scholarly journals MATLAB software for automated calculation of concentration of different compounds using extracted peak area from HPLC data file in pdf format

2019 ◽  
Author(s):  
Wenfa Ng
Keyword(s):  
Retention Time ◽  
Data File ◽  
Calibration Plot ◽  
Target Compound ◽  
Matlab Software ◽  
Ease Of Access ◽  
Program Output ◽  
Pdf Format ◽  
Area Data ◽  
Hplc Data

Chromatograms represent a class of data difficult to process expeditiously due to the large number of intermediary steps necessary to translate peak detection to a concentration reading of a specific compound. This problem is further exacerbated by the different output file format in which instrument manufacturers present chromatographic data. Steps necessary to convert a detected peak to a concentration reading include identification of compound using retention time, extraction of corresponding peak area, and calculation of concentration of compound by using a calibration curve. This work sought to develop a MATLAB software able to automatically extract peak area from chromatographic readout captured in pdf format and calculate the corresponding concentration values. Given manufacturer-specific formatting features in pdf file, the MATLAB software could only read and handle pdf files of HPLC readouts from Shimadzu’s LabSolutions software. In processing the pdf file of each analyzed sample, entire content of the file was first read as a character string. Subsequently, specific delimiters were used to extract retention time and detected peak area for each compound. This information was subsequently processed to identify specific target compound of interest, where extracted peak area was used to calculate concentration of compound using a calibration plot. Overall, the program generates a database comprising filename, raw retention time and peak area data, as well as concentration values of each target compound in an easy to read format. Finally, to provide ease of access and a permanent file for storage, the program output the above database as an Excel file stored on the hard drive. One important advantage of this software is that it could process multiple pdf files simultaneously and there is no upper limit to the number of pdf files (or samples) that could be processed. Collectively, the MATLAB software capable of automatically extracting peak area and calculating concentration of different compounds would provide significant savings of time in handling large number of pdf files in a typical chromatographic run from a Shimadzu HPLC instrument.

scholarly journals MATLAB software for automated calculation of concentration of different compounds using extracted peak area from HPLC data file in pdf format

2019 ◽  
Author(s):  
Wenfa Ng
Keyword(s):  
Retention Time ◽  
Data File ◽  
Calibration Plot ◽  
Target Compound ◽  
Matlab Software ◽  
Ease Of Access ◽  
Program Output ◽  
Pdf Format ◽  
Area Data ◽  
Hplc Data

Chromatograms represent a class of data difficult to process expeditiously due to the large number of intermediary steps necessary to translate peak detection to a concentration reading of a specific compound. This problem is further exacerbated by the different output file format in which instrument manufacturers present chromatographic data. Steps necessary to convert a detected peak to a concentration reading include identification of compound using retention time, extraction of corresponding peak area, and calculation of concentration of compound by using a calibration curve. This work sought to develop a MATLAB software able to automatically extract peak area from chromatographic readout captured in pdf format and calculate the corresponding concentration values. Given manufacturer-specific formatting features in pdf file, the MATLAB software could only read and handle pdf files of HPLC readouts from Shimadzu’s LabSolutions software. In processing the pdf file of each analyzed sample, entire content of the file was first read as a character string. Subsequently, specific delimiters are used to extract retention time and detected peak area for each compound. This information was subsequently processed to identify specific target compound of interest, where extracted peak area was used to calculate concentration of compound using a calibration plot. Overall, the program generates a database comprising filename, raw retention time and peak area data, as well as concentration values of each target compound in an easy to read format. Finally, to provide ease of access and a permanent file for storage, the program output the above database as an Excel file stored on the hard drive. One important advantage of this software is that it could process multiple pdf files simultaneously and there is no upper limit to the number of pdf files (or samples) that could be processed. Collectively, the MATLAB software capable of automatically extracting peak area and calculating concentration of different compounds would provide significant savings of time in handling large number of pdf files in a typical chromatographic run from a Shimadzu HPLC instrument.

scholarly journals MATLAB software for automated calculation of concentration of different compounds using extracted peak area from HPLC data file in pdf format

2019 ◽  
Author(s):  
Wenfa Ng
Keyword(s):  
Retention Time ◽  
Data File ◽  
Calibration Plot ◽  
Target Compound ◽  
Matlab Software ◽  
Ease Of Access ◽  
Program Output ◽  
Pdf Format ◽  
Area Data ◽  
Hplc Data

Chromatograms represent a class of data difficult to process expeditiously due to the large number of intermediary steps necessary to translate peak detection to a concentration reading of a specific compound. This problem is further exacerbated by the different output file format in which instrument manufacturers present chromatographic data. Steps necessary to convert a detected peak to a concentration reading include identification of compound using retention time, extraction of corresponding peak area, and calculation of concentration of compound by using a calibration curve. This work sought to develop a MATLAB software able to automatically extract peak area from chromatographic readout captured in pdf format and calculate the corresponding concentration values. Given manufacturer-specific formatting features in pdf file, the MATLAB software could only read and handle pdf files of HPLC readouts from Shimadzu’s LabSolutions software. In processing the pdf file of each analyzed sample, entire content of the file was first read as a character string. Subsequently, specific delimiters were used to extract retention time and detected peak area for each compound. This information was subsequently processed to identify specific target compound of interest, where extracted peak area was used to calculate concentration of compound using a calibration plot. Overall, the program generates a database comprising filename, raw retention time and peak area data, as well as concentration values of each target compound in an easy to read format. Finally, to provide ease of access and a permanent file for storage, the program output the above database as an Excel file stored on the hard drive. One important advantage of this software is that it could process multiple pdf files simultaneously and there is no upper limit to the number of pdf files (or samples) that could be processed. Collectively, the MATLAB software capable of automatically extracting peak area and calculating concentration of different compounds would provide significant savings of time in handling large number of pdf files in a typical chromatographic run from a Shimadzu HPLC instrument.

scholarly journals ProteoCat: a tool for planning of proteomic experiments

2015 ◽  
Vol 61 (6) ◽  
pp. 770-776 ◽  
Author(s):  
V.S. Skvortsov ◽  
N.N. Alekseychuk ◽  
D.V. Khudyakov ◽  
A.V. Mikurova ◽  
A.V. Rybina ◽  
...  
Keyword(s):  
User Interface ◽  
Amino Acid ◽  
Graphical User Interface ◽  
Retention Time ◽  
Large Scale ◽  
Data File ◽  
Mass Spectrometric ◽  
Amino Acid Sequences ◽  
Peptide Fragments ◽  
Peptide Detection

ProteoCat is a computer program has been designed to help researchers in the planning of large-scale proteomic experiments. The central part of this program is the subprogram of hydrolysis simulation that supports 4 proteases (trypsin, lysine C, endoproteinases AspN and GluC). For the peptides obtained after virtual hydrolysis or loaded from data file a number of properties important in mass-spectrometric experiments can be calculated or predicted. The data can be analyzed or filtered to reduce a set of peptides. The program is using new and improved modification of our methods developed to predict pI and probability of peptide detection; pI can also be predicted for a number of popular pKa's scales, proposed by other investigators. The algorithm for prediction of peptide retention time was realized similar to the algorithm used in the program SSRCalc. ProteoCat can estimate the coverage of amino acid sequences of proteins under defined limitation on peptides detection, as well as the possibility of assembly of peptide fragments with user-defined size of “sticky” ends. The program has a graphical user interface, written on JAVA and available at http://www.ibmc.msk.ru/LPCIT/ProteoCat.

scholarly journals Determination of Diazepam in Cream Biscuits by Liquid Chromatography

2004 ◽  
Vol 87 (3) ◽  
pp. 569-572 ◽  
Author(s):  
Priyankar Ghosh ◽  
Mudiam Mohanakrishna Reddy ◽  
Beedu Sashidhar Rao ◽  
Rajendra Kumar Sarin
Keyword(s):  
Retention Time ◽  
Mobile Phase ◽  
Limit Of Detection ◽  
Linear Regression Analysis ◽  
Internal Standard ◽  
Reversed Phase ◽  
Calibration Plot ◽  
Relative Standard ◽  
Limit Of Quantitation

Abstract An analytical procedure was developed for the detection and quantitation of diazepam in cream biscuits, which were used to commit crime. The method involves the extraction of diazepam with ethanol at room temperature, and the extract is filtered, evaporated to dryness, and redissolved in the mobile phase, methanol–acetonitrile–tetrahydrofuran–water (15 + 55 + 4 + 26, v/v). The separation is achieved on a C18 reversed-phase column with the mobile phase and diode array detection (λmax) at 230 nm. Medazepam is used as the internal standard is for quantification. The calibration plot for the determination of diazepam is based on linear regression analysis (y = 0.6687x + 0.0372; r2 = 0.995). The limit of detection for diazepam in the biscuit samples was estimated as 600 ng/mL. The limit of quantitation for diazepam was estimated as 1.75 μg/mL. The diazepam detected per piece of biscuit was found to be in the range of 0.27–0.45 mg. Pure diazepam was added to biscuit samples at 3 levels (100 and 500 μg/g, and 1 mg/g), and the recoveries were found to be 95%. The mean retention time of diazepam was 2.7 min and that of medazepam (IS) was 4 min. The relative standard deviations of the diazepam level in the biscuit samples were estimated to be 0.4% for retention time and 1.02% for peak area in intraday analysis, whereas the corresponding values were and 0.61 and 2.34% in interday analysis. The method is rapid and reliable for qualitative and quantitative analysis of cream biscuits laced with diazepam, and it can be used by law enforcement laboratories for routine analysis.

scholarly journals Development and Validation of an HPLC Method for Determination of Antidiabetic Drug Alogliptin Benzoate in Bulk and Tablets

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Hani Naseef ◽  
Ramzi Moqadi ◽  
Moammal Qurt
Keyword(s):  
Retention Time ◽  
Hplc Method ◽  
Calibration Plot ◽  
Pharmaceutical Dosage Forms ◽  
Dipeptidyl Peptidase 4 ◽  
Carbonate Buffer ◽  
Dipeptidyl Peptidase 4 Inhibitors ◽  
Run Time ◽  
Development And Validation

Alogliptin benzoate, a member of dipeptidyl peptidase-4 inhibitors, is a recent drug developed by Takeda Pharmaceutical Company for the treatment of Type 2 diabetes; it potentiates the effect of incretin hormones through the inhibition of their degradation. Alogliptin can be used alone or in combination therapy. A new sensitive and rapid HPLC method was developed for the determination of alogliptin benzoate in bulk and pharmaceutical dosage forms; it was validated according to ICH and FDA guidelines. The HPLC analysis was performed on the Agilent 1200 system equipped with a Hypersil Gold Thermo Scientific C18 (250 cm × 4.6 mm) 5 µm column, with a mixture of acetonitrile and ammonium carbonate buffer in the ratio of 55 : 45 v/v as the mobile phase, at the flow rate of 1.0 mL/min. The detection was performed at the wavelength (λ) of 277, and the retention time of alogliptin benzoate was around 4 min. The total run time was 6.0 min. The calibration plot gave linear relationship over the concentration range of 85–306 µg/ml. The LOD and LOQ were 0.03 and 0.09 μg, respectively. The accuracy of the proposed method was determined by recovery studies and was found to be 100.3%. The repeatability testing for both standard and sample solutions showed that the method is precise within the acceptable limits. RSD% of the determination of precision was <2%. The results of robustness and solutions stability studies were within the acceptable limits as well. The proposed method showed excellent linearity, accuracy, precision, specificity, robustness, LOD, LOQ, and system suitability results within the acceptance criteria. In addition, the main features of the developed method are low run time and retention time around 4 min.

A Second Generation Automated Powder Diffractometer Control System

1981 ◽  
Vol 25 ◽  
pp. 245-260 ◽  
Author(s):  
Robert L. Snyder ◽  
Camden R. Hubbard ◽  
Nicolas C. Panagiotopoulos
Keyword(s):  
Control System ◽  
Data Collection ◽  
Instrumental Variables ◽  
Statistical Error ◽  
Data File ◽  
X Ray ◽  
Powder Diffractometer ◽  
Data Collection And Analysis ◽  
File Structure ◽  
Area Data

AbstractThe real-time x-ray powder diffractometer control system AUTO incorporates several advances in data collection and analysis. Counting procedures for selected area data collection are optimized to achieve either a preselected statistical error in minimum time or a minimum error in fixed total time. Run files are employed to greatly simplify quantitative analysis procedures and for controlling repetitive runs. External calibration curves for 20 are used to eliminate all but sample dependent aberrations to peak positions. A generalized data file structure is used to document the instrumental variables and sample parameters.

A Study on the Farmers’ Awareness and Acceptance of Biofertilizers in Kottayam District

GIS Business ◽  
2019 ◽  
Vol 14 (6) ◽  
pp. 425-431
Author(s):  
Subin Thomas ◽  
Dr. M. Nandhini
Keyword(s):  
Organic Matter ◽  
Nitrogen Fixation ◽  
Plant Growth ◽  
Plant Growth Promoting Rhizobacteria ◽  
Nutrient Cycle ◽  
Plant Growth Promoting ◽  
Promote Plant Growth ◽  
Growth Promoting ◽  
Structured Questionnaire ◽  
Area Data

Biofertilizers are fertilizers containing microorganisms that promote plant growth by improving the supply of nutrients to the host plant. The supply of nutrients is improved naturally by nitrogen fixation and solubilizing phosphorus. The living microorganisms in biofertilizers help in building organic matter in the soil and restoring the natural nutrient cycle. Biofertilizers can be grouped into Nitrogen-fixing biofertilizers, Phosphorous-solubilizing biofertilizers, Phosphorous-mobilizing biofertilizers, Biofertilizers for micro nutrients and Plant growth promoting rhizobacteria. This study conducted in Kottayam district was intended to identify the awareness and acceptance of biofertilizers among the farmers of the area. Data have been collected from 120 farmers by direct interviews with structured questionnaire.

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