Quantification Of Content Research Articles

Wavelength selection enables robust quantification of oil content with near-infrared spectroscopy in pea protein gels produced under varying heating conditions

Thermal processing influences the near-infrared (NIR) spectra of food products, causing inaccuracy when quantifying the composition of the product. In this study, the effect of thermal processing on NIR measurements of the oil content in pea protein isolate (PPI) gels was investigated. Analysis of variance showed that the heating temperature (30°C–120 °C) during the production of PPI gels influenced large parts of the NIR spectra and time (2.5–15 min) only had a limited effect. Stepwise multiple linear regression was used to select a combination of 5 wavelengths that was suitable to create a robust model for the prediction of oil content (Q2 = 0.9928 ± 0.002, root mean standard error of prediction = 0.2806 ± 0.0423 wt%). This combination of wavelengths was shown to reduce the effect of the history of thermal processing on the measurement, improving the accuracy of oil content quantification. This approach can be applied for in-line quantification of the oil content of food products subjected to varying heating conditions using specialized sensors.

Development of a Spectrophotometric Flow-Based System for the Determination of Total Polyphenol Content in Legume Flours.

Conventional nutritional characterization of legume flours comprises costly and laborious analytical methods for nutrient quantification that establish the quality of seeds, including their macronutrient quantification, fiber, mineral, antioxidant content, and more. The quantification of total polyphenol content (TPC) in legumes is performed using different analytic methods, namely the Folin-Ciocalteu (FC) method, which is lengthy and employs potentially toxic reagents. Additionally, it is time-consuming and prone to human error if carried out in a conventional batch mode. To develop a semi-automatic method for a faster, greener, and more precise TPC quantification, resorting to flow injection analysis (FIA). The development of the FIA method was based on the FC method. The flow manifold was structured by performing an array of preliminary studies, which led to the establishment of the method that displayed the highest sensitivity (highest slope of the calibration curve). The method was applied to determine the TPC in various legume flours. This novel method resulted in a throughput of 1 analysis per minute, allowing to analyze 20 samples in triplicate within an hour, with a LOD of 4.32 mg L-1, a LOQ of 14.4 mg L-1, and an RSD of 4.4%. The results calculated from the proposed FIA method agreed with those of the reference procedure. By using the FIA system, a lower consumption of reagents was observed. Additionally, as there is no need to reach chemical equilibrium, a high throughput has been achieved, resulting in a faster and greener method for the determination of polyphenols in various types of legumes.

Benchtop NMR spectroscopy for quantitative determination of milk fat and qualitative determination of lactose: From calibration curve to deep learning

This study compares three different methodologies for the quantification of the fat content of ultra-high temperature (UHT) milk using benchtop proton nuclear magnetic resonance (1H NMR) spectroscopy, a flagship of green, accessible, and state-of-the-art technology suitable for modern laboratory environments. The evaluated approaches included traditional calibration curve and machine learning algorithms, with emphasis on partial least squares regression (PLS-R) and artificial neural networks (ANN), to estimate the fat content in skimmed, semi-skimmed and whole milk. Among these, ANN provided the most accurate results for all types of milk, particularly in skimmed milk, with a relative standard deviation (RSD) of 14.9% and an accuracy of −7.3%. The calibration curve showed higher variability, with an RSD of 34.1% and trueness of 25.3% for skimmed milk. PLS-R improved accuracy in relation to the calibration curve approach, reducing RSD to 18.9% and trueness to −17.7%. The developed method has been successfully applied to determine the fat content in 51 samples of UHT milk purchased in different Spanish supermarkets, providing adequate results for each of the three categories considered, including goat's milk, sheep's milk, and milk coffee. Furthermore, the application of machine learning has proven its validity by successfully distinguishing between lactose and lactose-free UHT milk.

Collaborative study on the cross-reactivity of two influenza B viral components in single radial immunodiffusion assay using quadrivalent influenza vaccines in Japan from 2015/16 to 2021–22 influenza season

A quadrivalent influenza vaccine (QIV) has been available in Japan since the 2015/2016 influenza season. Single radial immunodiffusion (SRID) assays are currently used worldwide to measure the hemagglutinin (HA) content of influenza vaccine components because they are simple, accurate, and the regulatory requirement, ensuring consistency in manufacture for the HA content. However, the cross-reactivity of antisera against the two lineages of the influenza B virus (IFVB) may cause inaccurate quantification of HA content in QIVs using the SRID assay. To examine cross-reactivity and develop an appropriate procedure for accurate measurement of vaccine potency, a collaborative study with four Japanese vaccine manufacturers was conducted to measure the HA contents of trivalent influenza vaccines (TIVs) and QIVs by SRID assay with a single and a mixture of reference antigens (refAgs) from each lineage of IFVB for seven influenza seasons from 2015/16 to 2021/22. The cross-reactivity of the two IFVB components in the SRID assay varied depending on the vaccine viruses. Our study demonstrated that it is useful to validate a suitable combination for each refAg and reference antiserum by selecting the combination showing similar HA contents between experimental TIV and QIV before lot release testing.

Presence of Soya in Industrial and Homemade Sausage Production in Kosovo and Its Reflection on Fatty Acid Profile

In the present study, we investigated to what extent soybean was used in industrial and homemade sausage produced in Kosovo, as well as its potential impact on the fatty acid profile. In total, 63 samples, 42 industrial and 21 traditional sausages, were collected either from the market or the production site. All samples were tested by means of real-time PCR for the detection and quantification of soy content, as well as the GC-FID to determine the potential reflection in the fatty acid profile. The presence of soy DNA was detected in 54 out of 63 samples. In total, 41 out of 42 industrial sausage samples were positive for soy DNA, with an average ct value of 22.60 and a standard deviation (SD) of 4.28, whereas 13 out of 21 traditional sausage samples were positive for soy DNA, with a mean ct value of 23.36 and a standard deviation (SD) of 4.56. There was a statistically significant difference in means between industrial sausage and traditional sausage, with p ≤ 0.001 based on CT values (ANOVA test). We investigated the correlation between ct values in real-time PCR for soy DNA with each fatty acid content. There is a moderate correlation of soy DNA ct values with C16:0 palmitin (decrease), C18:0 stearic acid (decrease), C18:1 oleic acid (increase), and overall saturated fatty acids (decrease). With the exception of C14:0 Myristin, C18:1 oleic, and C20:0 Arachin acids and monosaturated fats, the ANOVA test reveals a significant difference in means between groups for the majority of the fatty acids between industrial sausages and traditional sausages. The current study demonstrates that the fatty acid composition of sausages is influenced by the amount of soy present in them. The extent to which other components affect the fatty acid profile is unknown. However, an increase in oleic acid and a decrease in stearic and overall saturated fatty acids are expected.

Analytical control of residues of the herbicide trifluralin in the assessment of the food safety

Introduction. Trifluralin is a systemic herbicide of the chemical class of dinitroaniline derivatives. During monitoring studies, trifluralin was detected in carrots produced in the Russian Federation. Since trifluralin-based pesticides are not approved for the use in the Russian Federation, residual amounts of the substance are not typical for this crop. To check the contamination of carrots with a pesticide not typical for this crop, confirmatory qualitative and quantitative studies were carried out. Purpose of the work. Increasing the efficiency of identification and reliability of quantitative results when monitoring food products for safety when performing confirmatory analytical studies using the example of determining trifluralin in carrots to assess the safety of food products intended for the consumer. Materials and methods. Gas chromatography coupled with tandem mass spectrometry (GC-MS/MS) was used to identify and quantify trifluralin. Samples were prepared for analysis using the QuEChERS method. Identification was carried out by multiple reaction monitoring (MRM) using a triple quadrupole mass spectrometer using two to four MRM transitions (m/z): 306,1→264,0; 264,0→206,0; 264,0→188,0; 264,0→160,1. Matrix-matched calibration was used for quantitation. Results. The effectiveness of screening studies when monitoring food products using the GC-MS/MS method can be significantly elevated by increasing the number of MRM transitions (at least 3), controlling the reference retention time of the substance, and maintaining the ratio of confirmatory ions. To obtain a reliable quantification of trifluralin content in carrots, the use of a matrix-matched calibration is recommended. Limitation. In the study, only carrots were considered as food products. Conclusion. Confirmation of test results is especially important when determining pesticide residues that are not normally found in a given matrix, or when it is suspected that the maximum permissible level may be exceeded. When conducting a screening study and detecting a food contaminant, contamination of the product can only be reported in advance. Next, a confirmatory analysis is required using a validated quantitative method, including an appropriate calibration procedure. In quantitative analysis, the presence of matrix sample components can cause problems due to sample suppression/enhancement phenomena. Matrix-aware calibration has been shown to be effective in compensating for matrix effects.

Quantification and division of unfrozen water content of frozen soils during freezing and the influence of freeze-thaw cycles

Quantification and division of unfrozen water content of frozen soils during freezing and the influence of freeze-thaw cycles

Quantification of soluble protein content and characterization of protein secondary structure by Raman spectroscopy combined with chemometrics

In the large-scale industrialized production of soymilk, the accurate monitoring of protein content and its secondary structure is crucial to guarantee product quality and enhance nutritional value, while traditional protein detection methods have significant limitations. In this study, Raman spectroscopy was used to characterize the changes in the relative content of protein secondary structures during boiling, in which the α-helix structure was converted to other unstable secondary structures, indicating that the protein was thermally denatured. The study also accurately predicted the soluble protein content of soymilk for the first time by combining Raman spectroscopy and chemometrics based on algorithms such as Partial Least Squares (PLS), Least Squares Support Vector Machines (LSSVM), and Artificial Neural Networks (ANN). The results showed that the relative percentage deviation (RPD) values of all models were greater than 2, indicating that the models all possessed a certain degree of robustness; The PLS-based regression prediction model (with an average RPD value of 3.5592 and an average R2p of 0.9597) had the best overall prediction. The study's results can guide the soymilk production industry into online monitoring of soymilk quality.

Facile photometric quantification of aldehyde content in oxidized starches using 2-aminobenzamide oxime as an aldehyde selective reagent

AbstractThe oxidation of starch to aldehyde starch or dialdehyde starch introduces aldehyde groups to the starch core structure. This functionalization enables a wide range of further derivatization, e.g., oxidation to the corresponding carboxylic acid or functionalization to the corresponding amine via imine formation and reduction, which can be used for cross-linking. Herein, we report a novel method for efficiently quantifying these aldehyde groups based on the selective adduct formation of 2-aminobenzamide oxime (ABAO) and aldehydes. We initially implemented this photometric assay for C6-oxidized aldehyde starch using a plate reader, enabling parallel analysis of multiple samples in a fraction of the time and consistent with the conventional titration methods. Complementing this stream-lined analytical approach, we present an abbreviated workflow for faster downstream processing of reaction samples toward analysis. All of this was performed at a very small scale, cutting costs for (expensive) reagents and solvents, as well as drastically reducing working time. Finally, we also demonstrate our assay's applicability for quantifying the two aldehyde groups formed in dialdehyde starch derived from NaIO4 oxidation. Graphical abstract

Local Application of a New Chalconic Derivative (Chalcone T4) Reduces Inflammation and Oxidative Stress in a Periodontitis Model in Rats.

Chalcones are phenolic compounds with biological properties. This study had the aim to evaluate the effects of topical administration of a new synthetic chalcone, Chalcone T4, in an animal model of periodontitis induced by ligature. Forty rats were distributed in the following experimental groups: negative control (without periodontitis and topical application of distilled water), positive control (periodontitis and topical application of distilled water), chalcone I and II (periodontitis and topical application of 0.6 mg/mL and 1.8 mg/mL, respectively). Chalcone or distilled water was administered into the gingival sulcus of the first molars daily for 10 days, starting with the ligature installation. The following outcomes were evaluated: alveolar bone loss (µCT and methylene blue dye staining), quantification of osteoclasts (histomorphometry), cell infiltrate and collagen content (stereometry), gene expression of mediators (Nfact11, Tnf-α, Mmp-13, iNos, Sod and Nrf2) by (RT-qPCR); expression of BCL-2 and Caspase-1 (immunohistochemistry). Chalcone T4 inhibited bone resorption and prevented collagen matrix degradation. Reduction in the expression of inflammatory markers (Nfact11, Tnf-α, Mmp-13, and Caspase-1), attenuation of oxidative stress (iNOS reduction, and increase in Sod), and pro-apoptotic effect of the compound (BCL-2 reduction), were associated its effects on periodontal tissues. Topical application of Chalcone T4 prevented bone resorption and inflammation, demonstrating potential in the adjunctive treatment of periodontitis.

Accurate quantification of soil organic matter content using VNIR-SWIR spectra: The role of straw and spectrally active materials

Soil organic matter (SOM) is crucial for carbon sequestration and sustainable agriculture, yet traditional quantification methods are challenging to apply at large scales. Hyperspectral technology combined with machine-learning offers promising prospects for rapid quantification. This study explores the impact of using VNIR-SWIR spectra on SOM quantification in regions characterized by distinctive soil properties and agricultural activity. Specifically, we propose an innovative approach using 105 soil samples from Yueyang City, China, to refine the range of spectrally active materials and evaluate the effectiveness of iron oxides and straw on SOM quantification. Three feature construction methods (conventional (VNIR-SWIR spectra), optimal (information spectrum subset, ISS), and straw-merged ISS (SISS)) and seven models were employed to evaluate the contributions of iron oxides and straw in SOM quantification. The results indicate that the SISS improved the generalization (RPD and R2) of nonlinear and linear models by approximately 9 % and 4 %, respectively. The relative contributions of straw and iron oxides in modelling are approximately 35 % and 10 %, respectively. Our research successfully developed the SISS by refining the range of spectrally active materials and considering the background formed by the soil properties of the study area. We used it to evaluate the impact of straw on SOM quantification and demonstrated that the spectroscopic characterization of SOM can assess the carbon sequestration benefits of agricultural activities. This approach can be applied to regions with similar soil properties globally, offering a new perspective for SOM quantification.

Quantification of soil water content by machine learning using enhanced high-resolution ERT

The accurate acquisition of soil water content is a fundamental cornerstone of research into hydrological processes and agricultural engineering. Electrical Resistivity Tomography (ERT) has been validated for hydrological studies and soil monitoring. The establishment of a quantitative relationship between ERT resistivity data and soil water content is usually based on rock physics models. However, the applicability of such models in complex environments and the acquisition of the relevant parameters pose a certain challenge. In addition, the spatial resolution of ERT limits its application in soil moisture assessment. Therefore, a machine learning-based approach is proposed in this study to determine the quantitative relationship between resistivity and soil water content. We investigate the integration of three machine learning models (KNN, RF, XGBOOST) with ERT to predict the water content of clay soils. The results show that the RF model achieves an R2 of 0.92 with an RMSE of 0.41. To improve the ERT resolution, a new data collection method (MRU) is introduced in this study by increasing the density of ERT data collection. A comparative analysis is conducted between traditional ERT data collection methods and the MRU approach in terms of soil water content prediction accuracy. The results show that the MRU method of data collection improves the accuracy of soil water content prediction by an average of 57% compared to traditional methods. This study confirms the feasibility of using machine learning models to establish mappings between resistance and water content and shows that the MRU data collection method for ERT effectively improves the accuracy of predicting soil water content. These results provide a new perspective for hydrological process research and agricultural monitoring technology.

Comparative LC–MS-based metabolite profiling, antioxidant, and antibacterial properties of Bunium bulbocastanum tubers from two regions in Algeria

Traditional herbalists have been relied on for many years by Algerians to cure a wide range of diseases. Regardless of their nutritional values, mushrooms have chemical properties that make them attractive, beneficial, and more likely to be studied by researchers, according to ethnobotanical literature on traditional phytotherapy. Among all the edible mushrooms, tubers are a type of fungus that are traditionally used in fine dining and have garnered attention recently because of their many therapeutic applications. This research delves into a meticulous analysis of bioactive constituents in Bunium bulbocastanum tubers, sourced from Mostaganem and Relizane regions, with a keen focus on polyphenols, flavonoids, and condensed tannins. The quantification of total phenolic content was executed through the Folin-Ciocalteu assay, while flavonoids were assessed via the aluminum chloride colorimetric method. In addition, condensed tannins were evaluated in this study. Antioxidant capacities were scrutinized employing the 2,2′-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging assay. Microbial inhibition studies were conducted against five benchmark bacterial strains, utilizing the agar disc diffusion technique. Furthermore, a comprehensive liquid chromatography–mass spectrometry (LC–MS) analysis was performed to identify and quantify bioactive compounds. The findings underscore that the Mostaganem extracts were particularly rich in polyphenols (11.65 mg GAE/g of extract) and tannins (1.30 mg CE/g of extract), while the Relizane extracts boasted significant flavonoid concentrations (9.421 mg QE/g of extract). Notably, 4-methylguaiacol (82.04 mg/L), caffeic acid dimethyl ether (27.76 mg/L), syringic acid (20.48 mg/L), and naringenin (16.05 mg/L) emerged as the predominant volatile compounds. Compositional investigation of the extracts by LC–MS confirmed the presence of various compounds that were linked to the bioactivities exhibited by B. bulbocastanum tubers. These findings demonstrate the effective antibacterial and antioxidant properties of B. bulbocastanum tubers, indicating their potential use in pharmaceutical and nutraceutical applications.

Tear Proteomics in Children and Adolescents with Type 1 Diabetes: A Promising Approach to Biomarker Identification of Diabetes Pathogenesis and Complications.

The aim of the current study was to investigate the tear proteome in children and adolescents with type 1 diabetes (T1D) compared to healthy controls, and to identify differences in the tear proteome of children with T1D depending on different characteristics of the disease. Fifty-six children with T1D at least one year after diagnosis, aged 6-17 years old, and fifty-six healthy age- and sex-matched controls were enrolled in this cross-sectional study. The proteomic analysis was based on liquid chromatography tandem mass spectrometry (LC-MS/MS) enabling the identification and quantification of the protein content via Data-Independent Acquisition by Neural Networks (DIA-NN). Data are available via ProteomeXchange with the identifier PXD052994. In total, 3302 proteins were identified from tear samples. Two hundred thirty-nine tear proteins were differentially expressed in children with T1D compared to healthy controls. Most of them were involved in the immune response, tissue homeostasis and inflammation. The presence of diabetic ketoacidosis at diagnosis and the level of glycemic control of children with T1D influenced the tear proteome. Tear proteomics analysis revealed a different proteome pattern in children with T1D compared to healthy controls offering insights on deregulated biological processes underlying the pathogenesis of T1D. Differences within the T1D group could unravel biomarkers for early detection of long-term complications of T1D.

MetFinder: A Tool for Automated Quantitation of Metastatic Burden in Histological Sections From PreclinicalModels.

As efforts to study the mechanisms of melanoma metastasis and novel therapeutic approaches multiply, researchers need accurate, high-throughput methods to evaluate the effects on tumor burden resulting from specific interventions. We show that automated quantification of tumor content from whole slide images is a compelling solution to assess invivo experiments. In order to increase the outflow of data collection from preclinical studies, we assembled a large dataset with annotations and trained a deep neural network for the quantitative analysis of melanoma tumor content on histopathological sections of murine models. After assessing its performance in segmenting these images, the tool obtained consistent results with an orthogonal method (bioluminescence) of measuring metastasis in an experimental setting. This AI-based algorithm, made freely available to academic laboratories through a web-interface called MetFinder, promises to become an asset for melanoma researchers and pathologists interested in accurate, quantitative assessment of metastasis burden.

UPLC-MS/MS analysis and chemical profile of Ficus pertusa L. bark, an ethnomedicinal resource from intertropical America.

Background Ficus pertusa is a native species from intertropical America used for medicinal purpouses. However, despite its importance as ethnobotanical resource, there is poor chemical information about its extracts. Therefore, the objective of this study was to evaluate the phytochemical profile of the hydroalcoholic extract of F. pertusa barks through UPLC-MS/MS, as well as its phenolic and flavonoid content. Methodology The chemical identification was performed by Ultra-High Liquid Chromatography coupled to Mass Spectrometry, whereas the Total Phenolic content and Total Flavonoid quantification were measured through UV spectrometry. Results As a result, 19 substances were identified, which are mostly included within the group of condensed tannins, phenolic acids and flavonoids, such as procyanidins, chlorogenic acid and luteolin. Conclusion The identification of the main substances of F. pertusa’s bark and the measurement of its polyphenols content can guide the understanding of the pharmacological mechanisms involved in its ethnomedicinal uses.

On the determination of charge and nitrogen content in cellulose fibres modified to contain quaternary amine functionality

Research interest in quaternization of cellulose fibres has increased considerably over the past decades. However, there is little or no consensus regarding how to characterize the material in terms of degree of substitution (DS), and the literature suggests a range of different methods focusing on charge determination as well as nitrogen content quantification. This work aims to fill the knowledge gap regarding how the different methods perform in relation to each other, and for what cellulosic systems each method has advantages, disadvantages and even potential pitfalls. FT-IR and NMR measurements are used to establish successful modification and determine the relative number of substituent groups. Another six methods are compared for the determination of the DS of cellulosic fibres and nanofibrils. The methods include Kjeldahl measurements, nitrogen determination by chemiluminescence, determination of molecular nitrogen by the Dumas method, colloidal titration, conductometric titration and polyelectrolyte adsorption. It can be concluded that most techniques investigated are reliable within certain ranges of DS and/or when using appropriate post-treatment of the quaternized material and suitable sample preparation techniques. The results from the present work hence provide recommendations to make an educated choice of method, and experimental protocol, based on the technique at hand.

Biological, Antifungal, and Physical Efficacy of a Denture Cleanser Formulated with Cnidium officinale Extracts.

We aimed to assess the antifungal efficacy and impact of a denture cleanser containing Cnidium officinale extract on the surface characteristics of denture base materials, as well as its physical and biological properties. The experimental denture cleansers were formulated with C. officinale at concentrations of 100 and 150 μg/mL, combined with 1% cocamidopropyl betaine as a natural surfactant. Antifungal efficacy was evaluated using zone-of-inhibition assays against Candida albicans, revealing inhibition zones of 20 ± 1.8 mm for the 100 μg/mL concentration and 23.6 ± 1.6 mm for the 150 μg/mL concentration. Surface property assessments-including hardness, roughness, color stability, and solubility measurements-demonstrated no significant differences compared to the control group. Biological evaluations included the quantification of polyphenol and flavonoid content. The C. officinale-based cleanser showed significant antifungal activity without affecting the hardness, roughness, color stability, or solubility of denture base materials. Biological tests revealed no cytotoxicity and minimal mucosal irritation. Polyphenol and flavonoid contents were quantitatively measured, revealing higher concentrations in the experimental groups, which were correlated with significant antifungal activity. These compounds are known for their roles in disrupting microbial processes and enhancing antimicrobial effects. These findings suggest that the C. officinale-based denture cleanser effectively inhibits C. albicans while preserving the physical properties of denture base materials. This study highlights the potential of C. officinale in denture cleanser formulations, promoting denture hygiene and oral health. Future research should prioritize long-term clinical evaluations and formulation optimization.

Characterization of pediatric porcine pulmonary valves as a model for tissue engineered heart valves

Heart valve tissue engineering holds the potential to transform the surgical management of congenital heart defects affecting the pediatric pulmonary valve (PV) by offering a viable valve replacement. While aiming to recapitulate the native valve, the minimum requirement for tissue engineered heart valves (TEHVs) has historically been adequate mechanical function at implantation. However, long-term in situ functionality of TEHVs remains elusive, suggesting that a closer approximation of the native valve is required. The realization of biomimetic engineered pediatric PV is impeded by insufficient characterization of healthy pediatric tissue. In this study, we comprehensively characterized the planar biaxial tensile behaviour, extracellular matrix (ECM) composition and organization, and valvular interstitial cell (VIC) phenotypes of PVs from piglets to provide benchmarks for TEHVs. The piglet PV possessed an anisotropic and non-linear tension-strain profile from which material constants for a predictive constitutive model were derived. The ECM of the piglet PV possessed a trilayer organization populated by collagen, glycosaminoglycans, and elastin. Biochemical quantification of ECM content normalized to wet weight and DNA content of PV tissue revealed homogeneous distribution across sampled regions of the leaflet. Finally, VICs in the piglet PV were primarily quiescent vimentin-expressing fibroblasts, with a small proportion of activated α-smooth muscle actin-expressing myofibroblasts. Overall, piglet PV properties were consistent with those reported anecdotally for pediatric human PVs and distinct from those of adult porcine and human PVs, supporting the utility of the properties determined here to inform the design of tissue engineered pediatric PVs. Statement of significanceHeart valve tissue engineering has the potential to transform treatment for children born with defective pulmonary valves by providing living replacement tissue that can grow with the child. The design of tissue engineered heart valves is best informed by native valve properties, but native pediatric pulmonary valves have not been fully described to date. Here, we provide comprehensive characterization of the planar biaxial tensile behaviour, extracellular matrix composition and organization, and valvular interstitial cell phenotypes of pulmonary valves from piglets as a model for the native human pediatric valve. Together, these findings provide standards that inform engineered heart valve design towards generation of biomimetic pediatric pulmonary valves.

Rapid quantification of oil content in castor seeds using FT-NIR spectroscopy

Rapid quantification of oil content in castor seeds using FT-NIR spectroscopy