Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

video

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

sound

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Version

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

View:

226
Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Download:

271
Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Cites:

Information Journal Paper

Title

Least-Squares Support Vector Machine and its Application in the Simultaneous Quantitative Spectrophotometric Determination of Pharmaceutical Ternary Mixture

Pages

  25-36

Abstract

 This paper proposes the least-squares support vector machine (LS-SVM) as an intelligent method applied on absorption spectra for the simultaneous determination of Paracetamol (PCT), Caffeine (CAF), and Ibuprofen (IB) in Novafen. The signal to noise ratio (S/N) increased. Also, In the LS-SVM model, Kernel parameter (𝜎 2) and capacity factor (C) were optimized. Excellent prediction was shown using LS-SVM, with lower root mean square error (RMSE) and relative standard deviation (RSD). In addition, Regression coefficient (R2), correlation coefficient (r), and mean recovery (%) of this method obtained for PCT, CAF, and IB. LS-SVM / spectrophotometry method is reliable for simultaneous quantitative analysis of components in commercial samples. The results obtained from analyzing the real sample by the proposed method compared to the high-performance liquid chromatography (HPLC) as a reference method. One-way analysis of variance (ANOVA) test at 95% confidence level used and results showed that there was no significant difference between suggested and reference methods.

Cites

  • No record.

    • References

    Cite

    APA: Copy

    Mofavvaz, Shirin, SOHRABI, MAHMOUD REZA, Sahebi Farhad, Shiva, & NEZAMZADEH EJHIEH, ALIREZA. (2018). Least-Squares Support Vector Machine and its Application in the Simultaneous Quantitative Spectrophotometric Determination of Pharmaceutical Ternary Mixture. IRANIAN JOURNAL OF PHARMACEUTICAL SCIENCES, 14(3), 25-36. SID. https://sid.ir/paper/300405/en

    Vancouver: Copy

    Mofavvaz Shirin, SOHRABI MAHMOUD REZA, Sahebi Farhad Shiva, NEZAMZADEH EJHIEH ALIREZA. Least-Squares Support Vector Machine and its Application in the Simultaneous Quantitative Spectrophotometric Determination of Pharmaceutical Ternary Mixture. IRANIAN JOURNAL OF PHARMACEUTICAL SCIENCES[Internet]. 2018;14(3):25-36. Available from: https://sid.ir/paper/300405/en

    IEEE: Copy

    Shirin Mofavvaz, MAHMOUD REZA SOHRABI, Shiva Sahebi Farhad, and ALIREZA NEZAMZADEH EJHIEH, “Least-Squares Support Vector Machine and its Application in the Simultaneous Quantitative Spectrophotometric Determination of Pharmaceutical Ternary Mixture,” IRANIAN JOURNAL OF PHARMACEUTICAL SCIENCES, vol. 14, no. 3, pp. 25–36, 2018, [Online]. Available: https://sid.ir/paper/300405/en

    Related Journal Papers

  • No record.

    • Related Seminar Papers

  • No record.

    • Related Plans

  • No record.

    • Recommended Workshops






    Move to top