Chemometric determination of common cold infection drugs in human urine


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ERTOKUŞ G.

REVIEWS IN ANALYTICAL CHEMISTRY, vol.41, no.1, pp.158-167, 2022 (Journal Indexed in SCI) identifier

  • Publication Type: Article / Article
  • Volume: 41 Issue: 1
  • Publication Date: 2022
  • Doi Number: 10.1515/revac-2022-0040
  • Title of Journal : REVIEWS IN ANALYTICAL CHEMISTRY
  • Page Numbers: pp.158-167
  • Keywords: acetylsalicylic acid, paracetamol, caffeine, chemometry, human urine, SIMULTANEOUS SPECTROPHOTOMETRIC DETERMINATION, CONTINUOUS WAVELET TRANSFORM, ACETYLSALICYLIC-ACID, QUANTITATIVE-DETERMINATION, PHARMACEUTICAL FORMULATIONS, MULTICOMPONENT ANALYSIS, INFRARED-SPECTROSCOPY, TERNARY MIXTURE, RATIO SPECTRA, CAFFEINE

Abstract

In this work, spectrophotometric identification of acetylsalicylic acid (ASA), paracetamol (PCM), and caffeine (CAF) (common cold infection drugs) in human urine samples was investigated. For ASA, PCM, and CAF, chemometric analysis of human urine samples has proved successful. Spectrophotometric analysis of common cold infection drugs was performed using multivariate calibration methods (principal component regression [PCR] and partial least-squares regression). For the simultaneous prediction of common cold infection drugs in prepared mixes and human urine samples without prior separation, two spectrophotometric-chemometric approaches were proposed. The synthetic mixes were made with common cold infection drugs in the first stage, and the absorbance values were obtained using spectrophotometry. The quantities of common cold infection drugs in the human urine sample were calculated in the second stage. The calibration curves for each medication are linear in the concentration range of the synthetic mixes. The two methods were tested for accuracy and repeatability, and high recoveries and low standard deviations were calculated. sum of prediction residual errors, observation limit, and detection limit, and % recovery values, which are the analytical properties of the proposed methods, were 0.00029, 0.096, and 0.290, respectively; 0.0069, 0.086, and 0.260; 0.0077, 0.094, and 0.285; 0.0049, 0.066, and 0.199 for PCM, ASA, and CAF for the principal component regression method, respectively; 0.0059, 0.066, and 0.199; 0.0065, 0.069, and 0.210. The results produced using the employed chemometric methods are quick, easy, and consistent. The proposed methods are extremely sensitive and precise and have thus been effectively employed to detect active chemicals (ASA, PCM, and CAF) in human urine samples.