Simultaneous determination of ergotamine, caffeine and dipyrone in their ternary mixture by applying double divisor and first derivative ratio spectra methods

Ayman, Aya; Zeid, Abdallah M.; Wahba, M. E. K.; El-Shabrawy, Yasser

doi:10.1016/j.pharma.2022.01.003

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…Raw spectral data are susceptible to environmental interference, so the original spectral data needed to be processed using pretreatment methods, including Savitzky-Golay convolutional smoothing (SG) [20,21], multiple scattering correction (MSC)), logarithmic transformation (LG) [22], first derivative (FD) [23], second derivative (SD) [24], and combinations of two or three single pretreatment methods (SG + MSC, SG + SNV, SG + MSC + SD, SG + MSC + FD, SG + SNV + FD, SG + SNV + SD). The raw spectral data were preprocessed, and the effects of each pretreatment method were analyzed using trial-and-error comparison.…”

Section: Near-infrared Spectroscopy Data Preprocessingmentioning

confidence: 99%

Quantitatively Determine the Iron Content in the Fruit of ‘Huangguan’ Pear Using Near-Infrared Spectroscopy

Fang

et al. 2023

Horticulturae

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‘Huangguan’ pear has excellent quality, strong adaptability, and good socioeconomic value. Iron is one of the important trace elements in plants, and iron imbalance seriously affects the growth and development of pear trees and reduces their economic benefits. If the iron content in pear fruit can be easily and non-destructively detected using modern technology during the critical period of fruit development, it will undoubtedly help guide actual production. In this study, ‘Huangguan’ pear fruit was used as the research object, and the possibility of using the more convenient near-infrared spectroscopy (900~1700 nm) technology for nondestructive detection of the iron content in the peel and pulp of ‘Huangguan’ pear was explored. First, 12 algorithms were used to preprocess the original spectral data, and based on the original and the preprocessed spectral data, partial least squares regression and gradient boosting regression tree algorithms were used. A full-band prediction model of the iron content in the peel and pulp of ‘Huangguan’ pear was established, and the genetic algorithm was used to extract characteristic wavelengths, establish a characteristic wavelength prediction model, and evaluate the prediction effect of each model according to the coefficient of determination R² and the relative analysis error RPD. After comparison, we found that the prediction model with the best prediction of the iron content in the peel and pulp of ‘Huangguan’ pear reaches class A, and the prediction effect is good and meets expectations. This experiment shows that the use of near-infrared spectroscopy can achieve better prediction of the iron content in the peel and pulp of ‘Huangguan’ pear.

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Section: Near-infrared Spectroscopy Data Preprocessingmentioning

confidence: 99%

Quantitatively Determine the Iron Content in the Fruit of ‘Huangguan’ Pear Using Near-Infrared Spectroscopy

Fang

et al. 2023

Horticulturae

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Advanced Approaches in Green Univariate Spectrophotometric Methods

Lotfy,

Saleh,

Rostom

et al. 2023

Sustainable Approaches in Pharmaceutical Sciences

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TLC densitometric approach for concurrent determination of quinary mixture for treatment of migraine with appraisal to method greenness and whiteness

Sharkawi,

Ahmed,

Farid

et al. 2024

Sci Rep

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A new accurate, precise thin layer chromatographic (TLC densitometric) approach was developed for the simultaneous analysis of ergotamine tartrate (ERG), phenobarbital sodium (PHEN), caffeine anhydrous (CAF), dipyrone sodium (DIP) and meprobamate (MEP) in their pure form and in their combined formulation; (MIGRANIL tablets). TLC separation depended on using a stationary phase of TLC plates F 254 (20 × 10 cm) and using a developing system of ethyl acetate: methanol: n- hexane (8:2:3, by volume) with UV scanning at 254 nm for ERG, PHEN, CAF and DIP. For MEP, detection was done at 560 nm after spraying with 0.3% ethanolic solution of para -dimethylaminobenzaldehyde (PDAB) and heating at 110 ºC for 30 min. Analysis was performed in the concentration intervals of 0.06-2, 0.15–5 µg/band for ERG and PHEN, respectively; while for CAF, DIP and MEP it was done in the ranges of 0.3-9, 0.5–10, 1–100 µg/band, respectively. Moreover, the method was used for the quantitation of the five studied drugs in their unique formulation and the recovery% was calculated and found to be 102.04, 100.73, 100.65, 99.91, 1nd 101.77% for ERG, PHEN, CAF, DIP, and MEP, in order and all these outcomes proved excellent selectivity of the method. Other validation parameters were tested following ICH guidelines. Accuracy ranged from 98.02 to 100.53% while the obtained %RSD of interday day precision ranged from 1.215 to 1.940%. The environmental impact and performance of the method was tested with several tools; Analytical eco-scale, green analytical procedure index (GAPI), Analytical GREEnness Metric Approach (AGREE), Blue applicability grade index (BAGI), and RGB additive color model. Results ensured minimum hazardous effect on health and environment. This is the first analytical approach developed for simultaneous analysis of the quinary mixture which can be easily applied in any analytical laboratory. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-024-79518-5.

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Simultaneous determination of ergotamine, caffeine and dipyrone in their ternary mixture by applying double divisor and first derivative ratio spectra methods

Cited by 3 publications

References 9 publications

Quantitatively Determine the Iron Content in the Fruit of ‘Huangguan’ Pear Using Near-Infrared Spectroscopy

Quantitatively Determine the Iron Content in the Fruit of ‘Huangguan’ Pear Using Near-Infrared Spectroscopy

Advanced Approaches in Green Univariate Spectrophotometric Methods

TLC densitometric approach for concurrent determination of quinary mixture for treatment of migraine with appraisal to method greenness and whiteness

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