{"title":"Chemometric determination of common cold infection drugs in human urine","authors":"G. Ertokus","doi":"10.1515/revac-2022-0040","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":21090,"journal":{"name":"Reviews in Analytical Chemistry","volume":null,"pages":null},"PeriodicalIF":3.6000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Reviews in Analytical Chemistry","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1515/revac-2022-0040","RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
Reviews in Analytical Chemistry publishes authoritative reviews by leading experts in the dynamic field of chemical analysis. The subjects can encompass all branches of modern analytical chemistry such as spectroscopy, chromatography, mass spectrometry, electrochemistry and trace analysis and their applications to areas such as environmental control, pharmaceutical industry, automation and other relevant areas. Review articles bring the expert up to date in a concise manner and provide researchers an overview of new techniques and methods.