Unknown Concentration Research Articles

Efficient Sensor Calibration Via Machine Learning-Based Resampling Methods

It remains a challenge to accurately and precisely calibrate a sensor in an experimentally-efficient manner. The calibration task at hand is to quantify the functional relationship between the sensor’s response (denoted as y) and the target analyte concentration (denoted as x) based on laboratory experiments. In practical use, the sensor’s mathematical calibration model needs to be coupled with the device to make inference regarding the concentration of the analyte in an environment based on the observed sensor response. It is critical to provide a single-value estimate for the analyte concentration, as well as to quantify the uncertainty of the target estimate (i.e., to provide the possible range of the underlying concentration level).However, classic statistical methods are not able to provide valid inference on the target quantity of interest due to the following difficulties in sensor calibration. First, not only the typical forward modeling but also the inverse estimation is involved. The former refers to the generation of a calibration model functionally relating the analyte concentration to the sensor response (x→y) based on experimental data; The latter concerns the use of the calibration model in practical setting to obtain from the observed response the estimation of the unknown concentration (y→x). Second, the variability of the sensor responses tends to be heterogeneous as opposed to homogeneous, which is an assumption that classic statistical methods rely on.In light of the above, a computational resampling method was adapted in this work to address the machine learning (ML)-based inference issues (i.e., the uncertainty quantification of the estimated analyte concentrations). Built on the inference capability, a two-stage experimental design procedure was developed to guide the efficient sampling of calibration data via laboratory experiments.The ML-based inference and experimental design methods are integrated in the chemometric calibration procedure, which was applied on a simulated biosensor to demonstrate its efficiency over traditional methods. The sensor simulator is developed from the real experimental data for a paper-based lateral flow strip (PLFS) designed to measure in blood the diagnosis biomarkers for traumatic brain injury. PLFS is finding increasing applications in detection of various diseases in point-of-care testing settings. Figure 1

Glucose Monitoring System using Machine Learning

Bio-medical analysis is an indispensable procedure for identifying health-related diseases like diabetes. Monitoring the glucose level in our body regularly helps us identify hyperglycemia and hypoglycemia which can cause severe medical problems like nerve damage or kidney diseases. This paper outlines a technique for predicting the Glucose concentration in blood samples utilizing Image Processing and Machine Learning Algorithms. The glucose solution is prepared by the Glucose Oxidase (GOD) and Peroxidase (POD) method. Experimental database is generated based on colorimetric technique. The image of the glucose solution is captured by the raspberry pi camera and analyzed using Image Processing by extracting the RGB, HSV, LUX color space values. Regression algorithms like Multiple Linear Regression, Decision Tree, RandomForest, and XGBoost were used to predict the unknown glucose concentration. Since multiple linear regression yields the best results when compared to other models, with the highest accuracy of 96.56% for selected features and 89.73% for all features, it should be used to predict blood sample glucose levels. The image processing and machine learning based approach reduces the hardware complexities of the existing platforms.

Rapid and quantitative detection of respiratory viruses using surface-enhanced Raman spectroscopy and machine learning

Rapid and sensitive pathogen detection is important for prevention and control of disease. Here, we report a label-free diagnostic platform that combines surface-enhanced Raman scattering (SERS) and machine learning for the rapid and accurate detection of thirteen respiratory virus species including SARS-CoV-2, common human coronaviruses, influenza viruses, and others. Virus detection and measurement have been performed using highly sensitive SiO2 coated silver nanorod array substrates, allowing for detection and identification of their characteristic SERS peaks. Using appropriate spectral processing procedures and machine learning algorithms (MLAs) including support vector machine (SVM), k-nearest neighbor, and random forest, the virus species as well as strains and variants have been differentiated and classified and a differentiation accuracy of >99% has been obtained. Utilizing SVM-based regression, quantitative calibration curves have been constructed to accurately estimate the unknown virus concentrations in buffer and saliva. This study shows that using a combination of SERS, MLA, and regression, it is possible to classify and quantify the virus in saliva, which could aid medical diagnosis and therapeutic intervention.

Smartphone-based spectrometry system as a prescreening assessment of copper and iron for real time control of water pollution

Due to anthropogenic actions, the presence of pollutants in water bodies, such as toxic metals, are increasingly negatively affecting water quality, biodiversity and sustainable goals worldwide. Therefore, decentralization of water pollution monitoring with low-cost devices, such as using smartphones, suggests an innovative green technology for in situ and real-time control. In this study, a Handheld Smartphone Spectrophotometry System (HSSS) was developed to estimate copper and iron concentration water samples. The system mainly comprises a portable commercial spectrometer (GoSpectro) that can measure the spectrum of light in the visible region. The HSSS LOD and LOQ for copper were equal to 0.589 and 1.784 mg/L, respectively, and 0.479 and 1.450 mg/L, respectively for iron. In addition, the results of copper and iron concentrations in samples with unknown concentrations using HSSS were close to the Benchtop Spectrophometer (BS). Finally, HSSS performance showed to be a new green technology for water quality management with potential applications for monitoring water resources and also providing further possibilities to measure other pollutants by the same technique, in addition to metals.

Type 2 polysaccharide storage myopathy in Quarter Horses is a novel glycogen storage disease causing exertional rhabdomyolysis.

Both type 1 (PSSM1) and type 2 polysaccharide storage myopathy (PSSM2) are characterised by aggregates of abnormal polysaccharide in skeletal muscle. Whereas the genetic basis for PSSM1 is known (R309H GYS1), the cause of PSSM2 in Quarter Horses (PSSM2-QH) is unknown and glycogen concentrations not defined. To characterise the histopathological and biochemical features of PSSM2-QH and determine if an associated monogenic variant exists in genes known to cause glycogenosis. Retrospective case control. Sixty-four PSSM2-QH, 30 PSSM1-QH and 185 control-QH were identified from a biopsy repository and clinical data, histopathology scores (0-3), glycogen concentrations and selected glycolytic enzyme activities compared. Coding sequences of 12 genes associated with muscle glycogenoses were identified from whole genome sequences and compared between seven PSSM2-QH and five control-QH. Exertional rhabdomyolysis in PSSM2-QH occurred predominantly in barrel racing and working cow/roping performance types and improved with regular exercise and a low starch/fat-supplemented diet. Histopathological scores, including the amount of amylase-resistant polysaccharide (PSSM2-QH 1.4 ± 0.6, PSSM1-QH 2.1 ± 0.3, control-QH 0 ± 0, p < 0.001), and glycogen concentrations (PSSM2-QH 129 ± 62, PSSM1-QH 175 ± 9, control-QH 80 ± 27 mmol/kg, p < 0.0001) were intermediate in PSSM2-QH with significant differences among groups. In PSSM2-QH, abnormal polysaccharide had a less filamentous ultrastructure than PSSM1-QH and phosphorylase and phosphofructokinase activities were normal. Seventeen of 30 PSSM2-QH with available pedigrees descended from one of three stallions within four generations. Of the 29 predicted high or moderate impact genetic variants identified in candidate genes, none were present in only PSSM2-QH and absent in control-QH. Analyses of PSSM2-QH and PSSM1-QH were performed on shipped samples, controls on frozen samples. PSSM2-QH is a novel glycogen storage disorder that is not the result of a mutation in genes currently known to cause muscle glycogenoses in other species.

Quantification of organic fluorophores in absorbing media by solid-phase fluorescence excitation–emission matrix (SPF-EEM) spectroscopy of modeled mixtures containing bovine serum albumin (BSA) and colorants

Solid-phase fluorescence excitation–emission matrix (SPF-EEM) spectroscopy is beneficial for investigating the characteristics of natural organic matter (NOM) in the solid phase without extraction procedures. However, inner filter effect (IFE) due to the presence of dark components in samples can make it difficult to quantify the fluorophore concentration. To establish a new method to determine unknown concentrations of a fluorescent material in a sample containing various absorbing materials by SPF spectroscopy, modeled mixtures containing bovine serum albumin (BSA) and colorants at different ratios were examined. Fluorescence intensities of BSA against various concentrations afforded different saturation curves for different colorants in the mixtures, suggesting that it is difficult to use the SPF intensity for quantifying the concentration of fluorescent samples in which IFE has occurred, because one cannot obtain a single calibration curve that does not depend on the absorbing medium that it is mixed in. However, products of the fluorescence intensity and Kubelka–Munk (KM) function at the excitation wavelength were proportional to the first order of BSA weight concentrations, regardless of the colorant type. By using this trend as a calibration curve, it may be possible to quantify the amount of BSA from its SPF-EEM spectrum. In this study, the KM function was obtained using an ultraviolet–visible (UV–Vis) spectrometer with an integrating sphere. To reduce the labor and equipment cost of UV–Vis spectroscopy, a substrate of the KM function also was obtained from the Rayleigh scattering in an SPF-EEM spectrum, which could be used as a parameter for calibration curves that quantify the BSA concentration. Although further studies are required, this study proposed that the product of the SPF intensity and KM function at the excitation wavelength can be partially used for an empirical formula to quantify a variety of fluorescent materials mechanically mixed with various absorbing materials.

Method Measures Effective Scale-Inhibitor Concentration at Low Detection Limit

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 209503, “A Fast and Accurate Method for Scale-Inhibitor Effective Concentration Measurement With Low Detection Limit,” by Xin Wang, Rice University; Zhaoyi Dai, SPE, China University of Geosciences; and Saebom Ko, Rice University, et al. The paper has not been peer reviewed. _ The accurate measurement of residual scale-inhibitor concentration in produced brine is essential for scale prevention. However, these scale inhibitors are effective at substoichiometric concentrations in most production conditions. It is difficult to measure such low inhibitor concentration with traditional methods, especially for non- or low-phosphorous polymeric scale inhibitors. In the complete paper, an assay method is developed to determine effective scale-inhibitor concentration. The presented assay method is quick, robust, and accurate, with a relative error of less than 10% even at 1 mg/L of inhibitor. Experimental Setup and Methods Brine Chemistry Inhibitor (BCIn) Continuous-Stirred-Tank Reactor (CSTR) Method. The schematic of the BCIn CSTR testing apparatus is shown in Fig. 1. The original supersaturated solution (OSS) with a designated barite scale inhibitor is injected into the reaction vial with no head space. This OSS may contain zero or a certain amount of scale inhibitor, and it will be stirred continuously and kept at room temperature during the whole test period. At Time 0, the cation titrant solution (TC) and the anion titrant solution (TA) start to be pumped continuously into the reaction vial with the same flow rate by using two syringe pumps. A green laser beam (with a wavelength of 532 nm) is shone through the reaction vial; light intensity is recorded continuously as current by using a multimeter with a photodiode detector in real time. The time between Time 0 and the solution turbidity change, which is detected as current drop, is defined as the critical time. Without the supply of scale inhibitor from the TC and TA solutions, the inhibitor concentration in the reaction vial decreases exponentially and the inhibitor concentration at Time t can be calculated. With continuous dilution of the inhibitor and continuous supply of the barite supersaturated solution in the reaction vial by TC and TA solution, the small nuclei and crystals begin to accumulate in the reaction vial, resulting in a turbidity change detected by the laser at the critical time. Because of the exponential decrease of the scale inhibitors, it is observed that, with the same solution composition and residence time, critical time increases linearly with the initial concentration of inhibitor. A series of tests is designed to match the composition of target brine with different amounts of scale inhibitors. The standard curve can then be developed. For the brine with an unknown amount of scale inhibitor, the inhibitor concentration can be calculated from the measured critical time. The known-additions method also can be used to determine the unknown inhibitor concentration in a sample. This process, and its associated equations, is provided in the complete paper, as is sample pretreatment.

Biotin-labelled peptidomimetic for competitive time-resolved fluoroimmunoassay of benzothiostrobin.

In recent years, more and more functional peptide ligands have been identified from phage display libraries and served the immunoassay of small molecules. After the identification, the phage particle instead limits further application of peptide ligands, so it is of great significance to explore the peptide ligand as an independent detection reagent. In this work, the identified peptidomimetic of benzothiostrobin was synthesized and labelled with biotin, which wascombined with Eu3+-labelled streptavidin to develop the peptide-based time-resolved fluoroimmunoassay (P-TRFIA). Under the optimal conditions, the half-maximum inhibitory concentration (IC50) of proposed P-TRFIA is 3.63ngmL-1, which is similar to the TRFIA using phage-borne peptidomimetic and Eu3+-labelled anti-phage antibody (IC50: 4.55ngmL-1), also more sensitive than previously reported immunoassays for benzothiostrobin. In addition, the proposed P-TRFIA shows excellent specificity and accuracy for analysis of spiked samples, and its detection results shows good consistency with high-performance liquid chromatography for the detection of environment and agro-products samples with unknown benzothiostrobin concentrations.

Smart Systems for Material and Process Designing in Direct Nanoimprint Lithography Using Hybrid Deep Learning.

A hybrid smart process and material design system for nanoimprinting is proposed, which is combined with a learning system based on experimental and numerical simulation results. Instead of carrying out extensive learning experiments for various conditions, the simulation learning results are partially complimented when the results can theoretically be predicted by numerical simulation. In other words, the data that are lacking in experimental learning are complimented by simulation-based learning results. Therefore, the prediction of nanoimprint results without experimental learning could be realized under various conditions, even for unknown materials. In this study, material and process designs are demonstrated for a low-temperature nanoimprint process using glycerol-containing polyvinyl alcohol. The experimental results under limited conditions were learned to investigate the optimum glycerol concentrations and process temperatures. Simulation-based learning was used to predict the dependence on press pressure and shape parameters. The prediction results for unknown glycerol concentrations agreed well with the follow-up experiments.

Electrochemical Behavior of Duloxetine Hydrochloride at Au and GC Solid Electrodes: Its Quantitative Determination and Degradation

Duloxetine hydrochloride (DUL) is effective in treating depression, and was examined for electroanalytical purposes. The DUL standard was investigated by cyclic voltammetry (CV) and determined using differential pulse voltammetry (DPV) via its electro-oxidation at the Au electrode in 0.05 M NaHCO3. DPV showed a linear dependency of the anodic peak currents vs DUL standard concentrations in the range from 0.1 to 3.33 μg ml−1 with the values of the limit of detection (LOD) and the limit of quantification (LOQ): 0.133 and 0.667 μg ml−1, respectively. Using the constructed and validated calibration curve, the values of unknown DUL concentrations in both Taita® tablets and in human serum spiked with the standard were determined. The number of protons and electrons transferred was calculated and possible reaction mechanisms taking place on the surface of both electrodes were proposed. The Au electrode exhibited a better sensitivity and a wider range of current vs concentration linear dependency for DUL quantitative determination than the GC electrode. The study of DUL degradation showed that at the Au and GC electrodes, after 4.5 h of potential cycling, degradation occurs, giving formaldehyde as a product, which was confirmed by high performance liquid chromatography (HPLC).

Highly sensitive PCF based plasmonic biosensor for hemoglobin concentration detection

The present study reports on the investigation carried out on a newly designed photonic crystal fiber (PCF) based plasmonic biosensor for label-free hemoglobin concentration detection. In the first part, a new approach is proposed to avail external sensing mechanism by introducing the solid core at fiber periphery and nanoscale gold film coating outside the fiber. By analyzing modal characteristics using finite element method (FEM), the wavelength sensitivity is found to be varied nonlinearly up to 34,800 nm/RIU within the analyte detection range of 1.34 − 1.41 RIU (refractive index unit). In later study, the proposed fiber is investigated as hemoglobin concentration sensor (HbCS) to detect unknown hemoglobin concentration. From the detail study, to calculate sensitivity of HbCS, a nonlinear equation as a function of hemoglobin concentration has been proposed to detect unknown concentration and a maximum sensitivity of 31.2 nm/g/dL is achieved. Moreover, the minimum measurable deviation of the sensor is found to be lies of the order of 10−6 with a high figure of merit (FOM) up to 229 RIU−1 representing an overall good sensing performance. The proposed plasmonic sensor provides high sensitivity, good specificity, environmental stability along with high resolution compare to other existing technologies. Therefore, this study opens an easy and reliable window for hemoglobin concentration detection in biomedical applications.

Developing a fluorescent sensing based portable medical open-platform - a case study for albuminuria measurement in chronic kidney disease screening and monitoring

In this work, a portable medical platform is developed to monitor fluorescence assay for in-field and point of care (POC) analysis of albumin levels in urine. The platform can rapidly and efficiently measure the fluorescence intensity yielded from the mixture of recognising reagent and the biomarker. After mixing with the recognising reagent and placing under a certain optical excitation wavelength, the sample containing the target biomarker emits a fluorescent signal, which is captured by a Raspberry Pi Camera and analysed in real time to evaluate the Red Green Blue (RGB) values of the image and give an estimation of the biomarker concentration in the sample. Each biomarker, in testing for fluorescence, requires different excitation wavelengths and sensitivities for detection. The developed platform has been designed with flexibility for customization to suit the different requirements of various fluorescence reactions. In this study, we perform two stages of evaluating the device. In stage 1, we demonstrate the developed platform can detect known concentrations of albumin in artificial urine samples by using different recognising reagents. In stage 2, we confirm the developed platform can detect unknown concentrations of albumin in patients' urine samples and provide comparative accuracy results compared with current laboratory testing results. This study provides proof of concept that the developed device can measure the concentration of different biomarkers if the biomarker detection follows the rules of fluorescence sensing.

Experimental validation of an interval observer-based sensor fault detection strategy applied to a biohydrogen production dark fermenter

In this paper, the problem of designing an interval observer applied to a biohydrogen production dark fermenter is addressed, in order to: (i) estimate the glucose and biomass concentrations, and the hydrogen flow rate produced, (ii) reduce the influence of the unknown inlet glucose concentration and the model uncertainties, and (iii) detect the presence of faults in the hydrogen flow rate sensor. Based on literature reports, we propose an interval observer structure, constituted by a Luenberger observer and an interval predictor, for a class of linear systems. The interval observer offers three estimations: the upper and lower estimations provided by the interval predictor, and one estimation given by the Luenberger observer. The upper and lower estimations provide bounds at any instant of time for the state vector of the perturbed/uncertain biohydrogen production bioreactor model from an appropriate initial condition. The design conditions presented in this work are based on control H∞ in combination with pole placement and polytopic parametric uncertainties. The observer design is proposed as a semi-definite optimization problem subject to Linear Matrix Inequalities, which do not depend on the cooperativity property. Based on the proposed observer structure, we also present two fault detection schemes for the same class of uncertain systems to detect the occurrence of sensor faults: one makes use of the logic comparison between the set threshold and the residual produced by the Luenberger observer; the other considers adaptive thresholds for the output signal by using the upper and lower estimates of the interval predictor. Finally, the proposed approaches are validated with experimental data from a biohydrogen production dark fermenter.

Novel insights into prehistoric land use at Stonehenge by combining electromagnetic and invasive methods with a semi-automated interpretation scheme

Geophysical survey methods have led to high-resolution mapping of subsurface remnants of ancient landscapes at continually expanding spatial scales. Yet, particularly when applied across entire archaeological landscapes, spanning hundreds of hectares, resultant datasets provide little direct information about inhabitation, environments or change over time.Focusing on a 2.5 km2 area around Stonehenge, we show how geophysical soil survey, when combined with targeted sampling and excavation, can enable reliable empirically-grounded identification of complex activity traces. Particular focus lies on anthropogenic dug pits, identification and interpretation of which are vital in European earlier prehistoric archaeology due to their close connection with inhabitation and ceremonial practices. By integrating frequency domain electromagnetic and invasive datasets, and using a semi-automated interpretation scheme, we identified previously unknown concentrations of large pits (with diameters >2.4 m) among several thousand smaller pits and natural features across the Stonehenge landscape. Excavations of a subset of identified features demonstrate that, in this area, our investigative methodology is 66% accurate for identifying large anthropogenic pits. Our results have significant implications for understanding Stonehenge and its landscape setting, revealing elusive forms of Mesolithic to later Bronze Age land use that - even within the world's most intensively researched archaeological landscape - have gone unrecognized until now.These findings underscore both the crucial role of archaeological excavation as an essential basis for reliable interpretation of geophysical data, as well as the perils of inductive visual interpretation of features’ morphologies and their spatial configuration in non-invasive survey data.

Correlates of concurrent use of stimulants and opioids among people who access harm reduction services in British Columbia, Canada: Findings from the 2019 Harm Reduction Client Survey.

During a public health overdose emergency driven by fentanyl in British Columbia (BC), Canada, a parallel increase in concurrent use of methamphetamines may be contributing to the risk of overdose. Concurrent use refers to the use of stimulants and opioids one after the other, or together. Describing substance use practices and elucidating correlates of concurrent use can inform harm reduction service provision and prevent overdose events. This cross-sectional study analyzed the 2019 Harm Reduction Client Survey administered at 22 harm reduction sites across BC and sampled individuals aged 19 and older. Bivariable and multivariable logistic regression models identified correlates of concurrent use of stimulants and opioids in the last three days. The study sample was composed of 574 individuals who used stimulants and/or opioids, among whom 307 (53.5%) reported concurrent use. Compared with individuals who used stimulants and/or opioids separately, the odds of concurrent use were significantly increased (OR=2.74, 95%CI 1.71-4.51) while the adjusted odds were increased (aOR=1.79, 95%CI 0.98-3.34) among individuals who experienced an opioid overdose. Further, the adjusted odds of concurrent use decreased with every one-year increase in age (aOR=0.97, 95%CI 0.95-0.99), among individuals with paid work (aOR=0.58, 95%CI 0.33-0.99), and stable housing (aOR=0.61, 95%CI 0.37-1.02). The adjusted odds of concurrent use increased among individuals who used drugs daily (aOR=3.78, 95%CI 2.28-6.40), used tobacco (aOR=2.03, 95%CI 1.09-3.87), used benzodiazepines (aOR=3.72, 95%CI 1.76-8.41), owned a naloxone kit (aOR=1.94, 95%CI 1.15-3.31), used observed consumption sites (aOR=2.51, 95%CI 1.57-4.07), and were prescribed opioid agonist therapy (aOR=2.92, 95%CI 1.81-4.77). Individuals who used stimulants and opioids concurrently tend to be younger, without paid work or housing. They were engaged in harm reduction and treatment services, yet used illicit drugs with unknown concentration daily. Improving access to age-appropriate services and expanding the availability of both legal and regulated stimulants and opioids may further reduce harms of concurrent use.

Conventional versus Extended Standard Addition Method: Determination of Capsaicinoids in Topical Creams by High-Performance Liquid Chromatography – Diode Array Detection (HPLC-DAD)

External standard calibration is the most common approach used to calculate the unknown concentration of analytes in analytical procedures. However, this method is accurate only if the matrix can be reproduced in the standard samples or if there are no significant interferences in the sample. A well-known calibration method that was proposed especially for the analysis of samples with matrix effects is conventional standard addition method (SAM) which implies adding known and increasing quantities of the analyte to portions of the sample. Despite its benefit of constant matrix effect in the calibration standards, SAM has been frequently shown to be a suboptimal approach with flaws that include extrapolation prediction and thus wider confidence intervals and non-linearity at lower concentrations. Recently, new approaches have been proposed to overcome these shortcomings, including extension of the SAM by blank addition, a geometric alternative for interpolation, and even a new mathematical alternative. Herein, we applied an alternative to extend the SAM method, for semisolid samples with unavailable blank, by adding more samples with lower mass than the unknown in the SAM, and thus extending measurable points below the intercept. This approach was applied for capsaicinoid determination in topical creams after liquid-liquid microextraction, followed by high performance liquid chromatography with diode array detection (HPLC-DAD). Microextraction of the capsaicinoids was optimized in terms of pH, solvent type, vortexing and extraction times. The method appeared to significantly improve the precision, limit of detection, and lead to reduced bias compared to conventional SAM.

Internal Standard an Important Analyte Use in Drug Analysis by Liquid Chromatography Mass Spectrometry- An Article

Internal standard is an external compound which is mixed with targeted analytical solution and matrix as a constant concentration and use for preparing calibration standard curve by using ratio of analyte area and internal standard area with analyte concentration and internal standard concentration. This calibration curve used for quantification of unknown concentration of anlayte of interest. This article provide necessary information about internal standard like its selection procedure, characterization, types and response factor , to all analyst who are connected with drug analysis. This article is more important and I think first article which focuses a clear idea about internal standard use in drug analysis.

Precise determination of 244Cm by 244Cm/239Pu alpha ratio measurements

This work describes a method developed for the precise determination of Cm in a solution. The method is based on accurate spiking of a 244Cm solution having a known isotopic vector but unknown concentration, with a Pu reference standard.

Gas chromatography method to quantify α-monobenzoate glycerol in methanolic solutions using methyl benzoate as internal standard and a Rxi-1ms column

The 2,3-dihydroxypropyl benzoate, also known as α-monobenzoate glycerol (α-MBG), is an important building block used in the organic chemistry industry and its synthesis can be performed by the direct esterification of glycerol and benzoic acid. On the other hand, gas chromatography is a powerful laboratory tool applied for the quantification of known and unknown molar concentrations of chemical compounds. Under those conditions, I developed a method and a calibration curve for the analysis of unknown concentrations of α-MBG in methanolic solutions using a gas chromatograph equipped with a Rxi-1ms column and methyl benzoate (MBe) as an internal standard. Thus, by a suitable chromatograph setup, such as injector temperature and column oven temperature program, it was achieved a good resolution of the chromatographic peaks of each individual compound (MBe and α-MBG). Additionally, it was possible to generate an appropriated calibration curve with a corresponding equation for the calculation of unknow molar concentrations of α-monobenzoate glycerol in methanolic solutions. The highlights of the method described are:•There were no derivatization steps in this method, only dilution of the α-monobenzoate glycerol in methanol.•The calibration curve was made using only four calibration mixtures.•This method also promotes the appropriate separation of glycerol, benzoic acid, and methyl benzoate, which can be useful for the analysis of the progress of an esterification reaction of glycerol and benzoic acid for the synthesis of α-MBG.

QUANTIFICATION OF TOTAL PROTEIN CONTENT IN SHIGRU (MORINGA OLEIFERA LAMM) BY LOWRY’S METHOD

Introduction: Shigru is truly a miracle plant, and a divine gift for the nourishing and healing of man. This plant has so many uses and special features, the leaves are rich in sources of vitamins (especially Vitamin A, B and C), minerals (calcium, iron) and protein due its versatile effect shigru is truly miracle plant and divine gift for nourishing and healing the man. Protein is an important primary metabolite of our living system the primary objective of this study is to estimate protein contain in Shigru. Materials &amp; Methods: Aims &amp; Objectives: Estimation of total protein content by Lowry’s method. The protein content was estimated by UV-Spectrophotometric technique using conventional lowry’s method. Bovine serum albumin(BSA) was used as a standard reagent against the unknown protein concentration of shigru had been estimated. All the protein were extracted by using phosphate buffer (pH 7.4) Results: it has been found that protein content in shigru is 10.93 mg BSA E/g of FW protein content. Conclusion: the study finding concluded that the protein content obtained from leaves of Shigru