Issue 4, 2014

Rapid and sensitive determination of ambroxol hydrochloride injection by Raman spectroscopy combined with chemometric models

Abstract

A procedure for the quantitative determination of ambroxol hydrochloride in its commercial injection based on the conjunction of Raman spectroscopy and chemometrics is described. The active component constitutes less than 1% (w/v) of the injection in our study. The efficiency of various spectra treatment procedures, including classical least squares (CLS), partial least squares (PLS), principal component regression (PCR) and stepwise multiple linear regression (SMLR), was compared. First, the calibration models were built using ambroxol hydrochloride standard solutions. To compare the predictive ability of the four models constructed, the performance indices were calculated. As a result, both the CLS model and the PCR model were comparably effective ones, of which the difference values were 94.9% and 94.2%, respectively, and the root mean square errors (RMSEs) were 0.07 and 0.08, respectively. Eleven commercial injections were quantified directly applying the developed models. SPSS software was used to compare the difference between the results obtained from the pharmacopoeial HPLC method and Raman analysis, and there was no significant difference between them (p > 0.05). It shows that the proposed procedure based on the chemometric treatment of Raman spectra can be a specific, fast and convenient alternative to the compendial qualitative and quantitative determination of ambroxol hydrochloride Injection.

Graphical abstract: Rapid and sensitive determination of ambroxol hydrochloride injection by Raman spectroscopy combined with chemometric models

Article information

Article type
Paper
Submitted
25 Jul 2013
Accepted
30 Nov 2013
First published
02 Dec 2013

Anal. Methods, 2014,6, 1096-1100

Author version available

Rapid and sensitive determination of ambroxol hydrochloride injection by Raman spectroscopy combined with chemometric models

J. Le, J. Li, H. Wen, B. Chen, Z. Hong and G. Chen, Anal. Methods, 2014, 6, 1096 DOI: 10.1039/C3AY41250D

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