Analytical Method Development and Validation Analysis for Quantitative Assessment of Thifluzamide by HPLC Procedure
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2022.13.1.21Keywords:
Thifluzamide , Robust, Precision, Linearity and Stability.Dimensions Badge
Issue
Section
License
Copyright (c) 2022 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The precise, systematic, explicit, particular, linear, exact and robust scientific method was developed and validated for the assay of Thifluzamide in THIFLUZAMIDE 24% SC(CILPYROX) fungicide. Presently utilized Thifluzamide as a working standard having limit f assay of Thifluzamide in THIFLUZAMIDE 24% SC (CILPYROX) fungicide are not less than 95.0%. Acetonitrile, water and Phosphoric acid in the ratio (60:40:0.1 v/v/v) used as mobile phase and flow rate 1.0 ml / min. with 10 minutes run time. The detection was carried at 230 nm with column c18 - 250mm x 4.6mm x 5μ and ambient column temperature was maintained. The linearity of this method was found to be linear with a coefficient of regression at 0.999 in the concentration range of 50% to 150%. The linear regression equation was y=2174x-135.8. The present developed HPLC method is detected to be suitable. The analytical solution was detected to be stable up to 48 Hrs at room temperature.Abstract
How to Cite
Downloads
Similar Articles
- Y. Mohammed Iqbal, M. Mohamed Surputheen, S. Peerbasha, Swarm intelligence-driven HC2NN model for optimized COVID-19 detection using lung imaging , The Scientific Temper: Vol. 16 No. 03 (2025): The Scientific Temper
- Balaji V, Purnendu Bikash Acharjee, Muniyandy Elangovan, Gauri Kalnoor, Ravi Rastogi, Vishnu Patidar, Developing a semantic framework for categorizing IoT agriculture sensor data: A machine learning and web semantics approach , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Purnendu B. Acharjee, Bhupaesh Ghai, Muniyandy Elangovan, S. Bhuvaneshwari, Ravi Rastogi, P. Rajkumar, Exploring AI-driven approaches to drug discovery and development , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- N. Saranya, M. Kalpana Devi, A. Mythili, Summia P. H, Data science and machine learning methods for detecting credit card fraud , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Neerav Nishant, Nisha Rathore, Vinay Kumar Nassa, Vijay Kumar Dwivedi, Thulasimani T, Surrya Prakash Dillibabu, Integrating machine learning and mathematical programming for efficient optimization of electric discharge machining technique , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Gomathi Ramalingam, Logeswari S, M. D. Kumar, Manjula Prabakaran, Neerav Nishant, Syed A. Ahmed, Machine learning classifiers to predict the quality of semantic web queries , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- K. Gokulkannan, M. Parthiban, Jayanthi S, Manoj Kumar T, Cost effective cloud-based data storage scheme with enhanced privacy preserving principles , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Milindkumar N. Dandale, Amar P. Yadav, P. S. K. Reddy, Seema G. Kadu, Madhusudana T, Manthan S. Manavadaria, Deep learning enhanced drug discovery for novel biomaterials in regenerative medicine utilizing graph neural network approach for predicting cellular responses , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- Suresha S, Corporate bonds vis-a-vis bond market: Global economy , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Naveena Somasundaram, Vigneshkumar M, Sanjay R. Pawar, M. Amutha, Balu S, Priya V, AI-driven material design for tissue engineering a comprehensive approach integrating generative adversarial networks and high-throughput experimentation , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.

