Simultaneous Determination of Over-the-Counter Pain...

Research ArticleSimultaneous Determination of Over-the-Counter PainRelievers in Commercial Pharmaceutical Products UtilizingMultivariate Curve Resolution-Alternating Least Squares(MCR-ALS) Multivariate Calibration Model

Heba Shaaban Ahmed Mostafa Zahra Almatar Reem Alsheef and Safia Alrubh

Department of Pharmaceutical Chemistry College of Clinical Pharmacy Imam Abdulrahman Bin Faisal UniversityKing Faisal Road PO Box 1982 Dammam 31441 Saudi Arabia

Correspondence should be addressed to Heba Shaaban hsmohammediauedusa

Received 13 May 2019 Revised 26 June 2019 Accepted 15 July 2019 Published 31 July 2019

Academic Editor Antonio V Herrera-Herrera

Copyright copy 2019 Heba Shaaban et al is is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work isproperly cited

e quality of over-the-counter (OTC) pain relievers is important to ensure the safety of the marketed products in order tomaintain the overall health care of patients In this study the multivariate curve resolution-alternating least squares (MCR-ALS) chemometric method was developed and validated for the resolution and quantification of the most commonlyconsumed OTC pain relievers (acetaminophen acetylsalicylic acid ibuprofen naproxen and caffeine) in commercial drugformulations e analytical performance of the developed chemometric methods such as root mean square error of pre-diction bias standard error of prediction relative error of prediction and coefficients of determination was calculated for thedeveloped model e obtained results are linear with concentration in the range of 05ndash7 μgmL for acetaminophen and05ndash35 and 05ndash3 μgmL for naproxen and caffeine respectively while the linearity ranges for acetyl salicylic acid andibuprofen were 1ndash15 μgmL High values of coefficients of determination ge09995 reflected high predictive ability of thedeveloped model Good recoveries ranging from 980 to 997 were obtained for all analytes with relative standard deviations(RSDs) not higher than 162 e optimized method was successfully applied for the analysis of the studied drugs either intheir single or coformulated pharmaceutical products without any separation step e optimized method was also comparedwith a reported HPLC method using paired t-test and F-ratio at 95 confidence level and the results showed no significantdifference regarding accuracy and precision e developed method is eco-friendly simple fast and amenable for routineanalysis It could be used as a cost-effective alternative to chromatographic techniques for the analysis of the studied drugs incommercial formulations

1 Introduction

Pain relievers are among the most widely consumed med-ications worldwide ese medications are used for relievinginflammatory pain conditions [1] Pain relievers such asacetaminophen ibuprofen naproxen aspirin and caffeineare available as over-the counter (OTC) pharmaceuticalpreparations

Ibuprofen is a nonsteroidal anti-inflammatory (NSAID)drug Its chemical name is 2-(4-isobutylphenyl)-propionic acid

Ibuprofen is commonly used for treating pain and in-flammation in musculoskeletal disorders especially in rheu-matoid arthritis [2] Paracetamol (acetaminophen) is anantipyretic drug that acts by inhibiting the synthesis of pros-taglandins Aspirin (acetylsalicylic acid) is a NSAID drug usedfor relieving pain fever and inflammation [3] Howevercaffeine has no analgesic activity of its own and it is widely usedin combination with aspirin and acetaminophen as it hasadjunct action for the enhancement of relieving pain In-corporating caffeine in OTC pain relief formulations increases

HindawiJournal of Analytical Methods in ChemistryVolume 2019 Article ID 1863910 8 pageshttpsdoiorg10115520191863910

their activity by sim40 because of the ability of caffeine tofacilitate the absorption of other drugs and to cause con-striction of the cerebral blood vessels [3] Naproxen is also aNSAID drug that acts by inhibition of prostaglandin synthesis[1]

Because of the growing consumption of OTC pain re-lievers controlling the quality of these medications is highlyrequired in order to ensure their efficiency erefore de-veloping new reliable fast and cost-effective methods foranalyzing such drugs is of paramount importance

Implementation of green procedures in analytical labo-ratories is of paramount interest in order to minimize thenegative environmental impacts [4 5] Green analyticalchemistry aims at replacing nongreen analytical methodol-ogies with green alternatives that use and generate nolesshazardous chemicals [6 7] Compared to chromatographicmethods that utilize sophisticated instruments and consumeor generate large volumes of organic solvents spectropho-tometric methods are faster more economical and greenermaking them fully functional alternatives However spec-trophotometric techniques suffer from lack of selectivity es-pecially in case of severe spectral overlap and matrixinterferences the selectivity of such techniques can be en-hanced by using multivariate calibration models [8 9] edevelopment of multivariate calibration methods such asmultivariate curve resolution-alternating least squares (MCR-ALS) (based on signals mathematical resolution) enables theresolution and quantification of strongly overlapped spectraof multicomponent samples [10] MCR-ALS is characterizedby the ease of implementation and its ability to overcomebands overlapping MCR-ALS has recently attracted attentionin the analytical chemistry society and has been shown toprovide enhanced resolution and improved quantitative de-termination of different analytes in samples of increasingcomplexity such as environmental matrices [11] and phar-maceutical and agricultural samples [10 12] e main ad-vantage of using MCR-ALS is the possible recovery of thespectral information of target analytes and unknown in-terferences [9] Different analytical methods have been de-veloped for the individual assay of the OTC pain relievers andalso for some of their combinations including HPLC (eg[13ndash15]) and spectrophotometry (eg [16ndash20])

To the best of our knowledge no studies have been re-ported for simultaneous determination of paracetamol as-pirin caffeine naproxen and ibuprofen in pharmaceuticalformulations using multivariate calibration methodse aimof this work is to develop a green fast low-cost analyticalmethod utilizing MCR-ALS multivariate calibration for si-multaneous determination of the cited OTC pain relievers intheir single and coformulated products without any separa-tion step with the aid of easily accessible instruments (eg UVspectrophotometer) e proposed method is economic fasteco-friendly and amenable for routine analysis

2 Experimental

21 Instrumentation and Software A UV-1800 Shimadzudouble-beam spectrophotometer (Shimadzu Kyoto Japan)with 1 cm quartz cell was used e scanning speed was

maintained at 2800 nmmiddotminminus 1 and the wavelength range wasset from 200 to 400 nm with a bandwidth of 1 nm Spectrawere automatically obtained by Shimadzu UV-Probe 262software For performingMCR-ALS calculations MCR-ALSGUI 20 software was used with Matlab 2015a [21] eMCR-ALS algorithm is available at httpwwwmcralsinfo

22 Chemicals and Reagents e standards used in thisstudy including paracetamol (PAR) acetylsalicylic acid(ASA) ibuprofen (IPF) naproxen (NPX) and caffeine(CAF) were purchased from Sigma-Aldrich (SteinheimGermany) and certified to contain ge98 Methanol (CASNo 67-56-1) was obtained from Merck (Darmstadt Ger-many) Ultrapure water (182MΩ) purified using thePURELAB Ultra water system (ELGA HighWycombe UK)was used for sample preparation

23 Stock and Working Standard Solutions Stock solutionsof the studied drugs were prepared separately by dissolving10mg of each drug in 10ml methanol to obtain a con-centration of 1mgmiddotmLminus 1 All solutions were kept in dark at4degC Working standard solutions were freshly prepared byappropriate dilution in ultrapure water

24 Construction of Calibration and Validation Sets ecalibration model (a set of twenty-five calibration solutions)was built using a five-factor five-level design [22] to coverthe linearity ranges of 05ndash7 μgmL for PAR 05ndash35 μgmLfor NPX 05ndash3 μgmL CAF and 1ndash15 μgmL for ASA andIPF In addition a validation set of an additional 15 samplescontaining the studied drugs were similarly prepared usingdifferent concentrations within the calibration range of eachdrug Tables 1 and 2 show the calibration and validation setsrespectively e spectra of all mixtures were recorded in thewavelength ranging from 220 to 240 nm with 08 nm in-terval e data were exported into Matlab for developingtheMCR-ALSmodel in which five variables were used for allthe studied analytes

25Analysis of theCommercialPharmaceutical FormulationsA real challenge of the presented work would be to quantifythe studied drugs in commercial products presenting dif-ferent compositions and interferences In this study tencommercial pharmaceutical tablets were collected from thelocal pharmacies (Eastern Province Saudi Arabia) withhighly variable compositions regarding the excipients andsome containing other active ingredients such as codeinephosphate e specified concentration level was describedon the label of all the analyzed samples e composition ofthe analyzed samples is as follows (1) Fevadol Plusreg tablets(SPIMACO) labeled to contain 500mg of PAR 30mg ofCAF and 8mg codeine per tablet (2) Panadol Extrareg tablets(SPIMACO) labeled to contain 500mg of PAR and 65mg ofCAF per tablet (3) Panadolreg tablets (GSK) labeled tocontain 500mg of PAR per tablet (4) Adolreg tablets (Julphar)labeled to contain 500mg of PAR per tablet (5) Proxenregtablets (Grunenthal) labeled to contain 500mg of NPX per

2 Journal of Analytical Methods in Chemistry

tablet (6) Omarfenreg tablets (National Pharmaceutical In-dustries) labeled to contain 400mg of IPF per tablet (7)Disprinreg tablets (Riyadh Pharma) labeled to contain 81mgof ASA per tablet (8) Fevadolreg tablets (SPIMACO) labeledto contain 500mg of PAR per tablet (9) Riaproxereg tablets(Riyadh pharma) labeled to contain 500mg of NPX pertablet and (10) Brufenreg tablets (Abbott) labeled to contain500mg of IPF per tablet

Ten tablets of each pharmaceutical product were sepa-rately weighed and finely powdered A portion of the powderequivalent to the average tablet weight of each product wasseparately dissolved in 50mL methanol using ultrasonication

for 15min en the solution was cooled and filtrated in a100mL volumetric flask using Whatmanreg filter papers Fi-nally the volume was completed to 100mL with methanoland suitable volumes of the stock solutions were mixed anddiluted with ultrapure water to obtain different concentra-tions of the studied drugs at the specified linearity rangementioned abovee sample spectra were recorded using thesame procedures described for the calibration and test samplesets

26 Multivariate Calibration Analysis (MCR-ALS)MCR-ALS is a chemometric method that provides relevantinformation about the pure components by decomposingthe bilinear data matrix In MCR-ALS the matrix of mixedsignals (D) is decomposed into concentration and signalprofiles according to the following equation

D CST

+ E (1)

where C is the concentration matrix S is the signal matrixand E is the residual matrix (the matrix associated to ex-perimental error)

For optimizing the MCR-ALS model and achievingsolutions for C and S various constraints can be employedsuch as nonnegativity closure unimodality and correlationconstraints [22ndash25] Nonnegativity constraint enforces theconcentration andor spectral matrices to have values equalto or greater than zero Correlation constraint allowsquantitative analysis of analytes in presence of unknowninterfering components leading to enhanced accuracy of theanalysis [26] In this work both nonnegativity and corre-lation constraints are used For iteration procedures initiallyspectral data matrix D is decomposed into bilinear datamatrix according to Equation (1) followed by determinationof the optimum number of variables for resolution en C

and ST are initially estimated using simple-to-use interactiveself-modeling mixture analysis (SIMPLISMA) [26] Initialestimates of C and ST is an important step because differentestimates may lead to different results en the developedcalibration model is used for predicting concentrations insamples of both the validation and test sets Each ALS it-eration is completed after updating the obtained predictedvalues

27 Validation of the Model An external validation data setof 15 laboratory-prepared mixtures was used to evaluate thequantitative performance of the developed method bypredicting the concentration of the studied drugs in a val-idation set which was not used for the development of themodel (Table 2) e ability of the proposed model forpredicting concentrations was assessed by calculating dif-ferent figures of merit such as root mean square error ofcross validation (RMSECV) root mean square error ofprediction (RMSEP) bias standard error of prediction(SEP) and relative percentage error in the concentrationpredictions (RE) according to the following equations

Table 2 Concentration matrix (μgmL) used for the validation setof the studied drugs

Mixture PAR ASA CAF NPX IPF1 35 05 20 20 852 35 20 10 20 503 10 20 30 15 1504 10 150 15 30 855 60 50 30 20 506 20 150 20 15 507 60 85 15 15 1208 35 50 15 25 1509 20 50 25 30 12010 20 120 30 25 8511 50 150 25 20 15012 60 120 20 30 15013 50 85 30 30 2014 35 150 30 10 12015 60 150 10 25 20

Table 1 Concentration matrix (μgmL) used for the calibration setof the studied drugs

Mixture PAR ASA CAF NPX IPF1 38 80 18 20 802 38 10 05 35 453 05 10 30 13 1504 05 150 11 35 805 70 45 30 20 456 21 150 18 13 457 70 80 11 13 1158 35 45 11 28 1509 21 45 24 35 11510 21 115 30 28 8011 54 150 24 20 15012 70 115 18 35 15013 54 80 30 35 1014 38 150 30 05 11515 70 150 05 28 1016 70 10 24 05 8017 05 115 05 20 11518 54 10 18 28 11519 05 80 24 28 4520 38 115 24 13 1021 54 115 11 05 4522 54 45 05 13 8023 21 10 11 20 1024 05 45 18 05 1025 21 80 05 05 150

Journal of Analytical Methods in Chemistry 3

RMSEP

1113936ni1 ci minus 1113954ci( 1113857

2

n

1113971

bias 1113936

ni1 ci minus 1113954ci( 1113857

n

SEP

1113936ni1 ci minus 1113954ci minus bias( 1113857

2

n minus 1

1113971

RE ()

1113936ni1 ci minus 1113954ci( 1113857

2

1113936ni1c

2i

1113971

(2)

where ci and 1113954ci are the known and predicted analyteconcentrations in the sample i respectively and n is the totalnumber of samples used in the validation set Additionallycoefficients of determination (r2) were calculated for eachanalyte in the validation set

e accuracy of the proposed chemometricmethod for themeasurement of the studied drugs was tested by using thestandard addition technique at 80 100 and 120 of thetest concentration e study was performed by addition ofknown amounts of the studied drugs into a known concen-tration of the commercial pharmaceutical tabletse resultingmixtures were analyzed and the results obtained were com-pared with the expected resultse recovery of the exogenousamount added was calculated by the following equation

recovery() (X minus Y)

ZZ (3)

where recovery means the drug recovered () Y expressesthe normal concentration of sample before addition X is theconcentration of sample after addition of pure drug and Zindicates the added amount

Intraday precision (repeatability) and interday precision(reproducibility) of the developed method were evaluated bymeasuring three concentration levels that cover lowermiddle and upper limits of calibration curves

3 Results and Discussion

31 Features of the Spectra Figure 1 illustrates the UVspectra of the studied drugs PAR ASA CAF NPX IPF andtheir mixture As can be observed the spectra are severelyoverlapped therefore determination of such drugs simul-taneously in their mixtures cannot be accomplished utilizingunivariate spectrophotometric methods because of the se-vere spectral overlap As a result the MCR-ALS method wasproposed to improve the analysis of the studied drugs

32 Selection of the Spectral Zones e appropriate selectionof the wavelength range is an important factor that stronglyaffects the quality of multivariate analysis [25] Wavelengthslower than 220 nm were excluded because of the presence ofnoise in the spectra of the laboratory-prepared mixtures andpharmaceutical samples in this range Additionally wave-lengths higher than 240 nm were rejected as well because ofpoor absorption of NPX and IPF at the measured

concentration levels Overall absorbance data of the spectralregion from 220 to 240 nm were found to be the optimumchemometric region for the quantitative determination ofthe studied drugs using theMCR-ALS model After selectingthe wavelength interval suitable for the analysis of the fivedrugs in their mixtures commercial tablets were analyzedfollowing the same procedures

33MCR-ALSModel Optimization of the MCR-ALSmodelis a very important step in order to achieve optimum per-formance of the model Because of the variability in ratios ofthe studied drugs each drug was calibrated individually toprovide good results Proper selection of the latent variables(LVs) number is important to achieve correct quantitatione optimum number of LVs should be the smallest numberthat results in no significant difference between RMSECV ofthat factor and the next one [27] In this study the optimumLVs number using the leave-one-out cross-validationtechnique was five for all analyzed drugs

For MCR-ALS different constraints were optimized andsatisfactory results were obtained when applying non-negativity constraints for spectral and concentrationmatricesin addition to a correlation constraint e convergencecriterion was set at 01 and the maximum number of it-erations was 50 however no more than 8 iterations wererequired to achieve convergence in all tested samples Table 3shows the figures of merit of the calibration set of the modele results show low relative error (RE) ()le 165 for all thestudied drugs and low RMSECV (le0194) Coefficients ofdetermination (r2)ge 09993 were achieved for all studieddrugs which indicated the good ability of the model forprediction e scatter plots of resolved MCR-ALS

ASACAFIPF

MIXNPXPAR

16001500

1000

0500

000020000 25000 30000 35000 40000

Abs

Wavelength (nm)

Figure 1 UV absorption spectra of 1 μgmL of paracetamol (PAR)aspirin (ASA) ibuprofen (IPF) naproxen (NPX) caffeine (CAF)and their mixture


Table 3 Figures of merits of the calibration set for the developed MCR-ALS model

Parameters PAR ASA CAF NPX IPFCalibration range 05ndash7 1ndash15 05ndash3 05ndash35 1ndash15Slope 10000 09999 10000 10000 09999Intercept 875times10minus 15 minus 196times10minus 14 minus 202times10minus 13 251times 10minus 3 213times10minus 3

RMSECV 0136 0045 0194 0152 0064SEP 0153 0032 0170 0149 0063Bias 264times10minus 2 412times10minus 3 minus 258times10minus 2 322times10minus 2 311times 10minus 3

RE () 132 094 165 128 115Coefficient of determination (r2) 09998 09995 09993 09998 09996

0

4

8

12

16

0 4 8 12 16

Pred

icte

d co

ncen

trat

ion

Actual concentration

CalibrationValidation

(a)

0

1

2

3

4

0 1 2 3

Pred

icte

d co

ncen

trat

ion



(b)


0

4

8

12

16

0 2 4 6 8 10 12 14

Pred

icte

d co

ncen

trat

ion


(c)


0

2

4

6

8

0 2 4 6

Pred

icte

d co

ncen

trat

ion


(d)

0

1

2

3

4

0 1 2 3 4

Pred

icte

d co

ncen

trat

ion



(e)

Figure 2 Plots of actual concentrations versus predicted values of the studied drugs for the calibration and validation sets using the MCR-ALS model (a) IPF (b) CAF (c) ASA (d) PAR (e) NPX


concentration values versus the actual concentrations for thecalibration set are presented in Figure 2

34 Method Validation e developed model was validatedby predicting the concentrations of the studied drugs in anexternal validation set (fifteen mixtures) which are not in-cluded in the model development Different parameters suchas RMSEP SEP RE () and r2 were calculated to assess thepredictive ability of the developed model for the validationset (Table 4)e quality of the validation data is indicated bylow relative errors (REs)le 159 for all the studied drugse absorption spectra of each drug as well as their mixtureswere checked for their linearity thereby fulfilling the law ofadditivity for Beerrsquos law (Figure 1) ey were found to belinear in the ranges of 05ndash7 μgmL for PAR 05ndash35 μgmLfor NPX 05ndash3 μgmL CAF and 1ndash15 μgmL for ASA andIPF As can be seen in the figure the UV spectra of themixture are equivalent to the sum of the pure spectra of thestudied analytes reflecting the law of spectral additivity

Satisfactory validation results were obtained and showedthe high predictive ability of the model e plots of thepredicted concentrations versus the actual concentrationsfor the validation set are also illustrated in Figure 2e highlinearity (coefficient of determinationsge 09991) indicatedthe high prediction ability of the model

e accuracy of the proposed method was evaluated bythe standard addition method e percent recoveriesranged from 980 to 997 with RSDs not higher than162 (Table 4) ese results confirmed that the excipientsin pharmaceutical products do not interfere with the de-termination of the studied drugs Intraday precision andinterday precision of the developed method were also ex-amined by analyzing three concentration levels of thestudied drugs within the same day for intraday precision andat three consecutive days for interday precision e lowervalues of RSD (le134) indicated good precision of thedeveloped method (Table 4)

35 Analysis of Commercial Pharmaceutical Products eproposed chemometric method was used to analyze thestudied drugs in commercial tablets Five replicate de-

Table 4 Validation parameters of the developed MCR-ALS model

Parameter PAR ASA CAF NPX IPFAccuracya 981plusmn 128 995plusmn 099 980plusmn 112 989plusmn 102 997plusmn 162Intraday precisionb 093 085 123 102 123Interday precisionc 102 094 131 134 127RMSEP 0251 0099 0284 0232 0160SEP 0753 0092 0250 0219 0103Bias 354times10minus 2 499times10minus 3 minus 398times10minus 2 429times10minus 2 411times 10minus 3

RE () 152 159 135 149 143Correlation coefficient (r2) 09993 09994 09991 09996 09992aMean plusmn standard deviation for 15 determinations be intraday relative standard deviation (n 3) average of three different concentrations repeated threetimes within the day ce interday relative standard deviation (n 3) average of three different concentrations repeated three times in three different days

Table 5 Determination of the studied drugs in commercial tabletsby the MCR-ALS model by the proposed and the reported HPLCmethod

MCR-ALS HPLC

Sample 1

PAR(Mean + SD) 9922plusmn 111 9986plusmn 086t 086 mdashF 169 mdash

CAF(Mean + SD) 9974plusmn 072 998plusmn 070t 050 mdashF 108 mdash

Sample 2


CAF(Mean + SD) 9914plusmn 128 990plusmn 144t 009F 079

Sample 3


Sample 4

PAR(Mean + SD) 995plusmn 145 1005plusmn 147t 107 -F 097 -

Sample 5

NPX(Mean + SD) 987plusmn 150 999plusmn 125t 267 mdashF 144 mdash

Sample 6

IPF(Mean + SD) 996plusmn 177 991plusmn 149t 059 mdashF 140 mdash

Sample 7

ASA(Mean + SD) 1002plusmn 185 999plusmn 158t 034 mdashF 137 mdash

Sample 8



terminations were performed Table 5 represents the resultsfor the quantification of the studied drugs in their commercialtablets e results of the proposed MCR-ALS model wasstatistically compared with a reported HPLC method [28]using paired t-test and F-ratio at 95 confidence level erewas no significant difference between the proposed and thereference method regarding accuracy and precision (Table 5)

4 Conclusion

An eco-friendly fast and accurate MCR-ALS chemometricmethod for the quantification of the most widely consumedOTC pain relievers has been developed and validated eproposed model proved to be a green nondestructive andlow-cost alternative to chromatographic techniques for thedetermination of the studied drugs in pure and commercialpharmaceutical formulations e developed MCR-ALSmethod can be used as a green alternative for the analysis ofthe studied drugs without sample preparation steps andexpensive solvents especially in developing countries whereresources are limited

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors declare that they have no conflicts of interest todisclose

Acknowledgments

e authors gratefully acknowledge the College of ClinicalPharmacy Immam Abdel Rahman Bin Faisal University forproviding research facilities

References

[1] B McCarberg and A Gibofsky ldquoNeed to develop newnonsteroidal anti-inflammatory drug formulationsrdquo Clinicalerapeutics vol 34 no 9 pp 1954ndash1963 2012

[2] S S Adams P Bresloff and C G Mason ldquoPharmacologicaldifferences between the optical isomers of ibuprofen evidencefor metabolic inversion of the (-)-isomerrdquo Journal of Phar-macy and Pharmacology vol 28 no 3 pp 256-257 1976

[3] M William Martindale e Complete Drug ReferenceM William and K Parfitt Eds Pharmaceutical Press Lon-don UK 32nd edition 1999

[4] H Shaaban and T Gorecki ldquoCurrent trends in green liquidchromatography for the analysis of pharmaceutically activecompounds in the environmental water compartmentsrdquoTalanta vol 132 pp 739ndash752 2015

[5] H Shaaban ldquoNew insights into liquid chromatography formore eco-friendly analysis of pharmaceuticalsrdquo Analyticaland Bioanalytical Chemistry vol 408 no 25 pp 6929ndash69442016

[6] H Shaaban and A Mostafa ldquoSustainable eco-friendly ultra-high-performance liquid chromatographic method for si-multaneous determination of caffeine and theobromine incommercial teas evaluation of greenness profile using NEMIand eco-scale assessment toolsrdquo Journal of AOAC In-ternational vol 101 no 6 pp 1781ndash1787 2018

[7] M Tobiszewski ldquoMetrics for green analytical chemistryrdquoAnalytical Methods vol 8 no 15 pp 2993ndash2999 2016

[8] A El-Gindy S Emara and H Shaaban ldquoValidation andapplication of chemometrics-assisted spectrophotometry andliquid chromatography for simultaneous determination oftwo ternary mixtures containing drotaverine hydrochloriderdquoJournalmdashAssociation of Official Analytical Chemists vol 93no 2 pp 536ndash548 2010

[9] H Parastar and H Shaye ldquoComparative study of partial leastsquares and multivariate curve resolution for simultaneousspectrophotometric determination of pharmaceuticals inenvironmental samplesrdquo RSC Advances vol 5 no 86pp 70017ndash70024 2015

[10] A Mostafa H Shaaban M Almousa M Al Sheqawi andM Almousa ldquoEco-friendly pharmaceutical analysis of mul-ticomponent drugs coformulated in different dosage formsusing multivariate curve resolution and partial least squares acomparative studyrdquo Journal of AOAC International vol 102no 2 pp 465ndash472 2019

[11] A Mostafa and H Shaaban ldquoQuantitative analysis and res-olution of pharmaceuticals in the environment using multi-variate curve resolution-alternating least squares (MCR-ALS)rdquo Acta Pharmaceutica vol 69 no 2 pp 217ndash231 2019

[12] A El-Gindy S Emara and H Shaaban ldquoDevelopment andvalidation of chemometrics-assisted spectrophotometric andliquid chromatographic methods for the simultaneous de-termination of two multicomponent mixtures containingbronchodilator drugsrdquo Journal of Pharmaceutical and Bio-medical Analysis vol 43 no 3 pp 973ndash982 2007

[13] R R Cunha S C Chaves M M A C Ribeiro et al ldquoSi-multaneous determination of caffeine paracetamol andibuprofen in pharmaceutical formulations by high-perfor-mance liquid chromatography with UV detection and bycapillary electrophoresis with conductivity detectionrdquo Journalof Separation Science vol 38 no 10 pp 1657ndash1662 2015

[14] F Belal F Ibrahim Z A Sheribah and H Alaa ldquoNewspectrophotometricchemometric assisted methods for thesimultaneous determination of imatinib gemifloxacin nal-buphine and naproxen in pharmaceutical formulations andhuman urinerdquo Spectrochimica Acta Part A Molecular andBiomolecular Spectroscopy vol 198 pp 51ndash60 2018

[15] M A A Ragab and E I El-Kimary ldquoHigh performance liquidchromatography with photo diode array for separation and

Table 5 Continued

MCR-ALS HPLC

Sample 9


Sample 10


e reference HPLC published method used C18 (250times 46mm 50 μm)column at 35degC and themobile phase composed of 15mMphosphate buffer(pH 325) and acetonitrile using gradient elution at 11mLmiddotminminus 1 flow rateSD standard deviation of the mean of the percentage recovery from thelabel claim amount for 5 determinations eoretical values for t and F atp 005 are 278 and 639 respectively


analysis of naproxen and esomeprazole in presence of theirchiral impurities enantiomeric purity determination in tab-letsrdquo Journal of Chromatography A vol 1497 pp 110ndash1172017

[16] M R Khoshayand H Abdollahi M ShariatpanahiA Saadatfard and A Mohammadi ldquoSimultaneous spectro-photometric determination of paracetamol ibuprofen andcaffeine in pharmaceuticals by chemometric methodsrdquoSpectrochimica Acta Part A Molecular and BiomolecularSpectroscopy vol 70 no 3 pp 491ndash499 2008

[17] L Pinto F Stechi and M C Breitkreitz ldquoA simplified andversatile multivariate calibration procedure for multiproductquantification of pharmaceutical drugs in the presence ofinterferences using first order data and chemometricsrdquoMicrochemical Journal vol 146 pp 202ndash209 2019

[18] V D Hoang D T H Ly N H o and H M T NguyenldquoUV spectrophotometric simultaneous determination ofparacetamol and ibuprofen in combined tablets by derivativeand wavelet transformsrdquo e Scientific World Journalvol 2014 Article ID 313609 13 pages 2014

[19] M Knochen J Giglio and B F Reis ldquoFlow-injection spec-trophotometric determination of paracetamol in tablets andoral solutionsrdquo Journal of Pharmaceutical and BiomedicalAnalysis vol 33 no 2 pp 191ndash197 2003

[20] Y A Salem M E A Hammouda M A Abu El-Enin andS M El-Ashry ldquoApplication of derivative emission fluores-cence spectroscopy for determination of ibuprofen andphenylephrine simultaneously in tablets and biologicalfluidsrdquo Spectrochimica Acta Part A Molecular and Bio-molecular Spectroscopy vol 210 pp 387ndash397 2019

[21] J Jaumot A de Juan and R Tauler ldquoMCR-ALS GUI 20 newfeatures and applicationsrdquo Chemometrics and IntelligentLaboratory Systems vol 140 pp 1ndash12 2015

[22] A de Juan and R Tauler ldquoChemometrics applied to unravelmulticomponent processes and mixturesrdquo Analytica ChimicaActa vol 500 no 1-2 pp 195ndash210 2003

[23] A de Juan J Jaumot and R Tauler ldquoMultivariate curveresolution (MCR) solving the mixture analysis problemrdquoAnalytical Methods vol 6 no 14 pp 4964ndash4976 2014

[24] J Jaumot R Gargallo A de Juan and R Tauler ldquoA graphicaluser-friendly interface for MCR-ALS a new tool for multi-variate curve resolution in MATLABrdquo Chemometrics andIntelligent Laboratory Systems vol 76 no 1 pp 101ndash1102005

[25] M Blanco J Coello F Gonzalez H Iturriaga andS Maspoch ldquoSpectrophotometric analysis of a pharmaceu-tical preparation by principal component regressionrdquo Journalof Pharmaceutical Sciences vol 82 no 8 pp 834ndash837 1993

[26] W Windig and J Guilment ldquoInteractive self-modelingmixture analysisrdquo Analytical Chemistry vol 63 no 14pp 1425ndash1432 1991

[27] D M Haaland and E V omas ldquoPartial least-squaresmethods for spectral analyses 1 Relation to other quantitativecalibration methods and the extraction of qualitative in-formationrdquo Analytical Chemistry vol 60 no 11 pp 1193ndash1202 1988

[28] P N Patel G Samanthula V Shrigod S C Modh andJ R Chaudhari ldquoRP-HPLC method for determination ofseveral NSAIDs and their combination drugsrdquo Chromatog-raphy Research International vol 2013 Article ID 24286813 pages 2013


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their activity by sim40 because of the ability of caffeine tofacilitate the absorption of other drugs and to cause con-striction of the cerebral blood vessels [3] Naproxen is also aNSAID drug that acts by inhibition of prostaglandin synthesis[1]

Because of the growing consumption of OTC pain re-lievers controlling the quality of these medications is highlyrequired in order to ensure their efficiency erefore de-veloping new reliable fast and cost-effective methods foranalyzing such drugs is of paramount importance

Implementation of green procedures in analytical labo-ratories is of paramount interest in order to minimize thenegative environmental impacts [4 5] Green analyticalchemistry aims at replacing nongreen analytical methodol-ogies with green alternatives that use and generate nolesshazardous chemicals [6 7] Compared to chromatographicmethods that utilize sophisticated instruments and consumeor generate large volumes of organic solvents spectropho-tometric methods are faster more economical and greenermaking them fully functional alternatives However spec-trophotometric techniques suffer from lack of selectivity es-pecially in case of severe spectral overlap and matrixinterferences the selectivity of such techniques can be en-hanced by using multivariate calibration models [8 9] edevelopment of multivariate calibration methods such asmultivariate curve resolution-alternating least squares (MCR-ALS) (based on signals mathematical resolution) enables theresolution and quantification of strongly overlapped spectraof multicomponent samples [10] MCR-ALS is characterizedby the ease of implementation and its ability to overcomebands overlapping MCR-ALS has recently attracted attentionin the analytical chemistry society and has been shown toprovide enhanced resolution and improved quantitative de-termination of different analytes in samples of increasingcomplexity such as environmental matrices [11] and phar-maceutical and agricultural samples [10 12] e main ad-vantage of using MCR-ALS is the possible recovery of thespectral information of target analytes and unknown in-terferences [9] Different analytical methods have been de-veloped for the individual assay of the OTC pain relievers andalso for some of their combinations including HPLC (eg[13ndash15]) and spectrophotometry (eg [16ndash20])

To the best of our knowledge no studies have been re-ported for simultaneous determination of paracetamol as-pirin caffeine naproxen and ibuprofen in pharmaceuticalformulations using multivariate calibration methodse aimof this work is to develop a green fast low-cost analyticalmethod utilizing MCR-ALS multivariate calibration for si-multaneous determination of the cited OTC pain relievers intheir single and coformulated products without any separa-tion step with the aid of easily accessible instruments (eg UVspectrophotometer) e proposed method is economic fasteco-friendly and amenable for routine analysis

2 Experimental

21 Instrumentation and Software A UV-1800 Shimadzudouble-beam spectrophotometer (Shimadzu Kyoto Japan)with 1 cm quartz cell was used e scanning speed was

maintained at 2800 nmmiddotminminus 1 and the wavelength range wasset from 200 to 400 nm with a bandwidth of 1 nm Spectrawere automatically obtained by Shimadzu UV-Probe 262software For performingMCR-ALS calculations MCR-ALSGUI 20 software was used with Matlab 2015a [21] eMCR-ALS algorithm is available at httpwwwmcralsinfo

22 Chemicals and Reagents e standards used in thisstudy including paracetamol (PAR) acetylsalicylic acid(ASA) ibuprofen (IPF) naproxen (NPX) and caffeine(CAF) were purchased from Sigma-Aldrich (SteinheimGermany) and certified to contain ge98 Methanol (CASNo 67-56-1) was obtained from Merck (Darmstadt Ger-many) Ultrapure water (182MΩ) purified using thePURELAB Ultra water system (ELGA HighWycombe UK)was used for sample preparation

23 Stock and Working Standard Solutions Stock solutionsof the studied drugs were prepared separately by dissolving10mg of each drug in 10ml methanol to obtain a con-centration of 1mgmiddotmLminus 1 All solutions were kept in dark at4degC Working standard solutions were freshly prepared byappropriate dilution in ultrapure water

24 Construction of Calibration and Validation Sets ecalibration model (a set of twenty-five calibration solutions)was built using a five-factor five-level design [22] to coverthe linearity ranges of 05ndash7 μgmL for PAR 05ndash35 μgmLfor NPX 05ndash3 μgmL CAF and 1ndash15 μgmL for ASA andIPF In addition a validation set of an additional 15 samplescontaining the studied drugs were similarly prepared usingdifferent concentrations within the calibration range of eachdrug Tables 1 and 2 show the calibration and validation setsrespectively e spectra of all mixtures were recorded in thewavelength ranging from 220 to 240 nm with 08 nm in-terval e data were exported into Matlab for developingtheMCR-ALSmodel in which five variables were used for allthe studied analytes

25Analysis of theCommercialPharmaceutical FormulationsA real challenge of the presented work would be to quantifythe studied drugs in commercial products presenting dif-ferent compositions and interferences In this study tencommercial pharmaceutical tablets were collected from thelocal pharmacies (Eastern Province Saudi Arabia) withhighly variable compositions regarding the excipients andsome containing other active ingredients such as codeinephosphate e specified concentration level was describedon the label of all the analyzed samples e composition ofthe analyzed samples is as follows (1) Fevadol Plusreg tablets(SPIMACO) labeled to contain 500mg of PAR 30mg ofCAF and 8mg codeine per tablet (2) Panadol Extrareg tablets(SPIMACO) labeled to contain 500mg of PAR and 65mg ofCAF per tablet (3) Panadolreg tablets (GSK) labeled tocontain 500mg of PAR per tablet (4) Adolreg tablets (Julphar)labeled to contain 500mg of PAR per tablet (5) Proxenregtablets (Grunenthal) labeled to contain 500mg of NPX per






D CST

+ E (1)










RMSEP

1113936ni1 ci minus 1113954ci( 1113857

2

n

1113971

bias 1113936

ni1 ci minus 1113954ci( 1113857

n

SEP


2

n minus 1

1113971

RE ()

1113936ni1 ci minus 1113954ci( 1113857

2

1113936ni1c

2i

1113971

(2)




ZZ (3)









ASACAFIPF

MIXNPXPAR

16001500

1000

0500

000020000 25000 30000 35000 40000

Abs

Wavelength (nm)







0

4

8

12

16

0 4 8 12 16

Pred

icte

d co

ncen

trat

ion



(a)

0

1

2

3

4

0 1 2 3

Pred

icte

d co

ncen

trat

ion



(b)


0

4

8

12

16

0 2 4 6 8 10 12 14

Pred

icte

d co

ncen

trat

ion


(c)


0

2

4

6

8

0 2 4 6

Pred

icte

d co

ncen

trat

ion


(d)

0

1

2

3

4

0 1 2 3 4

Pred

icte

d co

ncen

trat

ion



(e)












MCR-ALS HPLC

Sample 1



Sample 2



Sample 3


Sample 4


Sample 5


Sample 6


Sample 7


Sample 8




4 Conclusion


Data Availability




Acknowledgments


References
















Table 5 Continued

MCR-ALS HPLC

Sample 9


Sample 10
























Journal of

Chemistry





Journal of

Volume 2018






Volume 2018




Journal of






Enzyme Research


Journal of



MaterialsJournal of






Na

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D CST

+ E (1)










RMSEP

1113936ni1 ci minus 1113954ci( 1113857

2

n

1113971

bias 1113936

ni1 ci minus 1113954ci( 1113857

n

SEP


2

n minus 1

1113971

RE ()

1113936ni1 ci minus 1113954ci( 1113857

2

1113936ni1c

2i

1113971

(2)




ZZ (3)









ASACAFIPF

MIXNPXPAR

16001500

1000

0500

000020000 25000 30000 35000 40000

Abs

Wavelength (nm)







0

4

8

12

16

0 4 8 12 16

Pred

icte

d co

ncen

trat

ion



(a)

0

1

2

3

4

0 1 2 3

Pred

icte

d co

ncen

trat

ion



(b)


0

4

8

12

16

0 2 4 6 8 10 12 14

Pred

icte

d co

ncen

trat

ion


(c)


0

2

4

6

8

0 2 4 6

Pred

icte

d co

ncen

trat

ion


(d)

0

1

2

3

4

0 1 2 3 4

Pred

icte

d co

ncen

trat

ion



(e)












MCR-ALS HPLC

Sample 1



Sample 2



Sample 3


Sample 4


Sample 5


Sample 6


Sample 7


Sample 8




4 Conclusion


Data Availability




Acknowledgments


References
















Table 5 Continued

MCR-ALS HPLC

Sample 9


Sample 10
























Journal of

Chemistry





Journal of

Volume 2018






Volume 2018




Journal of






Enzyme Research


Journal of



MaterialsJournal of






Na

nom

ate

ria

ls




RMSEP

1113936ni1 ci minus 1113954ci( 1113857

2

n

1113971

bias 1113936

ni1 ci minus 1113954ci( 1113857

n

SEP


2

n minus 1

1113971

RE ()

1113936ni1 ci minus 1113954ci( 1113857

2

1113936ni1c

2i

1113971

(2)




ZZ (3)









ASACAFIPF

MIXNPXPAR

16001500

1000

0500

000020000 25000 30000 35000 40000

Abs

Wavelength (nm)







0

4

8

12

16

0 4 8 12 16

Pred

icte

d co

ncen

trat

ion



(a)

0

1

2

3

4

0 1 2 3

Pred

icte

d co

ncen

trat

ion



(b)


0

4

8

12

16

0 2 4 6 8 10 12 14

Pred

icte

d co

ncen

trat

ion


(c)


0

2

4

6

8

0 2 4 6

Pred

icte

d co

ncen

trat

ion


(d)

0

1

2

3

4

0 1 2 3 4

Pred

icte

d co

ncen

trat

ion



(e)












MCR-ALS HPLC

Sample 1



Sample 2



Sample 3


Sample 4


Sample 5


Sample 6


Sample 7


Sample 8




4 Conclusion


Data Availability




Acknowledgments


References
















Table 5 Continued

MCR-ALS HPLC

Sample 9


Sample 10
























Journal of

Chemistry





Journal of

Volume 2018






Volume 2018




Journal of






Enzyme Research


Journal of



MaterialsJournal of






Na

nom

ate

ria

ls








0

4

8

12

16

0 4 8 12 16

Pred

icte

d co

ncen

trat

ion



(a)

0

1

2

3

4

0 1 2 3

Pred

icte

d co

ncen

trat

ion



(b)


0

4

8

12

16

0 2 4 6 8 10 12 14

Pred

icte

d co

ncen

trat

ion


(c)


0

2

4

6

8

0 2 4 6

Pred

icte

d co

ncen

trat

ion


(d)

0

1

2

3

4

0 1 2 3 4

Pred

icte

d co

ncen

trat

ion



(e)












MCR-ALS HPLC

Sample 1



Sample 2



Sample 3


Sample 4


Sample 5


Sample 6


Sample 7


Sample 8




4 Conclusion


Data Availability




Acknowledgments


References
















Table 5 Continued

MCR-ALS HPLC

Sample 9


Sample 10
























Journal of

Chemistry





Journal of

Volume 2018






Volume 2018




Journal of






Enzyme Research


Journal of



MaterialsJournal of






Na

nom

ate

ria

ls













MCR-ALS HPLC

Sample 1



Sample 2



Sample 3


Sample 4


Sample 5


Sample 6


Sample 7


Sample 8




4 Conclusion


Data Availability




Acknowledgments


References
















Table 5 Continued

MCR-ALS HPLC

Sample 9


Sample 10
























Journal of

Chemistry





Journal of

Volume 2018






Volume 2018




Journal of






Enzyme Research


Journal of



MaterialsJournal of






Na

nom

ate

ria

ls





4 Conclusion


Data Availability




Acknowledgments


References
















Table 5 Continued

MCR-ALS HPLC

Sample 9


Sample 10
























Journal of

Chemistry





Journal of

Volume 2018






Volume 2018




Journal of






Enzyme Research


Journal of



MaterialsJournal of






Na

nom

ate

ria

ls
























Journal of

Chemistry





Journal of

Volume 2018






Volume 2018




Journal of






Enzyme Research


Journal of



MaterialsJournal of






Na

nom

ate

ria

ls




Simultaneous Determination of Over-the-Counter Pain...

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