Objective: The purpose of this study was to develop and validate Fourier Transform Infra-Red (FTIR) method for a non-chromophore antibiotic, kanamycin sulfate. Afterward, it was compared to other quantitative methods, namely colorimetry using ninhydrin.
Methods: FTIR was used to determine the level of kanamycin sulfate. Firstly, the absorbance spectra were measured. To increase the specificity, it used mathematical derivation, which would separate the active compound's spectrum clearer from the other component of the dosage form. The clearest absorbance spectra was found at 1540-1480 cm-1, as the N-H bending, which then was used as the base for the quantitative analysis. This method was then applied to determine the kanamycin sulfate content in the commercial reconstitution-injection dosage form. Afterward, the analysis performance compared to another method which had established, namely colorimetry. The counterpart method was using ninhydrin as the chemical reagent to produce a chromophore, which able to be measured by visible spectrophotometry. Validation test checked the parameters such as linearity, LOD, LOQ, range, accuracy, and precision, continuing to evaluate and to compare the time and material involved with the colorimetry as the counterpart.
Results: The results showed that FTIR for kanamycin sulfate lavel determination gave linearity within the range 0.12–1.5% w/w, with the r = 0.99975. Meanwhile, the LOD and LOQ were 0.04% and 0.12% w/w. There were no significant differences between the results of content determination of the antibiotic from the two methods. However, FTIR shows more advantages on time reducing, more straightforward preparation, solvents reducing, less costly, and more convenient.
Conclusion: FTIR is suitable to be an alternative method for quantifying kanamycin sulfate in a dosage form accurately, sensitive, and economist. This method is superior in its accuracy, precision, and simplicity compared to the colorimetry method using UV spectrometer as the counterpart.
Performance Comparison of Quantitative Methods for PMU Data Event Detection with Noisy Data
