Int J App Pharm, Vol 15, Issue 1, 2023, 195-205Original Article

A MODEST UV SPECTROPHOTOMETRIC ASSISTED BY CHEMOMETRIC APPROACH FOR VERIFICATION OF ACETAMINOPHEN LEVEL IN VARIOUS MANUFACTURED TABLETS AND SYRUPS IN INDONESIAN PHARMACIES

MUHAMMAD ABDURRAHMAN MUNIR1, AHLAM INAYATULLAH2, SOFIAN IBRAHIM3, IMRAM RADNE RIMBA PUTRI4, EMELDA EMELDA5, ANNISA FATMAWATI6, NURHIDAYANTI NURHIDAYANTI7

1Department of Pharmacy, Faculty of Health Sciences, Alma Ata University, Bantul, Yogyakarta, 55184, Indonesia, 2Department of Food Technology, Faculty of Chemical Engineering, Politeknik Sriwijaya, Palembang, 30139, Indonesia, 3Malaysian Nuclear Agency, 43000 Kajang, Bangi, Malaysia, 4Department of Hospital Administration, Faculty of Health Sciences, Alma Ata University, Bantul, Yogyakarta, 55184, Indonesia, 5,6,7Department of Pharmacy, Faculty of Health Sciences, Alma Ata University, Bantul, Yogyakarta, 55184, Indonesia
Email: muhammad@almaata.ac.id

Received: 06 Sep 2022, Revised and Accepted: 08 Nov 2022

ABSTRACT

Objective: This study aimed to verify the paracetamol level in some fabricated tablets and syrups in Indonesian pharmacies.

Methods: The fabricated tablets and syrups were analyzed using a spectrophotometer UV that was assisted by the chemometric approach. Partial least squares (PLS) and principal component regression (PCR) were the chemometric methods employed to verify the paracetamol level in pharmaceutical products. There were 25 different samples (tablets and syrups) applied in this study. The validation study was employed in this study to verify the approach according to the ICH guidelines. The double-distilled water was applied as a solvent before the samples were analyzed using a spectrophotometer.

Results: This technique was efficient and require double-distilled water only as a solvent. The results of this study reveal that there was a deviation in absorbance of the samples with RSD ranging from (0.15-0.45). The technique was linear, ranging from 1.0–6.0 µg·ml-1, with an R2 (0.9991) obtained at 242 nm. The percentage recovery was applied to study the accuracy of the technique and was acquired at 99.18%. The results have shown that the approach was the potential to be applied in estimating the level of paracetamol in tablets and syrups.

Conclusion: The detection of paracetamol levels in tablets and syrups using UV spectrophotometric showed satisfactory outcomes. The application of the chemometric approach by using PLC and PCR as the statistical assessment indicated that there was no significant distinction among the validated methods. Furthermore, the method can be used by industries particularly small industries to secure medicines that comply with Indonesian rules.

Keywords: Acetaminophen, Pharmacies, Spectrophotometer, Syrups, Tablets

© 2023 The Authors. Published by Innovare Academic Sciences Pvt Ltd. This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/)
DOI: https://dx.doi.org/10.22159/ijap.2023v15i1.46290. Journal homepage: https://innovareacademics.in/journals/index.php/ijap

INTRODUCTION

Indonesia is one of the developed countries and the pharmaceutical sector is a potential sector that contributes to the economy of Indonesia. Thus, the quality of the drugs spreading all over the country becomes great attention for many stakeholders such as the government, the manufacturers, and consumers [1]. Developing pharmaceutical products must be related to product quality. Furthermore, finding a suitable method for verifying the quality of pharmaceutical products becomes an imperative task [2].

Paracetamol (PAR) or known as acetaminophen, is generally applied as an antipyretic, anti-inflammatory, and analgesic. These effects occur owing to the production of cyclooxygenase (COX) 1 and 2 by prostaglandin is impeded by PAR [3]. The chemical structure of PAR that is illustrated in (fig. 1) shows that PAR activity is different compared to the other non-steroidal anti-inflammatory drugs (NSAIDs) owing to in peripheral tissues, the PAR has a mild impact on COX. Hereafter, the PAR influences blood platelet function and increases blood clotting time [4]. However, the application of PAR can be used for several issues such as fever, muscle ache, toothache, and arthritis [5].

Fig. 1: The chemical structure of N-(4-Hydroxyphenyl) acetamide

Nowadays, the Covid-19 becomes a specter that haunted us and it can lead to several symptoms, whether heavy or mild such as fever, cold, and flu. However, some people who have good immunity want to continue their activity without worrying about those symptoms. Thus, fast healing is demanded to do the treatment, particularly for people with busy rosters, and demand to be vigilant and focused as soon as possible. Furthermore, it has been achieved successfully by pharmaceutical industries by merging two and more substances in their drug productions to medicate those symptoms in order to enhance PAR activity. Various components have been applied for PAR production, as illustrated in (fig. 2) [5]. However, verification of a specific substance in multicomponent formulations is a huge challenge for many researchers in order to improve the health care system. This is also imperative to access the drugs before spreading the medicines to the pharmacies [6]. Thus, the development of analytical techniques is imperative. Consideration of a suitable method is required, such as fast, modest, inexpensive, and at the same time, the method does not influence the reliability, precision, and accuracy of the results. The studies have unveiled numerous techniques for the detection of drugs, whether in single or multi-components [7-9]. Furthermore, only HPLC and UV approaches can be applied for the determination of paracetamol [10-14].

According to their studies, chromatographic methods are time-consuming and require too many solvents, which is not suitable for quality control laboratories. Furthermore, analytical experts are required to operate the instruments. Meanwhile, spectrophotometric techniques are considered inexpensive and fast. This instrument is not only can be purchased and easily found in most labs but also can be operated by anyone who is not an expert in analytical instruments. The spectrophotometers offer substitute resolutions for complex mixtures of analytes with the requirement of prior separation or extraction [15-17].

Finding a selective and sensitive method to analyze paracetamol in tablets and syrups using a modest technique has encouraged us to develop a spectrophotometric approach that can be applied to the determination of the various combination. This technique applied a handy procedure with slight modification and does not apply sophisticated tools. Furthermore, the solvent used should be an eco-friendly product, such as the use of double-distilled water. The purpose of this study is to apply a spectrophotometric method supported by a chemometrics technique to increase the selectivity of this study. Partial Least Squares (PLS) and Principal Component Regression (PCR) are the chemometrics technique applied to verify the quantity of PAR in the products chosen. Chemometrics can be described as the interaction between the statistical and mathematical approaches in chemical analysis to stipulate the information by arranging the data. In this study, this method was applied to collect data from the spectrum and supply fast analysis with acceptable precision and accuracy without the requirement of sample preparation, which takes time [18-21].

The application of chemometric techniques offers several advantages when applied for the verification of pharmaceutical products, such as being free from disturbances and the determination being more accurate. Furthermore, the PLS application can improve the selectivity owing to the capability of PLS such as the errors can be minimized and the processing of data is faster with several absorbances and concentrations of analytes [22,23], whereas the least significant principal substances can be deleted by the PCR technique. The PLS and PCR models are specified by several parameters such as (1) root mean square error of calibration (RMSEC), (2) predicted residual sum of squares (PRESS), (3) root mean standard error of prediction (RMSEP), (4) root mean square error of cross-validation (RMSECV) and (5) merit fig. that can be described into several parameters such as selectivity, sensitivity, detection limit, and quantification limit [24, 25].

MATERIALS AND METHODS

Instrumentation

UV visible spectrophotometer model Thermo Scientific Evolution 201 double beam was applied. The scan was performed at intervals of 0.1 nm ranging from 230 to 400 nm, integration time at 0.05 sec, and scan speed at 1200 nm·min-1. UV Insight Software was employed. Balance Fujitsu (Japan), stirring hot plate (Australia), sonic bath (India), and shaking water bath (China) were used in this research. The Unscrambler software was applied to interpret the data obtained from the spectrophotometer analysis.

Reagents (Chemicals and materials)

PAR (99.72% purity) was supplied from Sigma Aldrich, Indonesia as a reference standard. Marketed tablets and syrups containing PAR with various compositions are purchased from Indonesian pharmacies and listed in table 1. Double distilled water (Merck) was used throughout the experiment.

Caffeine Ibuprofen
Dextromethorphan Pseudoephedrine
Phenylpropanolamine Chlorpheniramine maleate
Salicylamide Tramadol
Noscapine Glyceryl guaiacolate

Fig. 2: Chemical structures of several compounds that are generally applied in several drugs fabricated in Indonesian pharmacies to support the activity of paracetamol

Table 1: List of samples (tablets and syrups) used in this research

Sample name Paracetamol composition (mg) Other ingredients (mg)
Biogesic 500 -
Panadol 500 -
Sanmol 500 -
Demacolin 500 Pseudoephedrine HCl (7.5 mg) and chlorpheniramine maleate (2 mg)
Ultracet 325 Tramadol HCl (37.5 mg)
Zetamol 500 -
Pyrexin 500 -
Paratusin 500 Noscapine (10 mg), chlorpheniramine maleate (2 mg), glyceryl guaiacolate (50 mg) and phenylpropanolamine HCl (15 mg)
Paracetamol capsule 500 -
Paracetamol Actavis 500 -
Neozep Forte 250 Chlorpheniramine maleate (2 mg), salicylamide (150 mg) and phenylpropanolamine HCl (15 mg)
Novagesic 500 -
Promed 500 -
Dapyrin 500 -
Fasidol 500 -
Bodrex 600 Caffeine (50 mg)
Procold 500 Dextromethorphan HBr (10 mg) and pseudoephedrine HCl (30 mg)
Farsifen plus 350 Ibuprofen (200 mg) and caffeine (50 mg)
Tempra syrup 160 mg/5 ml -
Termorex syrup 160 mg/5 ml -
Sanmol syrup 120 mg/5 ml -
Praxion syrup 120 mg/5 ml -
Panadol syrup 35 mg/1 ml -
Pamol syrup 10 mg/0.6 mg -
Alphamol syrup 120 mg/5 ml -

“-”: There are no additives in this product except paracetamol

Preparation of stock solution of paracetamol (50 µg·ml-1)

A standard stock solution of PAR was prepared by dissolving 2.5 mg that was transferred to a 50 ml volumetric flask and completed to the tag with double-distilled water. Several diluted solutions were also prepared to range from 1-6 µg·mL-1 by simple dilution of stock solution.

The verification of paracetamol level in tablets and syrups

For tablet formulation, ten tablets were accurately weighed and ground to a powder and homogenized. A tablet powder was weighed at 50 mg equivalent to PAR and moved to a 50 ml volumetric flask. The mixture was homogenized using a centrifuge for 3 min and filtered using Whatman filter paper (150 mm diameter). The filtrate was diluted to obtain a 10 µg·ml-1 of PAR. Whereas for syrup formulation, 0.1 ml of syrup was transferred to a 25 ml volumetric flask. The solution was homogenized using a centrifuge for 3 min and filtered using Whatman filter paper (150 mm diameter). The filtrate was diluted to obtain a 10 µg·mL-1 of PAR. The absorbance was gauged using the chosen wavelengths and the calibration curve was applied to determine the level of PAR in the pharmaceutical products.

Validation study

The validation study complied with the ICH guidelines [26]. The PAR concentration vs. absorbance was used to establish a calibration curve. Furthermore, by plotting the calibration curves at 1-6 µg·ml-1 of PAR, linearity was obtained. The accuracy was carried out using the PAR standard at different concentrations such as 80%, 100%, and 120%, meanwhile the precision was determined using the intraday and inter-day assessment. The detection and quantification limits are determined by calculating using the formula 3.3 (SD/n) and 10 (SD/n), concurrently.

PLS and PCR techniques

The scheming of the experiment

The calibration and prediction sets are established by arranging several codes to represent the eleven concentrations. The codes were-5,-4,-3,-2,-1, 0,+1,+2,+3,+4,+5; thus the central level of PAR was coded as (0) [27]. The central level for the design was 3.5 µg·ml-1 and the measured drug concentration was chosen in their mixture and its spectral sensitivity. The codes and PAR levels are presented in table 2.

The calibration and prediction sets arrangement

Several PAR concentrations (Ccal) were organized from a stock solution to obtain various concentrations as illustrated in table 2. The absorbance (Acal) was recorded while the scan for the calibration set ranged from 230-400 nm. Whereas, for the prediction set, the PLS and PCR approaches were applied to predict the concentration of the mixtures by organizing the various PAR levels (Ccal) as presented in table 2. The absorbance (Acal) was recorded while the scan for the calibration set ranged from 230-400 nm.

The development of PCR and PLS models

The spectral data of the calibration set was applied to establish the PCR and PLS models. By using the interval of 1 nm, the absorbance data were recorded ranging from 230-400 nm. A regression equation is acquired by plotting the Ccal and Acal. The absorbance and concentration data were put into the computer and calculated to acquire the PLS and PCR models. Using the established model, the PRESS, RMSECV, RMSEC, RMSEP, and the correlation coefficient were determined [28, 29]. Furthermore, the merit fig. such as selectivity, sensitivity, detection limit, and quantification limit, were also analyzed. The sensitivity was determined by obtaining the slope of the analytical signal, while the selectivity was referred to as the difference between two concentrations determined by the PLS and PCR models. Furthermore, the detection and quantification limits were obtained by using the formula mentioned in Validation Study.

RESULTS AND DISCUSSION

Paracetamol standard analysis

The analysis of PAR at a wavelength of 242 nm. Thus, the spectrum of PAR (4 µg·ml-1) is shown in (fig. 3) and indicated that the analysis was acceptable for PAR analysis.

Table 2: The establishment of 11 levels 3 factors to develop a calibration set illustrated as concentrations and coding levels of the substance

Paracetamol
Standard Coding level Conc. (µg·ml-1) Standard Coding level Conc. (µg·ml-1)
Calibration set 26 -4 1.5
1 1 4.0 27 1 4.0
2 0 3.5 28 0 3.5
3 0 3.5 29 0 3.5
4 -1 3.0 30 -1 3.0
5 -5 1.0 31 -5 1.0
6 4 5.5 32 4 5.5
7 3 5.0 33 3 5.0
8 -3 2.0 34 -3 2.0
9 0 3.5 35 0 3.5
10 5 6.0 36 5 6.0
11 2 4.5 37 2 4.5
12 -2 2.5 38 -2 2.5
13 -4 1.5 39 -4 1.5
14 1 4.0 40 5 6.0
15 0 3.5 Prediction set
16 0 3.5 41 0 3.5
17 -1 3.0 42 4 5.5
18 -5 1.0 43 -5 1.0
19 4 5.5 44 3 5.0
20 3 5.0 45 2 4.5
21 -3 2.0 46 -3 2.0
22 0 3.5 47 1 4.0
23 5 6.0 48 -1 3.0
24 2 4.5 49 -2 2.5
25 -2 2.5 50 -4 1.5

Fig. 3: The absorption spectrum of 4 µg·ml-1 of paracetamol (242 nm)

Fig. 4 illustrates that the calibration curves of PAR were linear. The solution stability was below±1% compared to the new solution to discover the assay results as shown in table 3.

Various data convey in table 3, such as the detection limit, quantification limit, correlation coefficient, and regression equation. Furthermore, the precision was obtained by calculating the inter and intraday and showing the value below 2%, exhibiting satisfactory precision, whereas the accuracy was determined by recovery percentage.

The wavelength preference

The application of PLS and PCR models can be employed to select a wavelength owing to the selection can use predicting the concentration of the analyte. The range for the analysis was 230 to 300 nm. Due to the presence of noise, the data below 240 nm and above 300 nm were removed owing to the appearance of tiny absorbance and the appearance of tiny absorbance, respectively.

Fig. 4: Calibration curve of paracetamol (1-6 µg·ml-1)

Table 3: The outcome of the validation study after PAR was determined using the spectrophotometric method

Description Data obtaineda
Detection wavelength (nm) 242
Slope 0.1447±0.0014
Detection limit (µg·ml-1) 0.1693
Quantification limit (µg·ml-1) 0.5131
Linearity (µg·ml-1) 1-6
Intercept 0.0335±0.0054
Confidence limit for Interday Precision 0.357±0.47
Confidence limit for Intraday Precision 0.6732±0.22
Confidence limit for System Precision 0.0178±0.15
Accuracy, % w/w 99.18±0.789
Regression coefficient (R2) 0.9991

amean±SD (n=6)

Spectrophotometric technique supported by PLS and PCR models

The absorption spectra of the PAR drugs that are combined with several drugs show broad overlapping; furthermore, a conventional method can be applied to determine simultaneous components.

Thus, to tackle this issue, the PLS and PCR were employed. These methods are the common chemometric technique applied for the determination of various substances in pharmaceutical formulation concurrently and particularly for the products that have broad overlapping in their spectrum.

Furthermore, the application of these models in this study unveiled several facts, such as the very potential to analyze the concentration of multi-component drugs and lastly, they can interfere with the absorbance measurement with various wavelengths. Furthermore, if this method is applied in spectrophotometric analysis, various advantages can be acquired, such as it can magnify the accuracy of analysis, selecting suitable data, and deleting unimportant data. Consequently, the PLS and PCR models used to assist the spectrophotometric methods were discovered to be more suitable, acceptable, and have various benefits, such as being fast, modest, inexpensive, and sensitive compared to conventional methods without support from chemometric techniques.

Selection of principal components and variables

The Unscramble 11 software was used to study the selected range absorbance. The optimum factors were applied to elevate the calibration models of PLS and PCR. The assessment of the PLS and PCR predictive potentials was acquired using the prediction sets by plotting expected levels against known levels for each analyte. Table 4 shows the calibration and prediction set, meanwhile (fig. 5) illustrates the values of RMSEC, PRESS, RMSEP, and RMSECV for the predicted vs. reference after plotting by the PLS and PCR models, whereas, (fig. 6 and 7) present the regression coefficient plotted by the PLS and PCR models.

Table 4: The forecast of calibration and prediction models by using the PLS and PCR

Standard PAR
PLS PCR
Total obtained (µg·ml-1) Recovery (%) Total obtained (µg·ml-1) Recovery (%)
Calibration
1 4.02 100.5 4.16 104
2 3.57 102 3.71 106
3 3.56 101.71 3.70 105.71
4 2.95 98.33 3.09 103
5 0.99 99 1.13 113
6 5.71 103.82 5.85 106.36
7 5.06 101.2 5.20 104
8 1.98 99 2.12 106
9 3.6 102.86 3.74 106.86
10 6.17 102.83 6.31 105.17
11 4.59 102 4.73 105.11
12 2.52 100.8 2.66 106.4
13 1.6 106.67 1.74 116
14 3.99 99.75 4.13 103.25
15 3.51 100.29 3.65 104.29
16 3.51 100.29 3.65 104.29
17 3.03 101 3.17 105.67
18 1.01 101 1.15 115
19 5.29 96.18 5.43 98.73
20 4.98 99.6 5.12 102.4
21 2.00 100 2.14 107
22 3.51 100.29 3.65 104.29
23 6.09 101.5 6.23 103.83
24 4.32 96 4.46 99.11
25 2.59 103.6 2.73 109.2
26 1.49 99.4 1.63 108.67
27 4.11 102.75 4.25 106.25
28 3.57 102 3.71 106
29 3.61 103.14 3.75 107.14
30 2.98 99.33 3.12 104
31 1.01 101 1.15 115
32 5.5 100 5.64 102.55
33 5.17 103.4 5.31 106.2
34 2.3 115 2.44 122
35 3.48 99.43 3.62 103.43
36 5.99 99.83 6.13 102.17
37 4.37 97.11 4.51 100.22
38 2.5 100 2.64 105.6
39 1.56 104 1.70 113.33
40 5.98 99.67 6.12 102
Prediction
41 3.64 104 3.78 108
42 5.5 100 5.64 102.55
43 0.899 89.9 1.04 104
44 5.23 104.6 5.37 107.4
45 4.49 99.78 4.63 102.89
46 2.05 102.5 2.19 109.5
47 3.99 99.75 4.13 103.25
48 2.99 99.67 3.13 104.33
49 2.48 99.2 2.62 104.8
50 1.49 99.27 1.63 108.67

PLS: Partial Least Squares; PCR: Principal Component Regression

Fig. 5 shows satisfactory statistical parameters of the developed PLS and PCR models. This fig. also presents several important values such as RMSEC, SLOPE, R2, and SEC that were acquired by building satisfactory precision and accuracy and then optimizing the calibration matrix of absorbance spectra. The obtained outcomes indicate that PAR in the pharmaceutical products produced by various pharmacy companies with the PLS and PCR models is acceptable.

(a)

(b)

Fig. 5: The predicted vs. reference for (a) PLS and (b) PCR models

The prediction of components can be done by using the latent variables (LV) and principal components (PC) provided by the PLS and PCR models. The LV and PC can also be applied to enhance the accuracy of models. The loadings and coefficients were calculated to acquire the number of LV and PC. Furthermore, in order to evade the under and over-fitting data, the best number of LV and PC are selected.

The ideal numbers of LV and PC were determined using the cross-validation technique and the minimum value of RMSECV was chosen as the factor number [30]. In the assessment of the quality of the established designs (PLS and PCR), the explained variance was plotted as illustrated in fig. 6 and 7. The plots of variances are divided into two lines; the blue line was the calibration variance, whereas the red line was the validation variance. Testing the model on data was employed to build the validation variance, on the other hand, corresponding to the calibration data to build the calibration variance.

Fig. 6: The PLS model plotting an explained variance

Fig. 7: The PCR model plotting an explained variance

(a) (b)

Fig. 8: The regression coefficient plotted by (a) PLS and (b) PCR models

The dimensionality can be determined after the variance spans the plateau. According to the obtained data, if the Y-variance is large and the plateau is small, it indicates a satisfactory model. Furthermore, the model is demonstrative based on fig. 6 and 7 due to the curves of validation and calibration being identical. The latent variables (PLS and PCR) and the component numbers of explained variance are also presented in fig. 6 and 7, whereas fig. 8 presents the regression coefficient plotted by PLS and PCR models.

Application of the validated and developed approaches in PAR products

The determination of PAR in various pharmaceutical products (tablets and syrups) was analyzed in six replicates using the validated methods, and the outcomes are illustrated in table 6. The determination of the products was analyzed using Spectrophotometric UV; according to table 5, all pharmaceutical products were dissolved with ethanol before being analyzed using Spectrophotometer.

Table 5: The tablet weights of paracetamol products

Sample name The mean tablet weights (mg)a
Biogesic 552.3±0.40
Panadol 603.0±0.66
Sanmol 546.6±1.16
Demacolin 550.7±0.89
Ultracet 401.6±0.85
Zetamol 547.5±1.20
Pyrexin 547.6±1.09
Paratusin 601.5±0.84
Paracetamol capsule 602.6±0.51
Paracetamol Actavis 548.8±1.12
Neozep Forte 451.7±0.44
Novagesic 548.6±0.90
Promed 549.2±0.97
Dapyrin 550.3±0.83
Fasidol 549.8±0.84
Bodrex 701.4±0.33
Procold 552.2±0.70
Farsifen plus 652.6±0.43

amean±SD (n=10)

Furthermore, the equation of PAR standard was employed to estimate the concentration of PAR in their tablets and syrups formulations, furthermore, chemometric techniques such as PLS and PCR were established to estimate the concentration of each analyte present in their formulation. The results were attained in table 6 and all of them were acceptable.

Table 6: The outcomes obtained with the pharmaceutical formulation

Paracetamol production Illustration Simultaneous formula technique Chemometrics technique
PCR PLS
Biogesic Label Claim (mg) 500 500 500
Amount found (mg) 499.65 499.71 499.73
% Label Claim 99.93 99.94 99.95
Panadol Label Claim (mg) 500 500 500
Amount found (mg) 499.82 499.11 499.01
% Label Claim 99.96 99.82 99.80
Sanmol Label Claim (mg) 500 500 500
Amount found (mg) 498.92 499.12 499.15
% Label Claim 99.78 99.82 99.83
Demacolin Label Claim (mg) 500 500 500
Amount found (mg) 499.58 499.92 498.99
% Label Claim 99.92 99.98 99.80
Ultracet Label Claim (mg) 325 325 325
Amount found (mg) 324.09 323.98 324.71
% Label Claim 99.72 99.69 99.91
Zetamol Label Claim (mg) 500 500 500
Amount found (mg) 496.98 498.79 499.01
% Label Claim 99.40 99.76 99.80
Pyrexin Label Claim (mg) 500 500 500
Amount found (mg) 499,45 499.71 499.83
% Label Claim 99,89 99.94 99,97
Paratusin Label Claim (mg) 500 500 500
Amount found (mg) 498.95 498.79 499.02
% Label Claim 99.79 99.76 99.80
Paracetamol capsule Label Claim (mg) 500 500 500
Amount found (mg) 499.09 499.79 499.19
% Label Claim 99.82 99.96 99.84
Paracetamol Actavis Label Claim (mg) 500 500 500
Amount found (mg) 498.38 499.22 499.78
% Label Claim 99.68 99.84 99.96
Neozep Forte Label Claim (mg) 250 250 250
Amount found (mg) 248.78 249.19 249.69
% Label Claim 99.51 99,68 99.88
Novagesic Label Claim (mg) 500 500 500
Amount found (mg) 498.12 499.19 499.18
% Label Claim 99.62 99.84 99.84
Promed Label Claim (mg) 500 500 500
Amount found (mg) 499.28 499.38 498.78
% Label Claim 99.86 99.88 99.76
Dapyrin Label Claim (mg) 500 500 500
Amount found (mg) 498.77 499.79 499.67
% Label Claim 99.75 99.96 99.93
Fasidol Label Claim (mg) 500 500 500
Amount found (mg) 499.13 499.09 499.17
% Label Claim 99.83 99.82 99.83
Bodrex Label Claim (mg) 600 600 600
Amount found (mg) 599.13 599.71 599.71
% Label Claim 99.86 99.95 99.95
Procold Label Claim (mg) 500 500 500
Amount found (mg) 499.71 499.79 499.73
% Label Claim 99.94 99.96 99.95
Farsifen plus Label Claim (mg) 350 350 350
Amount found (mg) 349.71 349.15 349.27
% Label Claim 99.92 99.76 99.79
Tempra syrup Label Claim (mg) 32 32 32
Amount found (mg) 31.89 31.71 31.69
% Label Claim 99.66 99.09 99.03
Termorex syrup Label Claim (mg) 32 32 32
Amount found (mg) 31.78 31.91 31.09
% Label Claim 99.31 99.72 97.16
Sanmol syrup Label Claim (mg) 24 24 24
Amount found (mg) 23.82 23.99 23.49
% Label Claim 99.25 99.96 97.88
Praxion syrup Label Claim (mg) 24 24 24
Amount found (mg) 23.94 23.18 23.68
% Label Claim 99.75 96.58 98.67
Panadol syrup Label Claim (mg) 35 35 35
Amount found (mg) 34.22 34.79 34.38
% Label Claim 97.77 99.40 98.23
Pamol syrup Label Claim (mg) 24 24 24
Amount found (mg) 23.29 23.22 23.79
% Label Claim 97.04 96.75 99.13
Alphamol syrup Label Claim (mg) 24 24 24
Amount found (mg) 23.27 23.17 23.77
% Label Claim 96.96 96.54 99.04

PLS: Partial Least Squares; PCR: Principal Component Regression

Numerous studies have analyzed PAR as a single component or in combination with other components in several dosage forms. Table 7 presents several studies of paracetamol analysis using the spectrophotometric approach. UV spectrophotometric determination is commonly applied in quality control testing and ordinary laboratories owing to its stability, simplicity, and broader availability. This study exhibited accurate, precise, and cheap assays for these medicines in mixtures. Based on the previous studies, Vidal et al. (2002) reported a single triparameter flow with UV analysis for the simultaneous analysis of caffeine, aspirin, and paracetamol. The calibration curve ranged from 4–50, 40–500, and 10–100 µg.ml-1, respectively, whereas the detection limits were from 0.3–0.8 µg.ml-1 [31]. Meanwhile, Cemal et al. (2008) presented a technique for paracetamol and aspirin detection using spectrophotometric UV and verified by HPLC. The linear range was applied at 0.5–4 and 0.75–6 µg.ml-1 for acetaminophen and aspirin, respectively [32].

Table 7: Several studies of paracetamol detection using a spectrophotometric approach

Sample Technique LoD Linear range References
Aspirin and Paracetamol Spectrophotometer UV 0.73 and 0.59 µg‧ml-1 2–64 µg‧ml-1 [33]
Tramadol and Paracetamol Spectrophotometer UV that validated using UHPLC and assisted by genetic algorithm coupled with PLS (GA-PLS) Not reported 1.7–4.0 and 16–37 µg‧ml-1 [34]
Paracetamol Spectrophotometer UV that verified using FTIR 0.19 µg‧ml-1 0.3–20 µg‧ml-1 [35]
Paracetamol, caffeine and acetylsalicylic acid Spectrophotometer UV and assisted PLS Not reported 10–15 and 2–6 µg‧ml-1 [36]
Aspirin, paracetamol, caffeine and chlorphenamine Spectrophotometer UV assisted by PCR and PLS Not reported 4.11–19.53, 3.33–16.65, 2–14 and 2.37–13.27 µg‧ml-1 [37]
Mefenamic acid and paracetamol Spectrophotometer UV assisted by PCR 1.15 and 2.50 µg‧ml-1 2–10 and 4–20 µg‧ml-1 [38]
Paracetamol, ibuprofen and caffeine Spectrophotometer UV assisted by genetic algorithm coupled with PLS (GA-PLS) and principal component-artificial neural network (PC-ANN) 0.21, 0.52 and 0.67 µg‧ml-1

0.6–11, 1–24 and 1–18

µg‧ml-1

 [39]
Paracetamol Spectrophotometer UV assisted by PCR and PLS 0.17 µg‧ml-1 1–6 µg‧ml-1 This study

UV: Ultraviolet; UHPLC: Ultra High-Performance Liquid Chromatography; FTIR: Fourier Transform Infra Red; PLS: Partial Least Squares; PCR: Principal Component Regression

CONCLUSION

The current study achieved paracetamol analysis in various pharmaceutical products (tablets and syrups). The developed and validated methods using UV spectrophotometric assisted by chemometric technique revealed satisfactory precision, accuracy, and fast to verify the selected analyte. The application of this method that assisted by statistical assessment (PLS and PCR) indicated that there was no significant distinction among the validated methods. The developed and validated methods give several benefits in terms of the analysis cost and using eco-friendly solvents owing to not all pharmacy industries in Indonesia having a sophisticated instrument to do the analysis, such as FTIR and NMR. Thus, this validated approach can be applied by small industries to ensure the drugs produced follow the regulation of the Indonesian government. Furthermore, these methods can be employed in routine quantity and quality controls to determine the paracetamol level in pharmaceutical products.

ACKNOWLEDGMENT

The authors are genuinely thankful to the Department of Pharmacy, Faculty of Health Sciences for supplying the necessary laboratory facilities to perform the study.

FUNDING

Nil.

AUTHORS CONTRIBUTIONS

All the authors have contributed equally.

CONFLICTS OF INTERESTS

Declared none

REFERENCES

  1. Mahendradhata Y, Andayani NLPE, Hasri ET, Arifi MD, Siahaan RGM, Solikha DA. The capacity of the Indonesian healthcare system to respond to covid-19. Front Public Health. 2021 Jul 7;9:649819. doi: 10.3389/fpubh.2021.649819, PMID 34307272.

  2. Abdul-hasan MT, Mohammed Jawad KK, Neamah Al-Shaibani AJ, Hasan Al-Gburi KM. Quality evaluation of brands of propranolol HCL tablets available on iraqi market. Int J App Pharm 2022;12(2):48-52. doi: 10.22159/ ijap.2022v14i2.43756.

  3. Garcia lopez EB, Munoz-ochoa M, Hernandez Guerrero CJ, Nieto Camacho A, Band Schmidt CJ. Evaluation of anti-inflammatory activity of macroalgae collected from Baja California Sur, Mexico. Int J Pharm Pharm Sci. 2021 Aug 1;13(8):81-8. doi: 10.22159/ijpps.2021v13i8.41453.

  4. Pathan SA, Mitra B, Cameron PA. A systematic review and meta-analysis comparing the efficacy of nonsteroidal anti-inflammatory drugs, opioids, and paracetamol in the treatment of acute renal colic. Eur Urol. 2018 Apr;73(4):583-95. doi: 10.1016/j.eururo.2017.11.001. PMID 29174580.

  5. Saroj K, Haresh B, Yogendra P. Efficacy, safety, and cost-effective analysis of low-dose etoricoxib and add-on paracetamol versus therapeutic dose etoricoxib for pain in patients after tooth extraction: a randomized interventional double-blind study. Asian J Pharm Clin Res 2022;15:46-50. doi: 10.22159/ajpcr.2022.v15i5.44414.

  6. Alshargabi A. In vitro assessment of physicochemical parameters of five generics amlodipine besylate tablets marketed in Yemen. Int J Pharm Pharm Sci. 2021 Nov 1;13(11):15-9. doi: 10.22159/ijpps.2021v13i11.42898.

  7. Aly FA, El-Enany N, Elmansi H, Nabil A. Simultaneous determination of cetirizine, phenyl propanolamine and nimesulide using third derivative spectrophotometry and high-performance liquid chromatography in pharmaceutical preparations. Chem Cent J. 2017 Oct 5;11(1):99. doi: 10.1186/s13065-017-0326-9, PMID 29086879.

  8. Munir MA, Inayatullah A, Badrul HA. Fish analysis containing biogenic amines using gas chromatography equipped with flame ionization and mass spectrometer detectors. Sci Technol Indonesia. 2021 Jan;6(1):1-7. doi: 10.26554/sti.2021.6.1.1-7.

  9. Mohamed D, Hegazy MA, Elshahed MS, Toubar SS, Helmy MI. Liquid chromatography-tandem MS/MS method for simultaneous quantification of paracetamol, chlorzoxazone and aceclofenac in human plasma: an application to a clinical pharmacokinetic study. Biomed Chromatogr. 2018 Jul;32(7):e4232. doi: 10.1002/bmc.4232, PMID 29505100.

  10. Vu Dang H, Truong Thi Thu H, Dong Thi Ha L, Nguyen Mai H. RP-HPLC and UV spectrophotometric analysis of paracetamol, ibuprofen, and caffeine in solid pharmaceutical dosage forms by derivative, fourier, and wavelet transforms: A comparison study. J Anal Methods Chem. 2020 Feb 8;2020:8107571. doi: 10.1155/2020/8107571, PMID 32089949.

  11. Dave HN, Mashru RC. Application of two advanced derivative spectrophotometric methods for simultaneous estimation of salbutamol sulphate, ambroxol hydrochloride and theophylline in pure and commercial formulations. Int J Curr Pharm Sci 2022;14(3):33-8. doi: 10.22159/ijcpr.2022v14i3.1976.

  12. Alkhafaji SL, Mahood AM. First-order derivative and UV-spectrophotometric methods for simultaneous determination of paracetamol, ibuprofen, and caffeine in bulk and pharmaceutical formulatation. JPRI 2018;25(2):1-14. doi: 10.9734/JPRI/2018/46503.

  13. Munir MA, Mackeen MMM, Heng LY, Badri KH. Study of histamine detection using liquid chromatography and gas chromatography. ASMScJ. 2021 Jul;16:1-9. doi: 10.32802/asmscj.2021.809.

  14. Palur K, Archakam SC, Koganti B. Chemometric assisted UV spectrophotometric and RP-HPLC methods for simultaneous determination of paracetamol, diphenhydramine, caffeine and phenylephrine in tablet dosage form. Spectrochim Acta A Mol Biomol Spectrosc. 2020 Dec 15;243:118801. doi: 10.1016/j.saa.2020.118801. PMID 32827914.

  15. Tantawy MA, Weshahy SA, Wadie M, Rezk MR. Eco-friendly spectrophotometric methods for assessment of alfuzosin and solifenacin in their new pharmaceutical formulation; Green profile evaluation via eco-scale and GAPI tools. Curr Pharm Anal. 2021 Aug 1;17(8):1093-103. doi: 10.2174/157341291699920073000 5740.

  16. Mohamed AA, El-Olemy A, Ramzy S, Abdelazim AH, Omar MKM, Shahin M. Spectrophotometric determination of lesinurad and allopurinol in recently approved FDA pharmaceutical preparation. Spectrochim Acta A Mol Biomol Spectrosc. 2021 Feb 15;247:119106. doi: 10.1016/j.saa.2020.119106. PMID 33160135.

  17. Munir MA, Badri KH, Heng LY, Inayatullah A, Nurinda E, Estiningsih D. The application of polyurethane-LiClO4 to modify screen-printed electrodes analyzing histamine in mackerel using a voltammetric approach. ACS Omega. 2022 Feb;7(7):5982-91. doi: 10.1021/acsomega.1c06295, PMID 35224359.

  18. Mishra P, Roger JM, Jouan Rimbaud Bouveresse DJR, Biancolillo A, Marini F, Nordon A. Recent trends in multi-block data analysis in chemometrics for multi-source data integration. TrAC Trends Anal Chem. 2021 Apr;137:116206. doi: 10.1016/j.trac.2021.116206.

  19. Liu J, Jin S, Bao C, Sun Y, Li W. Rapid determination of lignocellulose in corn stover based on near-infrared reflectance spectroscopy and chemometrics methods. Bioresour Technol. 2021 Feb;321:124449. doi: 10.1016/j.biortech.2020.124449. PMID 33285506.

  20. Kalogiouri NP, Samanidou VF. Liquid chromatographic methods coupled to chemometrics: a short review to present the key workflow for the investigation of phenolic wine composition as it is affected by environmental factors. Environ Sci Pollut Res Int. 2021 Nov;28(42):59150-64. doi: 10.1007/s11356-020-09681-5, PMID 32577971.

  21. da Costa NL, da Costa MS, Barbosa R. A review on the application of chemometrics and machine learning algorithms to evaluate beer authentication. Food Anal Methods. 2021 Jan;14(1):136-55. doi: 10.1007/s12161-020-01864-7.

  22. Agarwal A, Shah D, Shen D, Song D. On robustness of principal component regression. J Am Stat Assoc. 2021 Jul 19;116(536):1731-45. doi: 10.1080/01621459.2021.1928513.

  23. Chen X, Lee JD, Li H, Yang Y. Distributed estimation for principal component analysis: an enlarged eigenspace analysis. J Am Stat Assoc. 2021 Apr 6:1-12. doi: 10.1080/01621459.2021.1886937.

  24. Purwanto A, Sudargini Y. Partial least squares structural squation modeling (PLS-SEM) analysis for social and management research: a literature review. J Ind Eng Manag Res. 2021 Aug;2(4):114-23. doi: 10.7777/jiemar.v2i4.168.

  25. Cheah J, Roldan JL, Ciavolino E, Ting H, Ramayah T. Sampling weight adjustments in partial least squares structural equation modeling: guidelines and illustrations. Total Qual Manag Bus Excell. 2021 Apr;32(13-14):1594-613. doi: 10.1080/14783363.2020.1754125.

  26. Beg S, Haneef J, Rahman M, Peraman R, Taleuzzaman M, Almalki WH. Handbook of analytical quality by design; 2021. p. 1-14. doi: 10.1016/C2019-0-01419-7.

  27. Shi YY, Li JY, Chu XL. Progress and applications of multivariate calibration model transfer methods. Chin J Anal Chem. 2019 Apr;47(4):479-87. doi: 10.1016/S1872-2040(19)61152-7.

  28. Agnoletti BZ, Folli GS, Pereira LL, Pinheiro PF, Guarçoni RC, da Silva Oliveira ECS. Multivariate calibration applied to study of volatile predictors of arabica coffee quality. Food Chem. 2022 Jan 15;367:130679. doi: 10.1016/j.foodchem.2021.130679. PMID 34352695.

  29. El-Zeiny MB, Zawbaa HM, Serag A. An evaluation of different bio-inspired feature selection techniques on multivariate calibration models in spectroscopy. Spectrochim Acta A Mol Biomol Spectrosc. 2021 Feb 5;246:119042. doi: 10.1016/j.saa.2020.119042. PMID 33065451.

  30. MacArthur RL, Teye E, Darkwa S. Predicting adulteration of palm oil with Sudan IV dye using shortwave handheld spectroscopy and comparative analysis of models. Vib Spectrosc. 2020 Sep;110:2020.103129. doi: 10.1016/j.vibspec.2020.103129.

  31. Domínguez Vidal A, Garcia Reyes JF, Ortega Barrales P, Molina Diaz A. UV spectrophotometric flow-through multiparameter sensor for the simultaneous determination of acetaminophen, acetylsalicylic acid, and caffeine. Anal Lett. 2002 Aug 15;35(15):2433-47. doi: 10.1081/AL-120016535.

  32. Cemal A, Ismail TD, Ahmet S, Ahmet A, Yalcin O, Huamettin G. Rapid and simultaneous determination of acetylsalicylic acid, paracetamol, and their degradation and toxic impurity products by HPLC in pharmaceutical dosage forms. Turk J Med Sci. 2008 Feb 11;38(2):167-73.

  33. Murtaza G, Khan SA, Shabbir A, Mahmood A, Asad MHH, Farzana K. Development of a UV-spectrophotometric method for the simultaneous determination of aspirin and paracetamol in tablets. Sci Res Essays. 2011 Jan 18;6(2):417-21. doi: 10.5897/SRE10.925.

  34. Glavanovic S, Glavanovic M, Tomisic V. Simultaneous quantitative determination of paracetamol and tramadol in tablet formulation using UV spectrophotometry and chemometric methods. Spectrochim Acta A Mol Biomol Spectrosc. 2016 Mar 15;157:258-64. doi: 10.1016/j.saa.2015.12.020, PMID 26774813.

  35. Sirajuddin S, Khaskheli AR, Shah A, Bhanger MI, Niaz A, Mahesar S. Simpler spectrophotometric assay of paracetamol in tablets and urine samples. Spectrochim Acta A Mol Biomol Spectrosc. 2007 Nov;68(3):747-51. doi: 10.1016/j.saa.2006.12.055, PMID 17418626.

  36. Sena MM, Poppi RJ. N-way PLS applied to the simultaneous spectrophotometric determination of acetylsalicylic acid, paracetamol and caffeine. J Pharm Biomed Anal. 2004 Jan 27;34(1):27-34. doi: 10.1016/j.japna.2003.08.011, PMID 14738916.

  37. Moţ AC, Soponar F, Medvedovici A, Sarbu C. Simultaneous spectrophotometric determination of aspirin, paracetamol, caffeine, and chlorphenamine from pharmaceutical formulations using multivariate regression methods. Anal Lett. 2010 Feb 24;43(5):804-13. doi: 10.1080/00032710903486260.

  38. Dinc E, Yucesoy C, Onur F. Simultaneous spectrophotometric determination of mefenamic acid and paracetamol in a pharmaceutical preparation using ratio spectra derivative spectrophotometry and chemometric methods. J Pharm Biomed Anal. 2002 Jun 15;28(6):1091-100. doi: 10.1016/S0731-7085(02)00031-6, PMID 12049974.

  39. 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 A Mol Biomol Spectrosc. 2008 Aug;70(3):491-9. doi: 10.1016/j.saa.2007.07.033, PMID 17825606.