for traceability of Greek PDO Vostizza currants

https://link.springer.com/content/pdf/10.1007/s11306-022-01941-4.pdf

NMR-based metabolic profiling of children with premature adrenarche

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Nontargeted 1H NMR fingerprinting and multivariate statistical analysis for traceability of Greek PDO Vostizza currants

Identification of Novel Biomarkers in Late Preterm Neonates with Respiratory Distress Syndrome (RDS) Using Urinary Metabolomic Analysis

NMR-based metabolic profiling of children with premature adrenarche