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Chemometric determination of common cold infection drugs in human urine

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Veröffentlicht/Copyright: 22. Juli 2022
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Reviews in Analytical Chemistry
Aus der Zeitschrift Reviews in Analytical Chemistry Band 41 Heft 1

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.

Keywords: acetylsalicylic acid; paracetamol; caffeine; chemometry; human urine

1 Introduction

Acetylsalicylic acid (ASA) [1], paracetamol (PCM) [2,3], and caffeine (CAF) [4] are used to relieve painful conditions and flu-like infections. ASA, PCM, and CAF in combination provide potent, fast-acting analgesia. The synergistic combination of ASA, PCM, and CAF provides greater pain relief than either component alone. When administered alone, this preparation relieves pain associated with migraine headaches as well as other conditions. The combination is prescribed for temporary relief of headache, mild to moderate pain associated with migraine, pain associated with sinusitis or colds, muscle pain, pain associated with menstrual cramps, toothache and mild arthritis pain, and pain associated with fever [5]. The chemical structures of ASA, PCM, and CAF are shown in Figure 1.

Figure 1 Chemical structures of the drugs.
Figure 1

Chemical structures of the drugs.

Various chemometric and analytical methods for the analysis of the three active ingredients can be found in the literature. PCM was determined by ultraviolet (UV) spectroscopy [6,7,8,9,10], high-performance liquid chromatography (HPLC) [10], and near-infrared spectroscopy [11]. ASA was investigated by spectroelectrochemical methods [12], UV spectroscopy [13], Raman spectroscopy and capillary electrophoresis [14], thin-layer chromatography [15], and near-IR spectroscopy [16]. CAF has also been analyzed by various methods in the literature, such as UV spectroscopy [17,18], HPLC [19,20,21], and liquid–liquid microextraction, which can be used together [22]. UV spectroscopic studies involved in chemometric studies were also evaluated in the review of methods [23,24].

Mathematical models support the chemometric method [25]. The simultaneous determination of two or more chemicals in the same sample without prior chemical separation has improved thanks to multivariate calibration methods [26]. Chemometric techniques are recently preferred statistical methods to calculate the amount of each component in a complex mixture. One of the most difficult challenges in analytical chemistry is analyzing these compounds in mixtures containing two or more active components without any separation. In rare circumstances where chromatographic and spectrophotometric methods for studying sample mixtures do not yield satisfactory findings, studies utilizing other chromatographic and spectrophotometric methods are available. Furthermore, despite the use of advanced analytical equipment for the analysis of smaller samples, efforts are being made to increase the precision and correctness of the results by exposing the data collected by traditional analytical equipment utilizing chemometrics to various mathematical algorithms. As a result, the procedure is less expensive, much faster, and more precise than other analytical methods. Principal component regression (PCR) and partial least-squares (PLS) are the most extensively used chemometric approaches in the study of pharmacological samples [27].

The main objective of this work was to perform a statistical analysis of the determination of ASA, PCM, and CAF in a healthy human urine sample by chemometric methods. The proposed UV spectroscopy technique was supported by PLS and PCR and the sensitivity, accuracy, and selectivity of the results obtained by chemometric methods used were determined [28–30]. In this study, PLS and PCR were performed for the simultaneous detection of ASA, PCM, and CAF in human urine without using a sorting method in triumph. The average recoveries (%) and standard deviations of the chemometric methods were calculated to validate the methods. The results were statistically compared with each other. By applying analysis of variance to the results, it was tested whether the differences between the applied methods were significant [31,32]. The obtained F values were cross-checked with the value read from the F table (α: 0.05), and the Student’s t-test was applied. The difficulty in working with human urine samples using both the classical UV method and other instrumental methods was overcame by chemometric methods in this study. In this study, the urine sample is investigated by chemometric methods due to its complex structure, and a contribution to the literature is made. Previous studies include UV spectroscopy and chemometric analysis of active ingredients in drug tablets. In this study, a human urine sample was used. The urine sample has a matrix structure, especially due to the proteins it contains. By eliminating the proteins and adding chemometric calculations to the urine sample, a statistical determination of drug substances was performed by UV spectroscopy.

2 Materials and methods

HCl (0.1 M) was used to dissolve analytical-grade stock solutions of 25 mg/250 mL acetylsalicylic acid (Aldrich, Darmstadt, Germany), paracetamol (Aldrich, Darmstadt, Germany), and caffeine (Aldrich, Darmstadt, Germany). For all measurements and data processing, a Shimadzu UV-1700 PharmaSpec spectrophotometer (Kyoto, Japan) was linked to an IBM PC running UV Probe software.

2.1 Preparation of validation and calibration solutions

ASA, PCM, and CAF absorption spectra were recorded between 210 and 310 nm. Three-component mixtures with varying concentrations were used in the training and validation sets. PLS and PCR were used to compute concentration levels and analyze human urine samples. Drug samples containing 4.0 and 25.0 mg·L−1 were placed in volumetric flasks (25 mL) and dissolved with 0.1 M HCl. The medications were administered at varying rates in the training set and the validation set, and 19 synthetic mixes (for validation and calibration) were created (Table 1 and Figure 2). The calibration set was constructed using a partial factorial design. Chemometric procedures were based on a well-designed experiment. Nineteen samples were created after the data were evaluated according to the experimental design.

Table 1

Concentration set for ASA, PCM, CAF

No. Concentration (mg·L−1)
PCM ASA CAF
1 5 7 6
2 10 7 12
3 15 7 18
4 20 7 24
5 25 7 30
6 5 14 6
7 10 14 12
8 15 14 18
9 20 14 24
10 25 14 30
11 5 21 6
12 10 21 12
13 15 21 18
14 20 21 24
15 5 28 6
16 10 28 12
17 15 28 18
18 5 35 6
19 10 35 12
Figure 2 Concentration set design for the preparation of PCR and PLS calibrations.
Figure 2

Concentration set design for the preparation of PCR and PLS calibrations.

2.2 Preparing for analysis of human urine samples

To prevent the matrix effect, healthy human urine samples were diluted 20 times with deionized water. Two milliliters of diluted urine were put into a tube, along with 3 mL of acetonitrile. To 5 mL of urine sample, 4 mL of 10% acetonitrile and various concentrations of the pharmaceutical ingredients were added and mixed. After the preparations, the individual spectra of the solutions were recorded. Human urine samples studied were obtained from healthy persons who did not consumed any other drugs that they used continuously throughout their lives. A lot of water was drunk even before the sample was taken. Thus, the most suitable urine matrix for drug analysis was prepared.

3 Results

Absorption spectra of ASA, PCM, CAF, and the mix solution were obtained between certain wavelengths. The absorbance-wavelength graphs are shown in Figure 3.

Figure 3 The absorption spectrum of ASA, PCM, caffeine mixtures.
Figure 3

The absorption spectrum of ASA, PCM, caffeine mixtures.

By plotting the absorbance–concentration diagrams for three drug substances, we see that the absorbance values increase proportionally to an increase in concentration. The fact that the regression coefficient [34] approximates the individual values confirms the linear connection [33] between absorbance and concentration (Table 2).

Table 2

Spectroscopic properties of drugs

ƛ max (nm) Correlation coefficient
PCM 243.50 0.9997
ASA 302.5 1.0000
CAF 273 0.9989

For the basic component analysis [35], a standard series of stock solutions of ASA, PCM and caffeine-containing solutions were prepared using stock solutions with a linear working range of 5–30 ppm. As can be seen from the plot of the eigenvalues obtained from the chemometric data in Figure 4, the eigenvalues increased from the second to the third order.

Figure 4 Diagram of eigenvalues obtained from chemometric data.
Figure 4

Diagram of eigenvalues obtained from chemometric data.

The first two factors are more reliable than 99% of the total variance. This is the expected behavior in PCA. PCA is a technique whose main purpose is to obtain the data set with the highest variance in high dimensional data while achieving dimensionality reduction. It finds the general features in overplanning and provides their dimensional use and data availability. Principal component analysis is a model used to reduce the size or reduce the size of multivariate values. The method was validated according to ICH (International Conference on Harmonisation) guidelines for validation of analytical procedures for different validation parameters [36]. The concentrations found are compared against known concentrations for cross-validation [37,38] in the calculation. In this way, errors are avoided in the calibration of solutions used in commercial sample processing. A concentration set consisting of 19 artificial mixed solutions containing drugs was prepared. First, the pure substances were examined to determine the spectral range of each component. Measurements were made between 200 and 800 nm. Then, the wavelength range for the interval concentration set and in the direction of the statistical program to be used was narrowed down to 210–310 nm. While the concentration set was prepared, the absorbance of the spectra recorded in the pure form was evaluated.

3.1 PCR and PLS regression

The PLS and PCR techniques were found to be valid by the statistical parameters used to validate the ASA, PCM, and CAF calibrations. The PLS and PCR methods are extremely precise. As indicated in the results (Tables 3 and 4), the standard deviation values are sufficiently low, and the recovery values are sufficiently close to 100, indicating that the results obtained are reasonable.

Table 3

For the PLS approach, the composition of the prediction set and the recovery outcomes obtained in synthetic mixtures

Added (mg·L−1) Found (mg·L−1) % Recovery
ASA PCM CAF ASA PCM CAF ASA PCM CAF
7 5 6 6.96 4.89 5.87 99.43 97.80 97.83
7 10 12 6.87 9.99 11.96 98.14 99.90 99.67
7 15 18 6.89 14.96 17.95 98.43 99.73 99.72
7 20 24 6.99 19.89 23.95 99.86 99.45 99.79
7 25 30 6.97 24.95 29.89 99.58 99.80 99.63
14 5 6 13.96 4.96 5.97 99.71 99.20 99.50
14 10 12 13.97 9.96 11.94 99.79 99.60 99.50
14 15 18 13.90 14.97 17.96 99.29 99.80 99.78
14 20 24 13.92 19.98 23.87 99.43 99.90 99.46
14 25 30 13.87 24.98 29.96 99.07 99.92 99.87
21 5 6 20.95 4.98 5.76 99.76 99.60 96.00
21 10 12 20.97 9.99 11.96 99.86 99.90 99.67
21 15 18 20.96 14.96 17.97 99.81 99.73 99.83
21 20 24 20.89 19.97 23.89 99.48 99.85 99.54
28 5 6 27.89 4.86 5.97 99.61 99.20 99.50
28 10 12 27.85 9.95 11.90 99.46 99.50 99.17
28 15 18 28.01 14.96 17.98 100.03 99.73 99.89
35 5 6 35.01 4.74 5.96 100.03 94.80 99.33
35 10 12 34.89 9.95 11.97 99.69 99.50 99.75
Mean: 99.50% RSD: 0.49 Mean: 99.21% RSD: 1.29 Mean: 99.34% RSD: 0.93
Table 4

For the PCR approach, the composition of the prediction set and the recovery outcomes obtained in synthetic mixtures

Added (mg·L−1) Found (mg·L−1) % Recovery
ASA PCM CAF ASA PCM CAF ASA PCM CAF
7 5 6 6.85 4.96 5.94 97.86 99.20 99.00
7 10 12 6.86 9.87 11.95 98.00 98.70 99.58
7 15 18 6.95 14.88 17.94 99.29 99.20 99.67
7 20 24 6.90 19.89 23.86 98.57 99.45 99.42
7 25 30 6.98 24.96 29.95 99.71 99.84 99.83
14 5 6 13.95 4.96 5.98 99.64 99.20 99.67
14 10 12 14.01 9.92 11.95 100.07 99.20 99.58
14 15 18 13.89 14.99 17.89 99.21 99.93 99.39
14 20 24 13.94 19.97 23.89 99.57 99.85 99.54
14 25 30 13.98 24.95 29.97 99.86 99.80 99.90
21 5 6 20.99 4.99 5.89 99.95 99.80 98.17
21 10 12 20.98 9.97 11.92 99.90 99.70 99.33
21 15 18 21.01 14.95 17.89 100.05 99.67 99.39
21 20 24 20.95 19.98 24.01 99.76 99.90 100.04
28 5 6 27.98 4.95 5.90 99.93 99.00 98.33
28 10 12 28.01 9.89 11.94 100.04 98.90 99.50
28 15 18 28.00 14.95 17.89 100.00 99.67 99.39
35 5 6 34.87 4.89 5.92 99.63 97.80 98.67
35 10 12 34.89 9.96 11.99 99.69 99.60 99.92
Mean: 99.51% RSD: 0.67 Mean: 99.39% RSD: 1.53 Mean: 99.3% RSD: 0.51

3.2 Validation of the method

The chemometric approach was validated in terms of linearity, accuracy, and precision within one day and between two days and in terms of the limit of detection and limit of quantification in accordance with international conference on harmonisation criteria [39,40,41]. The sum of prediction residual errors (PRESS) is used for calibration (Eq. 1) [42] was calculated as follows:

(1) PRESS = ∑ i = 1 n ( C i added − C i found ) 2

where C i added is actual concentration, the added concentration of drug; and C i found is predicted concentration, the calculated concentration of drug. According to the actual and predicted concentrations of the samples, PRESS values of ASA, PCM, and CAF were calculated (Table 5).

Table 5

Statistical parameter values for calibrating step-simultaneous ASA, PCM, and CAF determinations using PLS and PCR techniques

Parameters Method PCM ASA CAF
SEC PLS 0.019 0.023 0.023
PCR 0.020 0.019 0.024
PRESS PLS 0.00029 0.0069 0.0077
PCR 0.0049 0.0059 0.0065
RMSEC PLS 0.00391 0.0602 0.0201
PCR 0.0160 0.0557 0.0185
LOD (µg·mL−1) PLS 0.096 0.086 0.094
PCR 0.066 0.091 0.069
LOQ (µg·mL−1) PLS 0.290 0.260 0.285
PCR 0.199 0.276 0.210
Accuracy (% recovery ± SD) PLS 99.21 ± 1.29 99.50 ± 0.49 99.34 ± 0.93
PCR 99.39 ± 1.53 99.51 ± 0.67 99.39 ± 0.51
Precision (reproducibility)
Intraday (% recovery ± SD) (n: 6) PLS 99.89 ± 0.85 99.88 ± 0.54 99.84 ± 0.55
PCR 99.84 ± 0.64 99.87 ± 0.45 99.94 ± 0.32
Interday (% recovery ± SD) (n: 6) PLS 99.66 ± 0.24 98.87 ± 0.87 98.88 ± 0.78
PCR 99.65 ± 0.11 99.95 ± 0.89 99.99 ± 0.79

It is critical to note that this method of normalizing PRESS values is incorrect unless all data sets have the same amount of samples. However, the number of samples is included in the standard error of calibration (SEC) (Eq. 2). The efficiency of the calibration was determined by a few statistical criteria. The SEC was determined using the following formula:

(2) SEC = ∑ i = 1 n ( C i added − C i found ) 2 n − 1

where n is the total number of synthetic mixtures. Another validation parameter is root mean square error of calibration (RMSEC) [43] as shown below:

(3) RMSEC   =   PRESS / n

The observation limit (LOD) and detection limit (LOQ) parameters are related but have different definitions (Eqs. 4 and 5) [44]:

(4) LOD = 3 Sa/ m

(5) LOQ = 10 Sa/ m

where m – slope, Sa – the corrected standard deviation value LOQ > LOD and LOQ = LOD were considered in the evaluation of the calculated LOD values [45].

The values of PRESS and SEC are close to zero, indicating that the degree of accuracy is increasing. The calculated PRESS and SEC values are close to zero for the PLS and PCR methods (Table 5).

The performance of the explored chemometric approaches with the UV spectrophotometric method for analyzing the materials was evaluated using Snedecor’s F-test [46]. To analyze the differences between the disposable tests, the ANOVA method was used on the real samples for each medication. In this investigation, the Snedecor F-values were calculated and compared to the experimental F-values. An identical mathematical process was applied to each drug. The experimental (calculated) F-values did not exceed the F-values in the analysis of variance. For both chemometric techniques, the within-group degrees of freedom were 1 and 36, respectively, and the F-table value was 4.11 with a 95% confidence interval for both. Using the PLS technique, the F-test value for ASA was calculated as 0.00049, with a p-value of 0.98; the F-test value for PCM was calculated as 0.00071, with a p-value of 0.98; and the F-test value for CAF was calculated as 0.00070, with a p-value of 0.98. In the PCR method, the F-test value for ASA was 0.00030, with a p-value of 0.98; for PCM, the F-test value was 0.00030, with a p-value of 0.98; and the F-test value for CAF was 0.00071, with a p-value of 0.98 (see Figure 5). As a result, all of these techniques were shown to be considerably different.

Figure 5 The results of the one-way ANOVA test accoding to Snedecor’s F-test.
Figure 5

The results of the one-way ANOVA test accoding to Snedecor’s F-test.

3.3 Analysis of human urine samples

Table 6 shows the experimental values of the PCR and PLS techniques for human urine samples. The results can be shown to be extremely near. As indicated in Table 6, chemometric methods were successfully used to determine drugs in healthy human urine samples, yielding high-accuracy results. The obtained statistical values appear to be appropriate for detecting these substances in human urine samples at the same time.

Table 6

Determination of ASA, PCM, CAF in human urine using PLS and PCR methods

Mix no. Acetlysalicilic acid (PLS) Paracetamol (PLS) Caffeine (PLS)
Added (mg·L−1) Found (mg·L−1) Recovery (% mean) Added (mg·L−1) Found (mg·L−1) Recovery (% mean) Added (mg·L−1) Found (mg·L−1) Recovery (% mean)
1 5 4.89 97.80 5 4.99 99.80 5 5.01 100.2
2 10 9.96 99.60 10 9.97 99.70 10 9.88 98.80
3 15 14.95 99.67 15 14.96 99.73 15 14.87 99.13
4 20 19.96 99.80 20 19.89 99.45 20 19.96 99.80
5 25 24.98 99.92 25 24.97 99.88 25 24.97 99.88
Mean ± SD 99.36 ± 0.88 99.71 ± 0.16 99.56 ± 0.58
Acetlysalicilic acid (PCR) Paracetamol (PCR) Caffeine (PCR)
Mix no. Added (mg·L −1 ) Found (mg·L −1 ) Recovery (% mean) Added (mg·L −1 ) Found (mg·L −1 ) Recovery (% mean) Added (mg·L −1 ) Found (mg·L −1 ) Recovery (% mean)
1 5 4.88 97.60 5 4.95 99.00 5 4.97 99.40
2 10 9.87 98.70 10 9.94 99.40 10 9.97 99.70
3 15 14.97 99.80 15 14.95 99.67 15 14.97 99.80
4 20 19.99 99.95 20 19.96 99.80 20 19.98 99.90
5 25 24.56 98.24 25 24.69 98.76 25 24.78 99.12
Mean ± SD 98.86 ± 1.01 99.33 ± 0.44 99.58 ± 0.32
Acetlysalicilic acid (classical UV) Paracetamol (classical UV) Caffeine (classical UV)
Mix no. Added (mg·L −1 ) Found (mg·L −1 ) Recovery (% mean) Added (mg·L −1 ) Found (mg·L −1 ) Recovery (% mean) Added (mg·L −1 ) Found (mg·L −1 ) Recovery (% mean)
1 5 4.96 99.20 5 4.98 99.60 5 4.94 98.80
2 10 9.96 99.60 10 9.98 99.80 10 9.97 99.70
3 15 14.89 99.27 15 14.95 99.67 15 14.95 99.67
4 20 19.97 99.85 20 19.95 99.75 20 19.96 99.80
5 25 24.89 99.56 25 24.96 99.84 25 24.98 99.92
Mean ± SD 99.50 ± 0.26 99.73 ± 0.097 99.58 ± 0.45

4 Discussion

Simultaneously, PLS and PCR were successful in determining pharmaceuticals in synthetic solutions with pharmaceutical formulations. Low estimation errors and high correlation coefficients highlight the strong linear relationship between anticipated and actual concentrations for all values. The results obtained with this ternary mixture, as well as the component concentration ratios, reveal that these approaches have high predictive ability. The UV spectroscopy data were evaluated chemometrically to analyze the drug compounds in the sample containing three different active ingredients. The UV spectra of the active ingredients ASA, PCM, and CAF were recorded to determine the purity at which the study could be conducted, and analytical studies were performed. For ASA, PCM, and CAF the method was statistically supported by improving the UV spectrum. The regression analysis of the standard curves of the method was performed, and the results were statistically calculated. The F-test was considered in the analysis of the data obtained by the chemometric program. The results were also compared with the synthetic model created during the experimental design before using the effervescent tablet sample. The synthetic models were compared with the experimental results obtained from the chemometric program. The F-test was performed using the within-group and between-group degrees of freedom. We decided to apply the model we used after the F-test result to the mixture of the drug sample, but we cannot apply it. Now that we have passed the decision, we have decided based on the F test result.

5 Conclusions

After certain processes, proteins were precipitated from urine, and measurements were made in the UV-visible range. The high recoveries also showed that the drugs were not bound to the urine proteins. The mixtures containing ASA, PCM, and CAF errors were predicted in the validation process. The values of the sum of squares (PRESS) and SEC are also calculated. PRESS and SEC values converge to zero. The closer they are, the better the accuracy. The chemometric methods are applied to drug samples and human urine samples. The sensitivity is high for drug analysis, so the results are reproducible. The obtained results suggest that this method may be suitable for the simultaneous determination of ASA, PCM, and CAF in human urine.

  1. Funding information: The author states no funding involved.

  2. Author contributions: Güzide Pekcan Ertokuş: writing –original draft, writing – review and editing, methodology, formal analysis visualization, project administration, resources.

  3. Conflict of interest: The author states no conflict of interest.

References

[1] Prado TM, Cincotto FH, Machado SAS. Spectroelectrochemical study of acetylsalicylic acid in neutral medium and its quantification in clinical and environmental samples. Electrochim Acta. 2017;233:105–12. 10.1016/j.electacta.2017.03.038.Suche in Google Scholar

[2] Issa YM, Zayed SIM, Habib IHI. Simultaneous determination of ibuprofen and paracetamol using derivatives of the ratio spectra method. Arab J Chem. 2011;4(3):259–63. 10.1016/j.arabjc.2010.06.044.Suche in Google Scholar

[3] Khoshayand MR, Abdollahi H, Shariatpanahi M, Saadatfard A, Mohammadi A. Simultaneous spectrophotometric determination of paracetamol, ibuprofen and caffeine in pharmaceuticals by chemometric methods. Spectrochim Acta Part A Molecular Biomol Spectrosc. 2008;70(3):491–9. 10.1016/j.saa.2007.07.033.Suche in Google Scholar

[4] Souri E, Nasab SAM, Amanlou M, Tehrani MB. Development and validation of a rapid derivative spectrophotometric method for simultaneous determination of paracetamol, ibuprofen and caffeine. J Anal Chem. 2015;70(3):310–5. 10.1134/S1061934815030041.Suche in Google Scholar

[5] Steiner TJ. Acetylsalicylic acid, paracetamol and caffeine combination in headache. Cephalalgia. 2006;1:1260–1. 10.1111/j.1468-2982.2006.01180.x.Suche in Google Scholar

[6] Dinç E. Linear regression analysis and its application to the multivariate spectral calibrations fort he multiresolution of a ternary mixture of caffeine, paracetamol and metamizol in tablets. J Pharm Biomed Anal. 2003;33:605–15. 10.1016/s0731-7085(03)00260-7.Suche in Google Scholar

[7] Glavanovic S, Glavanovic M, Tomisic V. Simultaneous quantitative determination of paracetamol and tramadol in tablet formulation using UV spectrophotometry and chemometric methods. Spectrochim Acta Part A Molecular Biomol Spectrosc. 2016;157:258–64. 10.1016/j.saa.2015.12.020.Suche in Google Scholar PubMed

[8] Hajian R, Afshari N. The spectrophotometric multicomponent analysis of a ternary mixture of ibuprofen, caffeine and paracetamol by the combination of double divisor-ratio spectra derivative and H-point standard addition method. E-J Chem. 2012;9(3):1153–64. 10.1155/2012/621263.Suche in Google Scholar

[9] Saraan SMD, Sinaga SM, Muchlisyam B. Development method for the determination of ternary mixture of paracetamol, ibuprofen and caffeine in tablet dosage form using zero-crossing derivative spectrophotometric. Int J Pharmtech Res. 2015;7(2):349–53.Suche in Google Scholar

[10] Wedian F. Bivariate analysis for the determination of paracetamol and caffeine in drug formulations. Jordan J Chem. 2016;11(4):217–25.Suche in Google Scholar

[11] Muntean DM, Alecu C, Tomuta I. Simultaneous quantification of paracetamol and caffeine in powder blends for tableting by NIR-Chemometry. J Spectrosc. 2017;1–9. 10.1155/2017/7160675.Suche in Google Scholar

[12] Neuberger S, Joob K, Flottmann D. Hydrolysis of acetylsalicylic acid in AOT/near-critical propane microemilsion:A model hydrolysis reaction in high pressure microemulsion as investigated by in situ UV-vis spectroscopy. Am Chem Soc. 2001;222:U332.Suche in Google Scholar

[13] Neuberger S, Joob K, Flottmann D, Scriba G, Neusüb C. Raman spectroscopy and capillary zone electrophoresis for the analysis of degradation processes in commercial effervescent tablets containing acetylsalicylic acid and ascorbic acid. J Pharm Biomed Anal. 2017;134:122–5. 10.1016/j.jpba.2016.11.020.Suche in Google Scholar PubMed

[14] Panahi HA, Rahimi A, Moniri E, Izadi A, Parvin MM. HPTLC separation and quantitative analysis of aspirin, salicylic acid and sulfosalicyclic acid. J Planar Chromatography-Modern TLC. 2010;23(2):137–40. 10.1556/jpc.23.2010.2.10.Suche in Google Scholar

[15] Novikova A, Carstensen JM, Rades T, Leopold CS. UV imaging of multiple unit pellet system (MUPS) tablets: a case study of acetylsalicylic acid stability. Eur J Pharma Biopharma. 2017;119:447–53. 10.1016/j.ejpb.2017.08.002.Suche in Google Scholar

[16] Aktaş AH, Pekcan H. Chemometric methods for the simultaneous spectrophotometric determination of caffeine, theobromine and theophylline in tea. Asian J Chem. 2013;25(15):8333–8. 10.14233/ajchem.2013.14735.Suche in Google Scholar

[17] Dinç E, Baleanu D, Onur F. Spectrophotometric multicomponent analysis of a mixture of metamizol, paracetamol and caffeine in pharmaceutical formulations by two chemometric techniques. J Pharm Biomed Anal. 2001;26:949–57. 10.1016/s0731-7085(01)00484-8.Suche in Google Scholar

[18] Pereira VP, Knor FJ, Vellosa JCR, Beltrame FL. Determination of phenolic compounds and antioxidant activity of green, black and white teas of Camellia sinensis (L.) Kuntze, Theaceae. Rev Bras Plantas Med. 2014;16(3):490–8. 10.1590/1983-084X/13_061.Suche in Google Scholar

[19] Srdjenovic B, Djordjevic-Milic V, Grujic N, Injac R, Lepojevic Z. Simultaneous HPLC determination of caffeine, theobromine, and theophylline in food, drinks, and herbal products. J Chromatographic Sci. 2008;46:144–9. 10.1093/chromsci/46.2.144.Suche in Google Scholar PubMed

[20] Shehata AB, Rizk MS, Rend EA. Certification of caffeine reference material purity by ultraviolet/visible spectrophotometry and high-performance liquid chromatography with diode-array detection as two independent analytical methods. J Food Drug Anal. 2006;24:703–15. 10.1016/j.jfda.2016.06.009.Suche in Google Scholar PubMed PubMed Central

[21] Frizzarin RM, Maya F, Estela JM, Cerda V. Fully-automated in-syringe dispersive liquid-liquid microextraction for the determination of caffeine in coffee beverages. Food Chem. 2016;212:759–67. 10.1016/j.foodchem.2016.06.032.Suche in Google Scholar PubMed

[22] Culzoni MJ, Goicoechea HC, Ibanez GA, Lozano VA, Marsili NR, Olivieri AC, et al. Second-order advantage from kinetic-spectroscopic data matrices in the presence of extreme spectral overlapping: A multivariate curve resolution-Alternating least-squares approach. Anal Chim Acta. 2008;614:46–57. 10.1016/j.aca.2008.03.013.Suche in Google Scholar PubMed

[23] Mofavvaz S, Sohrabi MR, Heydari A. Application of UV/Vis spectrophotometry based on using least squares support vector machine and continuous wavelet transform methods for the simultaneous analysis of antibiotics drugs in tablet formulation. Opt – Int J Light Electron Opt. 2020;220:165246. 10.1016/j.ijleo.2020.165246.Suche in Google Scholar

[24] Shokuhi S, Sohrabi MR, Heydari A. Comparison between UV/Vis spectrophotometry based on intelligent systems and HPLC methods for simultaneous determination of anti-diabetic drugs in binary mixture. Opt – Int J Light Electron Opt. 2020;206:164304. 10.1016/j.ijleo.2020.164304.Suche in Google Scholar

[25] Madrakian T, Mohammadnejad M. Simultaneous spectrophotometric determination of levodopa and carbidopa in pharmaceutical formulations and water samples by using mean centering of ratio spectra and H-point standard addition methods. Chem Pharm Bull. 2007;55(6):865–70. 10.1248/cpb.55.865.Suche in Google Scholar

[26] Dinç E, Baleanu D. Spectrophotometric quantitative determination of cilazapril and hydrochlorothiazide in tablets by chemometric methods. J Pharm Biomed Anal. 2002;30:715–23. 10.1016/S0731-7085(02)00359-X.Suche in Google Scholar

[27] Dinç E, Özdemir A, Baleanu D. Comparative study of the continuous wavelet transform, derivative and partial least squares methods applied to the overlapping spectra for the simultaneous quantitative resolution of ascorbic acid and acetylsalicylic acid in effervescent tablets. J Pharm Biomed Anal. 2005;37(3):569–75. 10.1016/j.jpba.2004.11.020.Suche in Google Scholar

[28] Uyanık A. Analitik Kimyacılar için İstatistik ve Kemometri. Vol. 5, Pegem Academy; 2008. p. 224–5.Suche in Google Scholar

[29] Minitab 17 Statistical Programme: http://www.inovadanismanlik.com.tr (30.03.2021).Suche in Google Scholar

[30] Dinç E. Kemometrik İşlem ve Yöntemlerin Analitik Kimyadaki Tipik Uygulamaları. Kemometri Yaz Okulu Notları; 2009. p. 1–20.Suche in Google Scholar

[31] Kaba A, Aktaş AH. Çeşitli Ligandları Kullanarak Fe3+, Al3+ ve Cu2+ nin Bir Arada Spektrofotometrik Tayinleri İçin Yöntem Geliştirilmesi ve Elde Edilen Verilerin En Küçük Kareler Kalibrasyon Yöntemi (PLS) ve Temel Bileşen Regresyon (PCR) Yöntemi ile Değerlendirilmesi. SAUFBE. 2014;18(1):71–9.10.16984/saufbed.73723Suche in Google Scholar

[32] Aktaş AH, Mutlutürk B. Demir ve Alüminyumun Doğal Sularda UV Spektrofotometrik Tayini için Yöntem Geliştirilmesi ve Elde Edilen Verilerin Kemometrik Kalibrasyon Yöntemleriyle Değerlendirilmesi. Süleyman Demirel Üniver Fen Bilimleri Enstitüsü Derg. 2014;18(3):52–9.Suche in Google Scholar

[33] Sharma D, Singh R, Garg R. Development and validation of stability indicating UV spectro-photometric method for the estimation of benzydamine hydrochloride in bulk and in pharmaceutical dosage form: a novel analytical technique for conducting in-vitro quality control tests. Int J Pharm Sci Res. 2017;9(2):678–86. 10.13040/IJPSR.0975-8232.9(2).678-86.Suche in Google Scholar

[34] Miao J, Forget B, Smit K. Predicting correlation coefficients for monte carlo eigenvalue simulations with multitype branching process. Ann Nucl Energy. 2018;112:307–21. 10.1016/J.ANUCENE.2017.10.014.Suche in Google Scholar

[35] Saganowska P, Wesolowski M. Principal component and cluster analyses as supporting tools for co-crystals detection. J Therm Anal Calorim. 2017;130:45–55. 10.1007/s10973-017-6436-8.Suche in Google Scholar

[36] Abbai N, Parameswari SA. Stability indicating RP-HPLC method development and validation for simultaneous estimation od salbutamol and beclomethasone in bulk and tablet dosage form. IJPSR. 2018;9(5):1980–8. 10.13040/IJPSR.0975-8232.9(5).1980-88.Suche in Google Scholar

[37] Yücelbaş Ş, Yücelbaş C. Temel Bileşen Analizi Yöntemleri Kullanarak Parkinson Hastalığının Otomatik Teşhisi. Avrupa Bilim ve Teknoloji Derg. 2019;16:294–300. 10.31590/ejosat.568544.Suche in Google Scholar

[38] Porfire A, Muntean D, Achim M, Vlase L, Tomuta I. Simultaneous quantification of simvastatin and excipients in liposomes using near spectroscopy and chemometry. J Pharm Biomed Anal. 2015;107:40–9. 10.1016/j.jpba.2014.12.013.Suche in Google Scholar PubMed

[39] Tarhan İ, Ismail AA, Kara H. Quantitative determination of free fatty acids in extra virgin olive oils by multivariate methods and fourier transform infrared spectroscopy considering different absorption modes. Int J Food Prop. 2017;790–7. 10.1080/10942912.2017.1312437.Suche in Google Scholar

[40] Deshpande P, Mandawad V. Development and validation of stability indicating hptlc method for determination of azelastine hydrochloride as bulk drug and in pharmaceutical liquid dosage form. IAJPS. 2018;05(06):5107–13. 10.5281/zenodo.1287895.Suche in Google Scholar

[41] Aravind D, Kamarapu SK. Method development and validation of RP-HPLC method for simultaneous estimation of clidinium bromide, chlordiazepoxide and dicylomine hydrochloride in bulk and combined tablet dosage forms. IJPBS. 2013;3:152–61.Suche in Google Scholar

[42] Uyanık A. Analitik Kimyacılar için İstatistik ve Kemometri. Vol. 6, Pegem Academy; 2012. p. 309.Suche in Google Scholar

[43] Bilgili AV, Çullu MA, Aydemir S. Tuzdan etkilenmiş toprakların yakın kızılötesi yansıma spektroradyometre ve elektromanyetik indüksiyon tekniği yardımıyla karakterize edilebilme potansiyelinin araştırılması. Harran Tarım Gıda Bilimleri Derg. 2014;18(1):32–45.Suche in Google Scholar

[44] Shrivastava A, Gupta V. Methods for the determination of limit of detection and limit of quantitation of the analytical methods. Chron Young Sci. 2011;2(1):21–5. 10.4103/2229-5186.79345.Suche in Google Scholar

[45] Armbruster DA, Pty T. Limit of blank, limit of detection and limit of quantitation. Clin Biochem Rev. 2008;29:49–52.Suche in Google Scholar

[46] Bajpai V, Kumar S, Singh A, Singh J, Negi MPS, Bag SK, et al. Chemometric based identification and validation of specific chemical markers for goegraphical, seasonal and gender variations in tinospora cordifolia stem using HPLC-ESI-QTOF-MS analysis. Photochem Anal. 2017;28(4):277–88. 10.1002/pca.2673.Suche in Google Scholar PubMed

Received: 2022-04-01
Revised: 2022-05-30
Accepted: 2022-06-21
Published Online: 2022-07-22

© 2022 Güzide Pekcan Ertokus, published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/revac-2022-0040
Schlagwörter für diesen Artikel
acetylsalicylic acid; paracetamol; caffeine; chemometry; human urine
Creative Commons
BY 4.0

Artikel in diesem Heft

  1. Research Articles
  2. Abnormal retention of s-triazine herbicides on porous graphitic carbon
  3. One-factor-at-a-time method combined with ICP-MS for determining 11 elements in soy sauce and their migration from the containing glass bottles
  4. Analysis of initiator content of prepreg by near-infrared spectroscopy
  5. Simultaneous MEKC-DAD and smart spectrophotometric assays of thiocolchicoside and etoricoxib in challenging concentration ratio mixtures
  6. Alternative analytical methods for ibrutinib quantification in pharmaceutical formulation: A statistical comparison
  7. Chemometric determination of common cold infection drugs in human urine
  8. An effective, novel, and cheap carbon paste electrode for naproxen estimation
  9. Fabrication of ultra-sensitive carbon paste electrode with nanocomposite CdS modification for electroanalysis of rafoxanide in dosage form and biological fluids
  10. Purification and characterisation of phytochemicals extracted from Rhizophora mucronata: Their efficacy against Pseudomonas aeruginosa infection in Catla catla
  11. Review Articles
  12. Recent applications of quantitative analytical FTIR spectroscopy in pharmaceutical, biomedical, and clinical fields: A brief review
  13. Review of characteristics and analytical methods for determination of indomethacin
  14. A review of the application of comprehensive two-dimensional gas chromatography MS-based techniques for the analysis of persistent organic pollutants and ultra-trace level of organic pollutants in environmental samples
  15. Enrichment and analysis of glycated proteins
  16. Round robin tests of secondary raw materials: A systematic review of performance parameters
  17. Paper-based microfluidic devices: Fabrication, detection, and significant applications in various fields
  18. Applications of headspace solid-phase microextraction in human biological matrix analysis
  19. Chemometrics and infrared spectroscopy – A winning team for the analysis of illicit drug products
  20. Canagliflozin: A review with specific focus on analytical methods in biological matrices and pharmaceuticals
  21. RNA-based isothermal amplification technology and its clinical application in pathogen infection
  22. Detection of diarrheal shellfish toxins
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  24. Preparation of cuprous oxide-supported silver-modified reduced graphene oxide nanocomposites for non-enzymatic electrochemical sensor
  25. Recent advances in matrix metalloproteinases-responsive nanoprobes for cancer diagnosis and therapy
  26. A simple salicylaldehyde-bearing pyrazine as a turn-on fluorescent chemosensor for Al3+ and Zn2+ recognition and its applications
  27. Preparation of silver nanosheet-assembled film as a surface-enhanced Raman scattering substrate
  28. Synthesis of group I–III–VI semiconductor quantum dots and its application in food safety testing
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