Original article revista.iq.unesp.br | Vol. 48 | n. 1 | 2023 | 72 Eclética Química, vol. 48, n. 1, 2023, 72-94 ISSN: 1678-4618 DOI: 10.26850/1678-4618eqj.v48.1.2023.p72-94 Development and validation of a new spectrophotometric method for simultaneous determination of sitagliptin and metformin hydrochloride in tablet pharmaceutical dosage forms using chemometrics technique in comparison with HPLC Maher Ali Almaqtari1+ , Najat Ahmed Al-Odaini1 , Fares Abdullah Alarbagi1 , Hussein Al-Maydama1 1. Sana’a University, Faculty of Science, Sana’a, Yemen. +Corresponding author: Maher Ali Almaqtari, Phone: +967 773262252, Email address: m.almaqtari@su.edu.ye ARTICLE INFO Article history: Received: August 21, 2022 Accepted: November 16, 2022 Published: January 11, 2023 Keywords: 1. sitagliptin 2. metformin hydrochloride 3. spectrophotometric method 4. chemometrics technique 5. validation Section Editors: Assis Vicente Benedetti ABSTRACT: A new, quick, easy, affordable and eco-friendly simultaneous spectrophotometric method for determining a combined sitagliptin and metformin hydrochloride in pharmaceutical formulations was developed and validated using two chemometrics technique. These two methods are the partial least square (PLS) and principal component regression (PCR). They do not need to do a sample preparation or separation before analysis. Various drug concentrations and instrumental spectra of 25 mixed solutions of a combination of sitagliptin and metformin hydrochloride were used for model construction in the range of 200–270 nm. The R2 values of 0.9994 and 0.9996 assigned for the PLS of the sitagliptin and metformin hydrochloride and that of 0.9987 and 0.9996 for the PCR of the sitagliptin and metformin hydrochloride, respectively. It is noteworthy that these two models were successfully and effectively used with the commercial pharmaceutical formulations. Finally, the statistical comparison revealed no significant differences with the results of the HPLC reference method. The proposed method is dependable to be adopted as an alternative analytical method in the pharmaceutical industry’s quality control. https://revista.iq.unesp.br/index.php/ecletica https://doi.org/10.26850/1678-4618eqj.v48.1.2023.p72-94 mailto:m.almaqtari@su.edu.ye https://orcid.org/0000-0002-2512-2325 https://orcid.org/0000-0002-7123-503X https://orcid.org/0000-0003-1282-8174 https://orcid.org/0000-0002-0455-8074 Original Article revista.iq.unesp.br 73 Eclética Química, vol. 48, n. 1, 2023, 72-94 ISSN: 1678-4618 DOI: 10.26850/1678-4618eqj.v48.1.2023.p72-94 1. Introduction Chemically, sitagliptin is (3R)-3-purcino-l-[3- (trifiuoromethyl)-5,6-dihydro[1,2,4] triazolo [4,3- a]pyrazin-7 (8H)-yl] -4-(2,4,S-trifiuorophenyl) butan-l- one phosphate monohydrate (Fig. 1a). It is used as dipeptidylpeptidase-4 inhibitor; treatment of diabetes mellitus (British Pharmacopoeia Commission, 2020; Swamy et al., 2020). Metformin HCl is 1,1-dimethylbiguanide hydrochloride and its chemical structure is presented in (Fig. 1b). It is used to treat diabetes mellitus. It is also used to treat polycystic ovarian syndrome. It is taken by mouth and is not linked to weight gain. It is sometimes used as an off-label supplement to help persons who are taking antipsychotics avoid gaining weight (British Pharmacopoeia Commission, 2020). Figure 1. Chemical structure of sitagliptin (a) and metformin hydrochloride (b). Uddin et al. (2019) reported that high-performance liquid chromatography (HPLC) is a technique that collects data from simultaneous separation and determination and is more frequently employed in analytical processes for the analysis of pharmaceutical products. However, it has several disadvantages, including the possibility of being bad for the environment and people’s health. The HPLC assay also needed a lot of costly chemicals and supplies. Furthermore, it takes a lot of time, which delays the marketing and production operations. The expense of HPLC maintenance is likewise substantial. Spectrophotometry, which is simple, dependable, rapid, economical, and most significantly, environmentally benign, may be a useful option for determining a complicated combination in pharmaceutical quality control laboratories. Additionally, the data show that spectrophotometry and chemometrics in conjugation have a promising future and can be used in place of HPLC in both quantitative and qualitative analysis. The study of chemometrics has significantly influenced analytical chemistry, notably in the field of spectrum analysis, which is crucial for the quality assurance of pharmaceutical formulations including two or more pharmaceuticals with overlapping spectra (K. Patel et al., 2013a; Glavanović et al., 2016). Chemometrics approaches rely on multivariate analysis, which necessitates that ultraviolet (UV) spectrophotometry methods consider multiple variables at once. The absorbance at each wavelength is taken into account, with many wavelengths being taken into consideration (Gandhi et al., 2017; R. Patel and Mashru, 2019). The principal component regression (PCR) and partial least squares (PLS) are the two most significant chemometrics techniques utilized in multivariate analysis. For the purpose of determining the combination of medications in pharmaceutical formulations, these multivariate calibration methods employ spectrophotometric data coupled with statistical tools, mathematical models, and software (R. Patel and Mashru, 2019). These techniques additionally rely on the mathematical model’s calibration using the absorbance data of calibration standards with known concentrations, which is followed by the prediction of the concentration of unknown samples using those samples’ absorbance data (Gandhi et al., 2017; R. Patel and Mashru, 2019). There are many applications for chemometrics in analytical spectroscopy, including UV-visible spectrophotometry (UV-VIS) (Ashour et al., 2015; Attia et al., 2018; Belal et al., 2018; Darbandi et al., 2020; Elfatatry et al., 2016; Gholse et al., 2021; Manouchehri et al., 2016; Moussa et al., 2021; M. Patel et al., 2013b; Phechkrajang et al., 2015; Putri et al., 2021; Sebaiy et al., 2020; 2022; V. D. Singh and V. K. Singh, 2021; Vichare et al., 2010), fluorescence spectroscopy (Manouchehri et al., 2016; Salem et al., 2019; Shinde and Divva, 2015; Walash et al., 2011; Zhu et al., 2016), NIR spectroscopy (Manouchehri et al., 2016; Moroni et al., 2022; Muntean et al., 2017; 2021; Rahman et al., 2020; Sun et al., 2021) and FTIR spectroscopic method (Rahman et al., 2020). Furthermore, chromatography methods like liquid chromatography (Aminu et al., 2019; https://revista.iq.unesp.br/index.php/ecletica https://doi.org/10.26850/1678-4618eqj.v48.1.2023.p72-94 Original Article revista.iq.unesp.br 74 Eclética Química, vol. 48, n. 1, 2023, 72-94 ISSN: 1678-4618 DOI: 10.26850/1678-4618eqj.v48.1.2023.p72-94 Mohammed et al., 2021; Tsvetkova et al., 2012; Vu Dang et al., 2020) along with a number of other analytical chemistry methods, such as flow-injection analysis are used for the pharmaceutical formulations (Ortega-Barrales et al., 2002; Silva et al., 2011). Uddin et al. (2019) reported that the majority of the analytes of interest are accompanied in their dosage forms by other compounds that absorb in the same spectral region, making it impossible to distinguish them using the traditional UV spectral studies. It is challenging to use traditional techniques like extraction because they demand a large amount of solvent, which carries risks of analyte loss or contamination as well as the potential for incomplete separation, which is expensive and time- consuming. However, spectrophotometry, as a quick, accurate, low-cost, and easy technology, may be a wonderful choice when used with chemometric techniques for determining a combined mixture in pharmaceutical quality control. When pharmaceutical product quality monitoring calls for dependable, precise, and quick analytical techniques, they are beneficial. This method, which is quick, accurate, and simple to use, avoids the usage of earlier separation procedures. Many methods for quantifying sitagliptin and metformin hydrochloride have been published, including chromatographic (Adsul et al., 2018; Krishnan and Mishra, 2020; Kumar et al., 2017) and spectrophotometric approaches (Himabindu et al., 2016; Lotfy et al., 2015). At the time of writing, we had the following information to our knowledge, there is no reference in the analytical literature reviews for the development and validation of simultaneous spectrophotometric method assisted chemometrics methods for the determination of sitagliptin with metformin HCl in pharmaceutical dosage form. This study aims to develop and validate an adequate and reproducible simultaneous spectrophotometric assay method for the determination of sitagliptin and metformin HCl in tablet pharmaceutical dosage forms using chemometrics technique. 2. Materials and methods 2.1 Materials and Reagents The reference standard of sitagliptin (as phosphate monohydrate) and metformin HCl were obtained from Global Pharma Company, Sana’a, Yemen. All reagents and chemicals used for the spectrophotometric methods were of analytical grade and HPLC grade were used for the HPLC method. Deionized water (with specific conductance of 0.05 µS cm–1) was produced in-house and used for the preparation of all samples solutions. 2.2 Instrumentation Double beam UV-VIS (AnalytiK Jena) model (SPECORD 200) at Sana’a University-Faculty of Science was used for the absorbance measurements. The HPLC system was from JASCO with detector (UV-2070 Plus), pump (PU-2089), an auto sampler (AS-2055 Plus) and a column oven (CO-2067 Plus). Electronic balance (AA-160), Denver Instrument. Electronic balance (GH- 252), AND. Electronic balance (GR-120), AND. pH meter (3520), Jenway. Centrifuge (Z326 K), Hermle were also used. 2.3 Development and validations procedures For the aim of developing an accurate, precise and dependable simultaneous spectrophotometric methods assisted with the chemometrics technique, the analytical methods were established and developed to get the intended results for quantifying the targeted components. 2.3.1 Selection of Solvent Literature reviews were conducted to identify the proper solvents that aid in dissolving the desired active pharmaceutical ingredients without excipients. Through a series of trial-and-error attempts, a suitable solvent was chosen. Other advantages for selecting the appropriate solvent such as available, easy to use, a cheap, environmentally friendly and for the spectrophotometric method implementation were given a full consideration. 2.3.2 Selection of spectral zones analysis After the phase of choosing the solvent and before the data is preprocessed, the range of 200–400 nm with a 0.2 nm interval was used to record the individual pure and mixed absorbance spectra of the targeted medicinal components. UV spectra of the mixtures analysis were selected among a suitable wavelength range against a solvent blank providing the greatest amount of information about the two components (Shah and Jasani, 2017). 2.3.3 Construction of the training set As the training set (calibration set), twenty-five different concentrations of the binary mixture of sitagliptin and metformin HCl were prepared to construct https://revista.iq.unesp.br/index.php/ecletica https://doi.org/10.26850/1678-4618eqj.v48.1.2023.p72-94 Original Article revista.iq.unesp.br 75 Eclética Química, vol. 48, n. 1, 2023, 72-94 ISSN: 1678-4618 DOI: 10.26850/1678-4618eqj.v48.1.2023.p72-94 the model. These mixtures’ absorbencies were measured against a blank at intervals of 0.2 nm between 200 and 400 nm. 2.3.4 Construction of chemometric models The two multivariate calibration models; the PLS and the PCR analysis were established as follows: • To begin with, binary mixture absorbencies were measured against a blank, and the spectra were saved and extracted into Microsoft Excel in order to develop models; • Secondly, using absorption data at chosen spectral zones for analysis at intervals of 0.2 nm, the PCR and PLS models were built using the Minitab 17 program; • Then, the required number of latent variables was obtained using the leave-one-out cross validation method; • After that, the calibration samples, constants, and coefficients for each wavelength were calculated in order to calculate the predicted concentrations; • In the end, the predicted concentrations were compared to the actual concentrations in each sample to compute the assay of binary mixture in each sample; • The root mean square error of cross-validation (RMSECV), which must be as small as possible for a given model, was determined for each method to assess the precision and accuracy of predictions for the models using the following Eq. 1 (Shah and Jasani, 2017): RMSECV = √ ∑(𝑪act−𝑪pre)𝟐 𝑰c (1) where RMSECV = Root mean square error of cross validation; Cact = Actual concentration of calibration set; Cpre = predicted concentration of calibration set; and Ic = Total number of samples in calibration set. 2.3.5 Validation and construction of the validation set In order to validate and assess the performance of the suggested and developed spectrophotometric methods assisted chemometric models, these methods were subjected to validation set. Also, the performance criteria of the developed methods including linearity, accuracy, precision (repeatability) and specificity were validated in accordance with the recommendations of International Conference Harmonization and after that determined. 2.4 Developed analytical method procedures for sitagliptin with metformin HCl determination and comparing with reference methods The performance of the proposed and developed method was determined in accordance with the method validation results. This method was studied and tested for determination of sitagliptin and metformin HCl in marketed pharmaceutical formulations. And they were compared with analysis results of reference method. 2.4.1 Preparation of standard stock solution Stock solutions of 1670 μg mL–1 of sitagliptin and 1000 μg mL–1 of metformin hydrochloride were individually prepared in a 100 mL volumetric flask by dissolving 167 mg sitagliptin and 100 mg metformin hydrochloride separately in water. 2.4.2 Preparation of working standard solution 2.4.2.1 Construction of the calibration (training) set Twenty-five binary mixtures of sitagliptin and metformin hydrochloride were prepared by transferring different aliquots of their standard stock solutions into a series of 50 mL volumetric flasks. The absorbencies of these mixtures were measured between 200 and 400 nm at 0.2 nm intervals against water as a blank. 2.4.2.2 Construction of the validation set A set of twelve binary mixtures of sitagliptin and metformin hydrochloride was prepared by transferring different volumes into 50 mL volumetric flasks and the procedure under the construction of the training set was repeated. 2.4.2.3 Preparation of spiked samples Powdered tablets of 25 mg of sitagliptin and 250 mg of metformin hydrochloride were accurately weighed, transferred to a 250 mL volumetric flask and then 200 mL of water was added, the mixture was shaken for 5 min and with frequent shaking the volume completion to 250 mL with the selected solvent was carried out. The solution was then filtered. A 0.5 mL of the filtrate was transferred into 50 mL volumetric flask and calculated amount of sitagliptin and metformin hydrochloride from standard solutions were spiked into sample solution and then diluted with water up to 50 mL. The absorbance was then measured. https://revista.iq.unesp.br/index.php/ecletica https://doi.org/10.26850/1678-4618eqj.v48.1.2023.p72-94 Original Article revista.iq.unesp.br 76 Eclética Química, vol. 48, n. 1, 2023, 72-94 ISSN: 1678-4618 DOI: 10.26850/1678-4618eqj.v48.1.2023.p72-94 2.4.2.4 Analysis of marketed formulations The developed method was applied to the measurement of a commercially available samples. It was carried out using the marketed formulation with concentration of 50 mg sitagliptin and 500 mg metformin hydrochloride. The tablets solution prepared in the sample preparation section was diluted with water to prepare solutions with concentration of 10.68 μg mL–1 sitagliptin and of 14 μg mL–1 metformin hydrochloride. The spectra of the prepared solutions were recorded and then the developed multivariate models PCR and PLS were applied to determine the concentrations of the sitagliptin and metformin HCl. 2.4.2.5 Comparing the suggested method with reference method Comparison was carried out with the recovery results of the newly developed methods and that of reference method for each of sitagliptin with metformin hydrochloride according to the United States Pharmacopeia (USP, 43). 80 μg mL–1 sitagliptin was prepared by dissolving 80 mg sitagliptin in acetonitrile: dilute phosphoric acid (5:95) in a 100 mL volumetric flask as standard stock solution; 5 mL of sitagliptin of the standard stock solution was transferred in acetonitrile: dilute phosphoric acid (5:95) in 50 mL volumetric flask. A test sample was prepared by placing 10 tablets containing 500 mg of sitagliptin to 500 mL volumetric flask; 500 mL of acetonitrile: dilute phosphoric acid (5:95) as solvent was added and the solution was shaken for 1 h then a portion of the solution was centrifuged for 10 min; 2 mL of the supernatant solution was transferred into 25 mL volumetric flask and diluted with solvent. The standard and the test sample of sitagliptin were injected through an HPLC system with a mixture of acetonitrile: monobasic potassium phosphate buffer (pH adjusted to 2 with phosphoric acid) (15:85) as the mobile phase at flow rate of 1 mL min–1 through a C8 column (15 cm × 4.6 mm, 5 μm) and column temperature was 30 °C. The UV detection of the sitagliptin was then carried out at 205 nm (United States Pharmacopeia and the National Formulary, 2020). Metformin hydrochloride was also determined, according to the USP (34), 200 μg mL–1 metformin hydrochloride was prepared by dissolving 40 mg metformin hydrochloride in acetonitrile: dilute phosphoric acid (5:95) in a 200 mL volumetric flask as standard stock solution. A test sample was prepared by placing 10 tablets containing 5,000 mg of metformin hydrochloride to 500 mL volumetric flask; 500 mL of acetonitrile: dilute phosphoric acid (5:95) as solvent was added and the solution was shaken for 1 h then a portion of the solution was centrifuged for 10 min; 2 mL of the supernatant solution was transferred into 100 mL volumetric flask and diluted with solvent. The standard and the test sample of metformin hydrochloride were injected through an HPLC system with a mixture of acetonitrile: monobasic potassium phosphate buffer (pH adjusted to 2 with phosphoric acid) (15:85) as the mobile phase at flow rate of 1 mL min–1 through a C8 column (15 cm × 4.6 mm, 5 μm) and column temperature was 30 °C. The UV detection of the sitagliptin was then carried out at 205 nm. 3. Results and discussion 3.1 Method development for sitagliptin and metformin HCl determination 3.1.1 Selection of solvent In order to choose a suitable solvent, solubility was checked in water, methanol, 0.1 mol L–1 NaOH and 0.1 mol L–1 HCl. The drug was found to be soluble in methanol, water, 0.1 mol L–1 NaOH and 0.1 mol L–1 HCl. Therefore, water was selected as diluent that has striking advantages such as easily available, easy to handle, a cheap and environmentally friendly for implementing the spectrophotometric method and Fig. 2 showed the spectra of the sitagliptin and metformin hydrochloride in water. 3.1.2 Selection of spectral zones for analysis To determine the overlap spectral zones, the absorbance spectra of the pure sitagliptin and metformin hydrochloride samples, and that sample of the mixed sitagliptin with metformin hydrochloride in water were recorded in the range of 200-400 nm with 0.2 nm interval. For the analysis, the UV spectra of the mixtures were selected for a suitable wavelength range (200–270 nm) against water blank. This range provided a great amount of information about the two components as shown in the sitagliptin with metformin hydrochloride spectra (Fig. 2). https://revista.iq.unesp.br/index.php/ecletica https://doi.org/10.26850/1678-4618eqj.v48.1.2023.p72-94 Original Article revista.iq.unesp.br 77 Eclética Química, vol. 48, n. 1, 2023, 72-94 ISSN: 1678-4618 DOI: 10.26850/1678-4618eqj.v48.1.2023.p72-94 Figure 2. UV absorbance spectra of the pure and mixed samples of sitagliptin and metformin hydrochloride in water solvent. 3.1.3 Construction of the training set To determine the linear, range from measuring the absorbance at different concentrations for sitagliptin with metformin hydrochloride, the response was found to be linear in the range of 13.36–26.72 μg mL–1 for sitagliptin and 8–16 μg mL–1 for metformin hydrochloride using 25 different concentrations of sitagliptin and metformin hydrochloride mixtures were prepared to construct the models as shown in Table 1. Table 1. Composition of calibration set. Mixture No. Sitagliptin (μg mL–1) Metformin hydrochloride (μg mL–1) Mixture No. Sitagliptin (μg mL–1) Metformin hydrochloride (μg mL–1) 1 13.36 8 14 20.04 14 2 13.36 10 15 20.04 16 3 13.36 12 16 23.38 8 4 13.36 14 17 23.38 10 5 13.36 16 18 23.38 12 6 16.7 8 19 23.38 14 7 16.7 10 20 23.38 16 8 16.7 12 21 26.72 8 9 16.7 14 22 26.72 10 10 16.7 16 23 26.72 12 11 20.04 8 24 26.72 14 12 20.04 10 25 26.72 16 13 20.04 12 3.1.4 Construction of chemometrics models The spectra were saved and extracted into Microsoft Excel for model generation. The PCR and PLS models were developed utilizing the absorption data for the selected spectral zones using Minitab 17 software program. After the PCR and PLS models have been constructed, the optimum number of principal components of sitagliptin and metformin hydrochloride were obtained and given in Tables A1–4 of the Appendix. 3.1.5 Determination of the optimum number of the principal components of sitagliptin and metformin hydrochloride for PLS Selecting the proper number of principal components for the development of model was necessary to obtain good prediction. Leave-one-out cross validation method was used to obtain the necessary optimum number of the principal factors for the PLS model. It was found that the optimum number of the principal components were eight for sitagliptin and eight for metformin hydrochloride as mentioned above and as given in Table A1 and A2 of the Appendix. 3.1.6 Determination of the constant and coefficients obtained at each wavelength of sitagliptin and metformin hydrochloride for PLS models The constant and coefficients at each wavelength were calculated using Minitab 17 program as illustrated in Table A1 of the Appendix. 3.1.7 Determination of the predicted concentrations and the recovery of sitagliptin and metformin hydrochloride for PLS models The predicted or calculated concentrations in μg mL–1 of the sitagliptin and metformin hydrochloride were worked out from the multiple regression Eq. 2: 0 1 2 3 4 5 6 200 220 240 260 280 300 320 340 360 380 A b so rb an ce Wavelength (λ) sita+ metf https://revista.iq.unesp.br/index.php/ecletica https://doi.org/10.26850/1678-4618eqj.v48.1.2023.p72-94 Original Article revista.iq.unesp.br 78 Eclética Química, vol. 48, n. 1, 2023, 72-94 ISSN: 1678-4618 DOI: 10.26850/1678-4618eqj.v48.1.2023.p72-94 predicted (Calculated) = Constant + ∑ (Coefficient × Absorbance) (2) The predicted or calculated concentrations of the components were compared with the actual concentrations and the assay of binary mixture were calculated. RMSECV was calculated and found to be low. The low values of RMSECV in Table 2 indicate both the precision and accuracy of PLS model for sitagliptin and metformin hydrochloride were very high and the R2 values in Fig. 3 were also of very high linearity. Table 2. Results of the predicted concentrations with the recovery of sitagliptin and metformin hydrochloride in the binary mixture in each sample for PLS model. Name Sitagliptin Metformin hydrochloride Constant –1.1712 0.4045 Mixture No. Actual Conc. Predicted Conc. %Recovery Actual Conc. Predicted Conc. %Recovery 1 13.36 13.36 100.00 8.00 7.99 99.88 2 13.36 13.35 99.93 10.00 10.02 100.20 3 13.36 13.36 100.00 12.00 11.98 99.83 4 13.36 13.34 99.85 14.00 14.00 100.00 5 13.36 13.37 100.07 16.00 15.99 99.94 6 16.70 16.73 100.18 8.00 7.99 99.88 7 16.70 16.68 99.88 10.00 10.00 100.00 8 16.70 16.71 100.06 12.00 12.00 100.00 9 16.70 16.67 99.82 14.00 14.01 100.07 10 16.70 16.71 100.06 16.00 16.01 100.06 11 20.04 20.04 100.00 8.00 8.00 100.00 12 20.04 20.04 100.00 10.00 10.02 100.20 13 20.04 20.05 100.05 12.00 12.00 100.00 14 20.04 20.04 100.00 14.00 14.00 100.00 15 20.04 20.08 100.20 16.00 16.00 100.00 16 23.38 23.37 99.96 8.00 8.00 100.00 17 23.38 23.38 100.00 10.00 10.00 100.00 18 23.38 23.37 99.96 12.00 11.99 99.92 19 23.38 23.39 100.04 14.00 14.00 100.00 20 23.38 23.36 99.91 16.00 15.99 99.94 21 26.72 26.71 99.96 8.00 7.99 99.88 22 26.72 26.74 100.07 10.00 10.01 100.10 23 26.72 26.73 100.04 12.00 12.00 100.00 24 26.72 26.71 99.96 14.00 13.99 99.93 25 26.72 26.71 99.96 16.00 16.00 100.00 Mean% 100 Mean% 99.99 RSD% 0.09 RSD% 0.089 RMSECV 0.016 RMSECV 0.01 The linearity of the developed method was tested by constructing a cross-validation of the data in Table 2. The results obtained in Fig. 3 indicated that the developed method possessed high linearity with R2 = 1 within the method linear range (13.36–26.72 μg mL–1) for sitagliptin and R2 = 1 within the method linear range (8–16 μg mL–1) for metformin hydrochloride. The linearity of the developed method was very high and most importantly, environmentally friendly with respect to the solvent (water) used. In comparison, Adsul et al. (2018) revealed that the linearity of the HPLC methods which carried out in non-eco-friendly solvents and mobile phases was almost similar to our eco-friendly (water) developed method and better than another HPLC method (Kumar et al., 2017). https://revista.iq.unesp.br/index.php/ecletica https://doi.org/10.26850/1678-4618eqj.v48.1.2023.p72-94 Original Article revista.iq.unesp.br 79 Eclética Química, vol. 48, n. 1, 2023, 72-94 ISSN: 1678-4618 DOI: 10.26850/1678-4618eqj.v48.1.2023.p72-94 Figure 3. The PLS cross validation for the calibration set of the actual vs. predicted concentration. (a) Sitagliptin; (b) Metformin HCl. 3.1.8 Determination of the optimum number of the principal components and their coefficients of sitagliptin and metformin HCl for PCR The PCR was computed by using a few principal components and performed regression analysis of these components with concentration in order to determine the principal components coefficients of sitagliptin and metformin hydrochloride for PCR model as illustrated in Table A4 of the Appendix. From the treatment of the principal component’s coefficients in (Table A4 of the Appendix) using Minitab 17 program. Regression equations (Eqs. 3 and 4) of sitagliptin and metformin hydrochloride were obtained and used to calculate the predicted concentration as shown below. Regression equation of sitagliptin –2.182 + 0.5991 Z1 + 3.9880 Z2 + 3.65 Z3 – 0.92 Z4 + 1.75 Z5 – 5.97 Z6 (3) Regression equation of metformin hydrochloride 0.066 + 0.94302 Z1 – 0.9038 Z2 – 0.353 Z3 – 1.524 Z4 + 1.993 Z5 + 1.703 Z6 (4) where Z is the principal components coefficients. 3.1.9 Determination of the predicted concentrations and recovery of sitagliptin and metformin hydrochloride for PCR models The predicted or calculated concentrations in μg mL–1 of the sitagliptin and metformin hydrochloride were calculated from multiple regression Eq. 5: predicted (calculated) = constant + ∑ (coefficient × absorbance) (5) The predicted or calculated concentrations of the sitagliptin and metformin hydrochloride were compared with the actual concentrations and the assay for binary mixture were calculated in each sample. RMSECV was calculated and found to be low. The RMSECV low values in Table 3 indicate that both the precision and accuracy of PCR model for sitagliptin and metformin hydrochloride were very high, with the R2 values in Fig. 4 of very high linearity. y = 1.0001x - 0.0012 R² = 1 0 5 10 15 20 25 30 0 10 20 30 a) Predicte d Conc. (μg mL-1) Actual concentration (μg mL-1) y = 0.9999x + 0.0004 R² = 1 0 5 10 15 20 0 5 10 15 20 b) Predicte d Conc. (μg mL-1) Actual concentration (μg mL-1) https://revista.iq.unesp.br/index.php/ecletica https://doi.org/10.26850/1678-4618eqj.v48.1.2023.p72-94 Original Article revista.iq.unesp.br 80 Eclética Química, vol. 48, n. 1, 2023, 72-94 ISSN: 1678-4618 DOI: 10.26850/1678-4618eqj.v48.1.2023.p72-94 Table 3. Results of the predicted concentrations with recovery of sitagliptin and metformin hydrochloride in binary mixture in each sample for PCR models. Name Sitagliptin Metformin hydrochloride Constant -2.182 0.066 Mixture NO. Actual Conc. Predicted Conc. %Recovery Actual Conc. Predicted Conc. %Recovery 1 13.36 13.40 100.30 8.00 8.04 100.50 2 13.36 13.15 98.43 10.00 10.07 100.70 3 13.36 13.23 99.03 12.00 11.91 99.25 4 13.36 13.14 98.35 14.00 14.07 100.50 5 13.36 13.39 100.22 16.00 15.98 99.88 6 16.70 16.95 101.50 8.00 7.96 99.50 7 16.70 16.75 100.30 10.00 10.02 100.20 8 16.70 16.61 99.46 12.00 12.02 100.17 9 16.70 16.98 101.68 14.00 13.95 99.64 10 16.70 16.89 101.14 16.00 15.98 99.88 11 20.04 20.05 100.05 8.00 7.96 99.50 12 20.04 20.08 100.20 10.00 9.90 99.00 13 20.04 19.70 98.30 12.00 11.94 99.50 14 20.04 20.01 99.85 14.00 13.96 99.71 15 20.04 20.26 101.10 16.00 16.08 100.50 16 23.38 23.63 101.07 8.00 8.04 100.50 17 23.38 23.45 100.30 10.00 10.10 101.00 18 23.38 23.14 98.97 12.00 12.03 100.25 19 23.38 23.49 100.47 14.00 14.08 100.57 20 23.38 23.50 100.51 16.00 15.96 99.75 21 26.72 26.68 99.85 8.00 8.01 100.13 22 26.72 26.77 100.19 10.00 9.99 99.90 23 26.72 26.55 99.36 12.00 11.95 99.58 24 26.72 26.47 99.06 14.00 13.97 99.79 25 26.72 26.73 100.04 16.00 16.01 100.06 Mean% 99.99 Mean% 100.00 RSD% 0.94 RSD % 0.49 RMSECV 0.169 RMSECV 0.054 Figure 4. The PCR cross validation for calibration set of the actual vs. predicted concentration. (a) Sitagliptin; (b) Metformin HCl. y = 0.9987x + 0.0266 R² = 0.9987 0 5 10 15 20 25 30 0 10 20 30 a) Predicte d Conc. (μg mL-1) Actual concentration (μg mL-1) y = 0,9995x + 0,0052 R² = 0,9996 0 5 10 15 20 0 5 10 15 20 b) Predicted Conc. (μg.mL-1) Actual concentration (μg mL-1) https://revista.iq.unesp.br/index.php/ecletica https://doi.org/10.26850/1678-4618eqj.v48.1.2023.p72-94 Original Article revista.iq.unesp.br 81 Eclética Química, vol. 48, n. 1, 2023, 72-94 ISSN: 1678-4618 DOI: 10.26850/1678-4618eqj.v48.1.2023.p72-94 3.2 Validation method for sitagliptin and metformin hydrochloride 3.2.1 Construction of validation set The results of prediction and the percentage recoveries are represented in Table 4. The predictive abilities of the models were evaluated by plotting the actual known concentrations against the predicted concentrations that shown in Fig. 5 and 6. A tremendous agreement between the predicted (calculated) and actual concentration of sitagliptin and metformin hydrochloride for PLS and PCR models can be observed in Fig. 5 and 6. Table 4. Results of validation set of sitagliptin and metformin HCl for PLS and PCR model. NO. METHOD PLS PCR Sita. Metf. Sita. Metf. Sita. Metf. Actual (μg mL-1) Predicted (μg mL-1) %R Predicted (μg mL-1) %R Predicted (μg mL-1) %R Predicted (μg mL-1) %R 1 16.02 8 15.76 98.38 7.71 96.38 16.35 102.06 7.93 99.13 2 16.02 9.6 16.17 100.94 9.36 97.50 16.63 103.81 9.58 99.79 3 13.35 8 13.41 100.45 8.02 100.25 13.55 101.50 8.09 101.13 4 13.35 10 12.96 97.08 9.75 97.50 13.02 97.53 9.85 98.50 5 20.03 10 19.95 99.60 9.80 98.00 20.40 101.85 9.97 99.70 6 20.03 12 20.03 100.00 11.71 97.58 20.56 102.65 11.92 99.33 7 26.7 8 26.66 99.85 7.78 97.25 27.43 102.73 7.98 99.75 8 26.7 10 26.83 100.49 9.58 95.80 27.12 101.57 9.79 97.90 9 16 9.6 16.28 101.75 9.76 101.67 16.33 102.06 9.77 101.77 10 16 12 15.91 99.44 12.03 100.25 15.96 99.75 12.00 100.00 11 24 12 23.91 99.63 11.75 97.92 24.17 100.71 11.92 99.33 12 24 14.4 24.32 101.33 14.09 97.85 24.55 102.29 14.16 98.33 Mean% 99.91 98.16 Mean% 101.54 99.56 RSD% 1.22 1.74 RSD% 1.53 1.06 Figure 5. The PLS cross-validation for validation set of the actual vs. predicted concentration. (a) Sitagliptin; (b) Metformin HCl. y = 1.0342x - 0.7586 R² = 0.9987 0 5 10 15 20 25 30 0 10 20 30 a) Predicted Conc. (μg mL-1) Actual concentration (μg mL-1) y = 0.9996x - 0.3058 R² = 0.9989 0 2 4 6 8 10 12 14 16 0 5 10 15 20 b) Predicted Conc. (μg mL-1) Actual concentration (μg mL-1) https://revista.iq.unesp.br/index.php/ecletica https://doi.org/10.26850/1678-4618eqj.v48.1.2023.p72-94 Original Article revista.iq.unesp.br 82 Eclética Química, vol. 48, n. 1, 2023, 72-94 ISSN: 1678-4618 DOI: 10.26850/1678-4618eqj.v48.1.2023.p72-94 Figure 6. The PCR cross-validation for validation set of the actual vs. predicted concentration. (a) Sitagliptin; (b) Metformin HCl. 3.2.2 Precision (repeatability) The repeatability (intraday precision) of the developed method was carried out by determining the binary mixture at three different concentrations for sitagliptin and metformin hydrochloride in bulk using three different concentrations (i.e., 13.36/10, 20.04/12 and 26.72/16 μg mL–1 of sitagliptin/metformin hydrochloride, respectively) in triplicates sequentially. The results were reported as %RSD. The low values of %RSD were indicative of the high precision of the method. The %RSD values of the developed method were within the acceptable limit as suggested by the USP and the results are presented in Table 5. Table 5. Results of repeatability and intraday precision using the developed PLS and PCR models. Amount taken (actual conc.) (mg mL–1) Predicted conc. (mg mL–1) % Recovery Acceptable % RSD NMT 2% Sita. Metf. PLS PCR PLS PCR PLS PCR Sita. Metf. Sita. Metf. Sita. Metf. Sita. Metf. Sita. Metf. Sita. Metf. 13.36 10 13.31 9.56 13.54 9.76 99.63 95.60 101.35 97.60 0.62 0.58 1.17 0.27 13.36 10 13.33 9.60 13.43 9.80 99.78 96.00 100.52 98.00 13.36 10 13.18 9.67 13.23 9.81 98.65 96.70 99.03 98.10 20.04 12 19.90 11.55 20.19 11.70 99.30 96.25 100.75 97.50 0.60 0.38 0.33 0.13 20.04 12 20.08 11.62 20.31 11.73 100.20 96.83 101.35 97.75 20.04 12 20.13 11.63 20.30 11.72 100.45 96.92 101.30 97.67 26.72 16 26.22 15.51 25.91 15.65 98.13 96.94 96.97 97.81 1.03 0.40 1.15 0.16 26.72 16 26.65 15.63 26.44 15.70 99.74 97.69 98.95 98.13 26.72 16 26.15 15.54 25.93 15.67 97.87 97.13 97.04 97.94 % Recovery = (predicted conc. in μg mL–1 /Actual conc. in μg mL–1) ×100. 3.2.3 Accuracy Accuracy of the method was investigated using standard addition method for three different percentage levels (i.e., 80, 100, and 120%) by recovery experiments. Known amounts of standard solutions containing sitagliptin and metformin hydrochloride were added to sample solutions under investigation to make up solutions of 80, 100, and 120% levels in triplicates and scanned at the range 200–400 nm. The amount of the drugs recovered at each percentage level were determined by using the developed PCR and PLS models. The mean percentage recovery for each percentage level was showed low values of %RSD and the percentage recovery was within the acceptable limit (90–110%) as suggested by the USP. This indicates a high accuracy method at all the three levels and the accuracy data are given in Tables 6 and 7. y = 1.0077x + 0.3181 R² = 0.999 0 5 10 15 20 25 30 0 10 20 30 a) Predicte d Conc. (μg mL-1) Actual concentration (μg mL-1) y = 0.9871x + 0.0069 R² = 0.9988 0 2 4 6 8 10 12 14 16 0 5 10 15 20 b) Predicted Conc. (μg mL-1) Actual concentration (μg mL-1) https://revista.iq.unesp.br/index.php/ecletica https://doi.org/10.26850/1678-4618eqj.v48.1.2023.p72-94 Original Article revista.iq.unesp.br 83 Eclética Química, vol. 48, n. 1, 2023, 72-94 ISSN: 1678-4618 DOI: 10.26850/1678-4618eqj.v48.1.2023.p72-94 Table 6. Accuracy data of sitagliptin by PCR and PLS models. %Level Sample conc. (μg mL–1) Amount of standard sitagliptin (μg mL–1) Total conc. (μg mL–1) Predicted conc. (μg mL–1) %Recovery %RSD PLS PCR PLS PCR PLS PCR 80% 1 9.68 10.68 11.10 10.97 103.91 102.70 0.86 1.08 10.91 10.89 102.16 101.94 10.98 10.74 102.83 100.54 100% 1 12.10 13.10 13.37 13.36 102.09 101.99 0.87 0.96 13.28 13.32 101.37 101.69 13.14 13.12 100.34 100.18 120% 1 14.52 15.52 15.45 15.16 99.55 97.66 0.95 0.20 15.16 15.10 97.67 97.30 15.32 15.15 98.69 97.59 Table 7. Accuracy data of metformin hydrochloride by PCR and PLS models. %Level Sample conc. (μg mL–1) Amount of standard metformin HCl (μg mL–1) Total conc. (μg mL–1) Predicted Conc. (μg mL–1) %Recovery %RSD PLS PCR PLS PCR PLS PCR 80% 10 4 14 13.70 13.87 97.86 99.04 0.54 0.35 13.79 13.93 98.51 99.50 13.85 13.96 98.91 99.73 100% 10 5 15 15.27 15.17 101.79 101.14 0.49 0.23 15.37 15.22 102.47 101.48 15.42 15.24 102.77 101.59 120% 10 6 16 16.49 16.14 103.04 100.84 0.09 0.09 16.50 16.11 103.14 100.69 16.52 16.11 103.23 100.68 3.2.4 Specificity (Spiking Method) The specificity of the method was checked by adding a certain amount of sitagliptin and metformin hydrochloride standard into known amount of marketed sample solution as described in the Methodology section. Specificity data are shown in Tables 8 and 9. Table 8. Results of specificity for sitagliptin using the developed PCR and PLS models. Name of marketed sample Sample conc. (μg mL–1) Amount added (μg mL–1) Total conc. (μg mL–1) Predicted conc. (μg mL–1) %Recovery %RSD PLS PCR PLS PCR PLS PCR Jauntab 1 12.1 13.1 13.37 13.36 102.09 101.99 0.50 0.21 13.28 13.32 101.37 101.69 Jaunmet 1 12.1 13.1 13.14 13.12 100.34 100.18 0.20 0.33 13.18 13.06 100.63 99.71 Jauncare 1 12.1 13.1 13.13 12.93 100.22 98.69 0.23 0.70 13.17 13.06 100.54 99.67 Table 9. Results of specificity for metformin HCl using the developed PCR and PLS models. Name of marketed sample Sample conc. (μg mL–1) Amount added (μg mL–1) Total conc. (μg mL–1) Predicted conc. (μg mL–1) %Recovery %RSD PLS PCR PLS PCR PLS PCR Jauntab 10 5 15 15.27 15.17 101.79 101.14 0.47 0.24 15.37 15.22 102.47 101.48 Jaunmet 10 5 15 15.42 15.24 102.77 101.59 0.06 0.00 15.40 15.24 102.69 101.59 Jauncare 10 5 15 15.45 15.25 103.03 101.66 0.42 0.22 15.55 15.30 103.65 101.97 https://revista.iq.unesp.br/index.php/ecletica https://doi.org/10.26850/1678-4618eqj.v48.1.2023.p72-94 Original Article revista.iq.unesp.br 84 Eclética Química, vol. 48, n. 1, 2023, 72-94 ISSN: 1678-4618 DOI: 10.26850/1678-4618eqj.v48.1.2023.p72-94 As it can be appeared from these data, recovery for sitagliptin and metformin hydrochloride using the developed PCR and PLS models are within the acceptable limit (90–110%) This suggests that the methods are free from interference due to the excipients used in the commercial formulation. The above validation results indicate that method is simple, rapid, economical, precise and accurate beside being eco-friendly. Therefor it can be used for a routine analysis in quality control of mixtures and commercial products containing sitagliptin and metformin hydrochloride. 3.2.5 Analysis of marketed formulations The applicability of the developed methods for the quantification of sitagliptin and metformin hydrochloride in marketed formulations was carried out using the marketed formulation of 50 mg sitagliptin with 500 mg metformin hydrochloride concentration collected from the local pharmacies in the capital Sana’a. Tables 10 and 11 summarized the data obtained for the sitagliptin and metformin hydrochloride in the analyzed marketed formulations. Table 10. Assay result for sitagliptin and metformin hydrochloride in tablet (marketed sample) by PLS proposed method. Name of marketed sample METHOD PLS Sita. Metf. Sita. Metf. Measured conc. (μg mL–1) Obtained conc. (μg mL–1) %Recovery %RSD Obtained conc. (μg mL–1) %Recovery %RSD Jauntab 10.68 14 11.10 103.91 1.20 13.70 97.86 0.47 10.68 14 10.91 102.16 13.79 98.51 Jaunmet 10.68 14 10.98 102.83 0.50 13.85 98.91 0.24 10.68 14 10.91 102.11 13.90 99.25 Jauncare 10.68 14 11.15 104.40 0.26 13.79 98.52 0.44 10.68 14 11.19 104.79 13.88 99.14 Table 11. Assay result for sitagliptin and metformin hydrochloride in tablet (Marketed Sample) by PCR proposed method. Name of marketed sample METHOD PCR Sita. Metf. Sita. Metf. Measured conc. (μg mL–1) Obtained conc. (μg mL–1) %Recovery %RSD Obtained conc. (μg mL–1) %Recovery %RSD Jauntab 10.68 14 10.97 102.70 0.53 13.87 99.04 0.33 10.68 14 10.89 101.94 13.93 99.50 Jaunmet 10.68 14 10.74 100.54 0.64 13.96 99.73 0.06 10.68 14 10.64 99.63 13.98 99.82 Jauncare 10.68 14 11.17 104.55 0.43 13.75 98.21 0.27 10.68 14 11.10 103.91 13.80 98.59 As it can be seen from these data, the sitagliptin and metformin hydrochloride concentrations were within the acceptable limit (90–110%) according to the USP. 3.2.6 Comparing with reference method Comparison was carried out, with the aid of SPSS program using F-Test to assure non-significant difference between the recovery results of the newly developed methods and that of reference method for both the sitagliptin and metformin hydrochloride. Significance level indicated that null hypothesis was acceptable since the P-value was greater than significance level (Table 12). As for reference methods, sitagliptin and metformin hydrochloride were determined according to the USP as described in the Methodology section. https://revista.iq.unesp.br/index.php/ecletica https://doi.org/10.26850/1678-4618eqj.v48.1.2023.p72-94 Original Article revista.iq.unesp.br 85 Eclética Química, vol. 48, n. 1, 2023, 72-94 ISSN: 1678-4618 DOI: 10.26850/1678-4618eqj.v48.1.2023.p72-94 Table 12. Results of statistical comparison between newly developed method and reference method. Name of marketed sample Component Sitagliptin Metformin HCl Methods Reference method (HPLC) PLS PCR Reference method (HPLC) PLS PCR Jauntab 101.62 103.91 102.70 96.87 97.86 99.03 100.86 102.17 101.94 96.94 98.52 99.50 Mean% 101.24 103.04 102.32 96.91 96.91 98.19 RSD% 0.53 1.19 0.53 0.05 0.48 0.33 F-value 0.20 0.18 0.06 0.01 Jaunmet 98.11 102.82 100.54 97.08 98.91 99.73 98.32 102.00 99.64 98.60 99.25 99.82 Mean% 98.22 102.47 100.09 97.84 99.08 99.78 RSD% 0.15 0.49 0.64 1.10 0.24 0.06 F-value 0.01 0.06 0.25 0.13 F-value at p = 0.01. Also, the chromatograms in Fig. 7 have showed the results of the analysis for reference method for the determination of sitagliptin and metformin hydrochloride. https://revista.iq.unesp.br/index.php/ecletica https://doi.org/10.26850/1678-4618eqj.v48.1.2023.p72-94 Original Article revista.iq.unesp.br 86 Eclética Química, vol. 48, n. 1, 2023, 72-94 ISSN: 1678-4618 DOI: 10.26850/1678-4618eqj.v48.1.2023.p72-94 Figure 7. Chromatogram of sitagliptin and metformin HCl standard and commercial samples. (a) Standard sitagliptin; (b) Standard Metformin HCl; (c) Sitagliptin in Jauntab Sample (commercial); (d) Metformin HCl in Jauntab Sample (commercial); (e) Sitagliptin in Jaunmet Sample (commercial); (f) Metformin HCl in Jaunmet Sample (commercial) 4. Conclusions The proposed chemometrics models (PLS and PCR) has proven to determine simultaneously sitagliptin and metformin HCl in combined mixtures of pharmaceutical dosage forms without excipients interference or each other, and without prior physical separation of the two drugs. Multivariate calibration models were generated using matrices of spectral and concentration data. The validation of the two models and their application to a commercial pharmaceutical dosage form gave excellent results. As a result, the suggested techniques can be applied to regular quality control of the specified medications in their combination dosage form in standard laboratories. Authors’ contribution Conceptualization: Almaqtari, M. A. Data curation: Almaqtari, M. A.; Al-Odaini, N. A. Formal Analysis: Alarbagi, F. A. Funding acquisition: Not applicable. Investigation: Alarbagi, F. A.; Al-Maydama, H. Methodology: Alarbagi, F. A. Project administration: Almaqtari, M. A.; Al-Odaini, N. A. Resources: Not applicable. Software: Alarbagi, F. A. Supervision: Almaqtari, M. A.; Al-Odaini, N. A. Validation: Alarbagi, F. A. Visualization: Al-Odaini, N. A. Writing – original draft: Alarbagi, F. A. Writing – review & editing: Al-Maydama, H. Data availability statement Data will be available upon request. Funding Not applicable Acknowledgments The authors would like to thank the Chemistry Department-Factuality of Science, Sana’a University, Global Pharma and Shiba’a pharma Companies, Sana'a, Yemen for providing the laboratory facilities and the reference standards of the samples drugs as a gift. References Adsul, S.; Bidkar, J. S.; Harer, S.; Dama, G. Y. RP- HPLC method development and validation for simultaneous estimation for metformin and sitagliptin in bulk and tablet formulation. Int. J. Chem. Tech. Res. 2018, 11 (11), 428–435. https://doi.org/10.20902/IJCTR.2018.111149 Aminu, N.; Chan, S.-Y.; Khan, N. H.; Farhan, A. B.; Umar, M. N.; Toh, S.-M. A simple stability-indicating HPLC method for simultaneous analysis of paracetamol and caffeine and its application to determinations in fixed-dose combination tablet dosage form. Acta Chromatogr. 2019, 31 (2), 85–91. https://doi.org/10.1556/1326.2018.00354 Ashour, A.; Hegazy, M. A.; Abdel-Kawy, M.; ElZeiny, M. B. Simultaneous spectrophotometric determination of overlapping spectra of paracetamol and caffeine in laboratory prepared mixtures and pharmaceutical preparations using continuous wavelet and derivative transform. J. Saudi Chem. Soc. 2015, 19 (2), 186–192 https://doi.org/10.1016/j.jscs.2012.02.004 Attia, K. A.-S. M.; Abdel-Aziz, O.; Magdy, N.; Mohamed, G. F. Development and validation of different chemometric-assisted spectrophotometric methods for determination of cefoxitin-sodium in presence of its alkali-induced degradation product. Future J. Pharm. Sci. 2018, 4 (2), 241–247 https://doi.org/10.1016/j.fjps.2018.08.002 https://revista.iq.unesp.br/index.php/ecletica https://doi.org/10.26850/1678-4618eqj.v48.1.2023.p72-94 https://doi.org/10.20902/IJCTR.2018.111149 https://doi.org/10.20902/IJCTR.2018.111149 https://doi.org/10.20902/IJCTR.2018.111149 https://doi.org/10.20902/IJCTR.2018.111149 https://doi.org/10.20902/IJCTR.2018.111149 https://doi.org/10.20902/IJCTR.2018.111149 https://doi.org/10.1556/1326.2018.00354 https://doi.org/10.1556/1326.2018.00354 https://doi.org/10.1556/1326.2018.00354 https://doi.org/10.1556/1326.2018.00354 https://doi.org/10.1556/1326.2018.00354 https://doi.org/10.1556/1326.2018.00354 https://doi.org/10.1556/1326.2018.00354 https://doi.org/10.1016/j.jscs.2012.02.004 https://doi.org/10.1016/j.jscs.2012.02.004 https://doi.org/10.1016/j.jscs.2012.02.004 https://doi.org/10.1016/j.jscs.2012.02.004 https://doi.org/10.1016/j.jscs.2012.02.004 https://doi.org/10.1016/j.jscs.2012.02.004 https://doi.org/10.1016/j.jscs.2012.02.004 https://doi.org/10.1016/j.fjps.2018.08.002 https://doi.org/10.1016/j.fjps.2018.08.002 https://doi.org/10.1016/j.fjps.2018.08.002 https://doi.org/10.1016/j.fjps.2018.08.002 https://doi.org/10.1016/j.fjps.2018.08.002 https://doi.org/10.1016/j.fjps.2018.08.002 https://doi.org/10.1016/j.fjps.2018.08.002 Original Article revista.iq.unesp.br 87 Eclética Química, vol. 48, n. 1, 2023, 72-94 ISSN: 1678-4618 DOI: 10.26850/1678-4618eqj.v48.1.2023.p72-94 Belal, F.; Ibrahim, F.; Sheribah, Z.; Alaa, H. New spectrophotometric/chemometric assisted methods for the simultaneous determination of imatinib, gemifloxacin, nalbuphine and naproxen in pharmaceutical formulations and human urine. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2018, 198, 51–60. https://doi.org/10.1016/j.saa.2018.02.048 British Pharmacopoeia Commission. Medicines and Healthcare products Regulatory Agency (MHRA). British Pharmacopoeia Commission. 2020, 3 (6), 1844. Darbandi, A.; Sohrabi, M. R.; Bahmaei, M. Development of a chemometric-assisted spectrophotometric method for quantitative simultaneous determination of Amlodipine and Valsartan in commercial tablet. Optik. 2020, 218, 165110. https://doi.org/10.1016/j.ijleo.2020.165110 Elfatatry, H. M.; Mabrouk, M. M.; Hammad, S. F.; Mansour, F. R.; Kamal, A. H.; Alahmad, S. Development and validation of chemometric-assisted spectrophotometric methods for simultaneous determination of phenylephrine hydrochloride and ketorolac tromethamine in binary combinations. J. AOAC Inter. 2016, 99 (5), 1247–1251. https://doi.org/10.5740/jaoacint.16-0106 Gandhi, S. V.; Waghmare, A. D.; Nandwani, Y. S.; Mutha, A. S. Chemometrics - Assisted UV spectrophotometric method for determination of ciprofloxacin and ornidazole in pharmaceutical formulation. ARC Journal of Pharmaceutical Sciences. 2017, 3 (1), 19–25. https://doi.org/10.20431/2455- 1538.0301005 Gholse, Y. N.; Chaple, D. R.; Kasliwal, R. H. Development and validation of novel analytical simultaneous estimation based UV spectrophotometric method for doxycycline and levofloxacin determination. Biointerface Res. Appl. Chem. 2021, 12 (4), 5458–5478. https://doi.org/10.33263/BRIAC124.54585478 Glavanović, S.; Glavanović, M.; Tomišić, V. Simultaneous quantitative determination of paracetamol and tramadol in tablet formulation using UV spectrophotometry and chemometric methods. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2016, 157, 258–264. https://doi.org/10.1016/j.saa.2015.12.020 Himabindu, T.; Narmadha, S.; Sireesha, D.; Vasudha, B. Development and validation of spectrophotometric method for the simultaneous estimation of metformin hydrochloride and sitagliptinin tablet dosage form. World J. Pharm. Res. 2016, 5 (7), 1011–1018. Krishnan, B.; Mishra, K. Quality by design based development and validation of RP-HPLC method for simultaneous estimation of sitagliptin and metformin in bulk and pharmaceutical dosage forms. Int. J. Pharm. Investig. 2020, 10 (4), 512–518. https://doi.org/10.5530/ijpi.2020.4.89 Kumar, V. P.; Kavitha, M.; Patro, S.; Bhavya, C.; Bag, A. K. Development and validation of new analytical method for the simultaneous estimation of metformin and sitagliptin in bulk and dosage form by RP-HPLC. World J. Pharm. Res. 2017, 6 (3), 1691–1700. Lotfy, H. M.; Mohamed, D.; Mowaka, S. A comparative study of smart spectrophotometric methods for simultaneous determination of sitagliptinand metformin hydrochloride in their binary mixture. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2015, 149, 441–451. https://doi.org/10.1016/j.saa.2015.04.076 Manouchehri, F.; Izadmanesh, Y.; Aghaee, E.; Ghasemi, J. B. Experimental, computational and chemometrics studies of BSA-vitamin B6 interaction by UV–Vis, FT-IR, fluorescence spectroscopy, molecular dynamics simulation and hard-soft modeling methods. Bioorg. Chem. 2016, 68, 124–136. https://doi.org/10.1016/j.bioorg.2016.07.014 Mohammed, O. J.; Hamzah, M. J.; Saeed, A. M. RP– HPLC method validation for simultaneous estimation of paracetamol and caffeine in formulating pharmaceutical form. Res. J. Pharm. Technol. 2021, 14 (9), 4743–4748. https://doi.org/10.52711/0974-360X.2021.00825 Moroni, A. B.; Vega, D. R.; Kaufman, T. S.; Calvo, N. L. Form quantitation in desmotropic mixtures of albendazole bulk drug by chemometrics-assisted analysis of vibrational spectra. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2022, 265, 120354. https://doi.org/10.1016/j.saa.2021.120354 Moussa, B. A.; Mahrouse, M. A.; Fawzy, M. G. Smart spectrophotometric methods for the simultaneous determination of newly co-formulated hypoglycemic drugs in binary mixtures. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2021, 257, 119763 https://doi.org/10.1016/j.saa.2021.119763 Muntean, D. M.; Alecu, C.; Tomuta, I. Simultaneous quantification of paracetamol and caffeine in powder blends for tableting by NIR-chemometry. J. Spectroscopy. 2017, 2017, 7160675. https://doi.org/10.1155/2017/7160675 Muntean, D.; Porfire, A.; Alceu, C.; Iurian, S.; Casian, T.; Gavan, A.; Tomuta, I. A non-destructive NIR https://revista.iq.unesp.br/index.php/ecletica https://doi.org/10.26850/1678-4618eqj.v48.1.2023.p72-94 https://doi.org/10.1016/j.saa.2018.02.048 https://doi.org/10.1016/j.saa.2018.02.048 https://doi.org/10.1016/j.saa.2018.02.048 https://doi.org/10.1016/j.saa.2018.02.048 https://doi.org/10.1016/j.saa.2018.02.048 https://doi.org/10.1016/j.saa.2018.02.048 https://doi.org/10.1016/j.saa.2018.02.048 https://doi.org/10.1016/j.ijleo.2020.165110 https://doi.org/10.1016/j.ijleo.2020.165110 https://doi.org/10.1016/j.ijleo.2020.165110 https://doi.org/10.1016/j.ijleo.2020.165110 https://doi.org/10.1016/j.ijleo.2020.165110 https://doi.org/10.1016/j.ijleo.2020.165110 https://doi.org/10.5740/jaoacint.16-0106 https://doi.org/10.5740/jaoacint.16-0106 https://doi.org/10.5740/jaoacint.16-0106 https://doi.org/10.5740/jaoacint.16-0106 https://doi.org/10.5740/jaoacint.16-0106 https://doi.org/10.5740/jaoacint.16-0106 https://doi.org/10.5740/jaoacint.16-0106 https://doi.org/10.5740/jaoacint.16-0106 https://doi.org/10.20431/2455-1538.0301005 https://doi.org/10.20431/2455-1538.0301005 https://doi.org/10.20431/2455-1538.0301005 https://doi.org/10.20431/2455-1538.0301005 https://doi.org/10.20431/2455-1538.0301005 https://doi.org/10.20431/2455-1538.0301005 https://doi.org/10.20431/2455-1538.0301005 https://doi.org/10.33263/BRIAC124.54585478 https://doi.org/10.33263/BRIAC124.54585478 https://doi.org/10.33263/BRIAC124.54585478 https://doi.org/10.33263/BRIAC124.54585478 https://doi.org/10.33263/BRIAC124.54585478 https://doi.org/10.33263/BRIAC124.54585478 https://doi.org/10.1016/j.saa.2015.12.020 https://doi.org/10.1016/j.saa.2015.12.020 https://doi.org/10.1016/j.saa.2015.12.020 https://doi.org/10.1016/j.saa.2015.12.020 https://doi.org/10.1016/j.saa.2015.12.020 https://doi.org/10.1016/j.saa.2015.12.020 https://doi.org/10.5530/ijpi.2020.4.89 https://doi.org/10.5530/ijpi.2020.4.89 https://doi.org/10.5530/ijpi.2020.4.89 https://doi.org/10.5530/ijpi.2020.4.89 https://doi.org/10.5530/ijpi.2020.4.89 https://doi.org/10.5530/ijpi.2020.4.89 https://doi.org/10.1016/j.saa.2015.04.076 https://doi.org/10.1016/j.saa.2015.04.076 https://doi.org/10.1016/j.saa.2015.04.076 https://doi.org/10.1016/j.saa.2015.04.076 https://doi.org/10.1016/j.saa.2015.04.076 https://doi.org/10.1016/j.saa.2015.04.076 https://doi.org/10.1016/j.bioorg.2016.07.014 https://doi.org/10.1016/j.bioorg.2016.07.014 https://doi.org/10.1016/j.bioorg.2016.07.014 https://doi.org/10.1016/j.bioorg.2016.07.014 https://doi.org/10.1016/j.bioorg.2016.07.014 https://doi.org/10.1016/j.bioorg.2016.07.014 https://doi.org/10.1016/j.bioorg.2016.07.014 https://doi.org/10.52711/0974-360X.2021.00825 https://doi.org/10.52711/0974-360X.2021.00825 https://doi.org/10.52711/0974-360X.2021.00825 https://doi.org/10.52711/0974-360X.2021.00825 https://doi.org/10.52711/0974-360X.2021.00825 https://doi.org/10.1016/j.saa.2021.120354 https://doi.org/10.1016/j.saa.2021.120354 https://doi.org/10.1016/j.saa.2021.120354 https://doi.org/10.1016/j.saa.2021.120354 https://doi.org/10.1016/j.saa.2021.120354 https://doi.org/10.1016/j.saa.2021.120354 https://doi.org/10.1016/j.saa.2021.119763 https://doi.org/10.1016/j.saa.2021.119763 https://doi.org/10.1016/j.saa.2021.119763 https://doi.org/10.1016/j.saa.2021.119763 https://doi.org/10.1016/j.saa.2021.119763 https://doi.org/10.1016/j.saa.2021.119763 https://doi.org/10.1155/2017/7160675 https://doi.org/10.1155/2017/7160675 https://doi.org/10.1155/2017/7160675 https://doi.org/10.1155/2017/7160675 https://doi.org/10.1155/2017/7160675 https://doi.org/10.37897/RJPhP.2021.2.2 https://doi.org/10.37897/RJPhP.2021.2.2 Original Article revista.iq.unesp.br 88 Eclética Química, vol. 48, n. 1, 2023, 72-94 ISSN: 1678-4618 DOI: 10.26850/1678-4618eqj.v48.1.2023.p72-94 spectroscopic method combined with chemometry for simultaneous assay of paracetamol and caffeine in tablets. Ro. J. Pharm. Pract. 2021, 14 (2), 68-75. https://doi.org/10.37897/RJPhP.2021.2.2 Ortega-Barrales, P.; Padilla-Weigand, R.; Molina-Díaz, A. Simultaneous determination of paracetamol and caffeine by flow injection–solid phase spectrometry using C18 silica gel as a sensing support. Anal. Sci. 2002, 18 (11), 1241–1246. https://doi.org/10.2116/analsci.18.1241 Patel, K. R.; Prajapati, L. M.; Joshi, A. K.; Kharodiya, M. L.; Patel, J. R. Application of chemometrics in simultaneous spectrophotometric quantification of etophylline and theophylline: The drugs with same chromophore. Iranian Journal of Pharmaceutical Sciences 2013a, 9 (3), 17–28. Patel, M. N.; Alvi, S. N.; Savalia, M. D.; Kathiria, P. B.; Patel, B. A.; Parmar, S. J. Development and validation of first order derivative spectrophotometric method for simultaneous estimation of paracetamol and caffeine in tablet dosage form. Inventi Rapid: Pharm Analysis & Quality Assurance. 2013b, 2013 (2), 1–5. Patel, R.; Mashru, R. Development and validation of chemometric assisted methods and stability indicating RP-HPLC method for simultaneous estimation of rasagiline mesylate and pramipexole in synthetic mixture. Acta Scientific Pharmaceutical Sciences. 2019, 3 (8), 154–168. https://doi.org/10.31080/ASPS.2019.03.0359 Phechkrajang, C. M.; Siriratawan, W.; Narapanich, K.; Thanomchat, K.; Kantanawat, P.; Srikajhondei, W.; Khajornvanitchot, V.; Sakchaisri, K. Development and validation of chemometrics-assisted spectrophotometric method for determination of clotrimazole in the presence of betamethasone valerate. Mahidol University J. Pharm. Sci. 2015, 42 (2), 1–7. Putri, D. C. A.; Gani, M. R.; Octa, F. D. Chemometrics- assisted UV spectrophotometric method for simultaneous determination of paracetamol and tramadol in divided powder dosage form. Int. J. Pharm. Res. 2021, 13 (1), 1901–1907. https://doi.org/10.31838/ijpr/2021.13.01.075 Rahman, A.; Sravani, G. J.; Srividya, K.; Priyadharshni, A. D. R.; Narmada, A.; Sahithi, K.; Sai, T. K.; Padmavathi, Y. Development and validation of chemometric assisted FTIR spectroscopic method for simultaneous estimation of valsartan and hydrochlorothiazide in pure and pharmaceutical dosage forms. J. Young Pharm. 2020, 12 (2s), s51-s55. https://doi.org/10.5530/jyp.2020.12s.46 Salem, Y. A.; Hammouda, M. E. A.; El-Enin, M. A. A.; El-Ashry, S. M. Application of derivative emission fluorescence spectroscopy for determination of ibuprofen and phenylephrine simultaneously in tablets and biological fluids. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2019, 210, 387–397. https://doi.org/10.1016/j.saa.2018.11.054 Sebaiy, M. M.; El-Adl, S. M.; Mattar, A. A. Different techniques for overlapped UV spectra resolution of some co-administered drugs with paracetamol in their combined pharmaceutical dosage forms. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2020, 224, 117429. https://doi.org/10.1016/j.saa.2019.117429 Sebaiy, M.; Mattar, A. A.; El-Adl, S. M. UV- chemometric method development for resolving the overlapped spectra of aspirin, caffeine and orphenadrine citrate in their ternary pharmaceutical dosage form. Research Square. Preprint; 2022. https://doi.org/10.21203/rs.3.rs-1262160/v1 Shah, U. H.; Jasani, A. H. Chemometric assisted spectrophotometric methods for simultaneous determination of paracetamol and tolperisone hydrochloride in pharmaceutical dosage form. Eurasian J. Anal. Chem. 2017, 12 (3), 211–222. Shinde, M. A.; Divya, O. Simultaneous quantitative analysis of a three-drug combination using synchronous fluorescence spectroscopy and chemometrics. Current Science. 2015, 108 (7), 1348–1354. Silva, W. C.; Pereira, P. F.; Marra, M. C.; Gimenes, D. T.; Cunha, R. R.; Silva, R. A.; Munoz, R. A.; Richter, E. M. A simple strategy for simultaneous determination of paracetamol and caffeine using flow injection analysis with multiple pulse amperometric detection. Electroanalysis. 2011, 23 (12), 2764–2770. https://doi.org/10.1002/elan.201100512 Singh, V. D.; Singh, V. K. Chemo-metric assisted UV- spectrophotometric methods for simultaneous estimation of Darunavir ethanolate and Cobicistat in binary mixture and their tablet formulation. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2021, 250, 119383. https://doi.org/10.1016/j.saa.2020.119383 Sun, X.; Li, H.; Yi, Y.; Hua, H.; Guan, Y.; Chen, C. Rapid detection and quantification of adulteration in Chinese hawthorn fruits powder by near-infrared spectroscopy combined with chemometrics. https://revista.iq.unesp.br/index.php/ecletica https://doi.org/10.26850/1678-4618eqj.v48.1.2023.p72-94 https://doi.org/10.37897/RJPhP.2021.2.2 https://doi.org/10.37897/RJPhP.2021.2.2 https://doi.org/10.37897/RJPhP.2021.2.2 https://doi.org/10.37897/RJPhP.2021.2.2 https://doi.org/10.2116/analsci.18.1241 https://doi.org/10.2116/analsci.18.1241 https://doi.org/10.2116/analsci.18.1241 https://doi.org/10.2116/analsci.18.1241 https://doi.org/10.2116/analsci.18.1241 https://doi.org/10.2116/analsci.18.1241 https://doi.org/10.31080/ASPS.2019.03.0359 https://doi.org/10.31080/ASPS.2019.03.0359 https://doi.org/10.31080/ASPS.2019.03.0359 https://doi.org/10.31080/ASPS.2019.03.0359 https://doi.org/10.31080/ASPS.2019.03.0359 https://doi.org/10.31080/ASPS.2019.03.0359 https://doi.org/10.31080/ASPS.2019.03.0359 https://doi.org/10.31838/ijpr/2021.13.01.075 https://doi.org/10.31838/ijpr/2021.13.01.075 https://doi.org/10.31838/ijpr/2021.13.01.075 https://doi.org/10.31838/ijpr/2021.13.01.075 https://doi.org/10.31838/ijpr/2021.13.01.075 https://doi.org/10.31838/ijpr/2021.13.01.075 https://doi.org/10.5530/jyp.2020.12s.46 https://doi.org/10.5530/jyp.2020.12s.46 https://doi.org/10.5530/jyp.2020.12s.46 https://doi.org/10.5530/jyp.2020.12s.46 https://doi.org/10.5530/jyp.2020.12s.46 https://doi.org/10.5530/jyp.2020.12s.46 https://doi.org/10.5530/jyp.2020.12s.46 https://doi.org/10.5530/jyp.2020.12s.46 https://doi.org/10.1016/j.saa.2018.11.054 https://doi.org/10.1016/j.saa.2018.11.054 https://doi.org/10.1016/j.saa.2018.11.054 https://doi.org/10.1016/j.saa.2018.11.054 https://doi.org/10.1016/j.saa.2018.11.054 https://doi.org/10.1016/j.saa.2018.11.054 https://doi.org/10.1016/j.saa.2018.11.054 https://doi.org/10.1016/j.saa.2019.117429 https://doi.org/10.1016/j.saa.2019.117429 https://doi.org/10.1016/j.saa.2019.117429 https://doi.org/10.1016/j.saa.2019.117429 https://doi.org/10.1016/j.saa.2019.117429 https://doi.org/10.1016/j.saa.2019.117429 https://doi.org/10.21203/rs.3.rs-1262160/v1 https://doi.org/10.21203/rs.3.rs-1262160/v1 https://doi.org/10.21203/rs.3.rs-1262160/v1 https://doi.org/10.21203/rs.3.rs-1262160/v1 https://doi.org/10.21203/rs.3.rs-1262160/v1 https://doi.org/10.21203/rs.3.rs-1262160/v1 https://doi.org/10.1002/elan.201100512 https://doi.org/10.1002/elan.201100512 https://doi.org/10.1002/elan.201100512 https://doi.org/10.1002/elan.201100512 https://doi.org/10.1002/elan.201100512 https://doi.org/10.1002/elan.201100512 https://doi.org/10.1002/elan.201100512 https://doi.org/10.1016/j.saa.2020.119383 https://doi.org/10.1016/j.saa.2020.119383 https://doi.org/10.1016/j.saa.2020.119383 https://doi.org/10.1016/j.saa.2020.119383 https://doi.org/10.1016/j.saa.2020.119383 https://doi.org/10.1016/j.saa.2020.119383 https://doi.org/10.1016/j.saa.2020.119346 https://doi.org/10.1016/j.saa.2020.119346 https://doi.org/10.1016/j.saa.2020.119346 https://doi.org/10.1016/j.saa.2020.119346 Original Article revista.iq.unesp.br 89 Eclética Química, vol. 48, n. 1, 2023, 72-94 ISSN: 1678-4618 DOI: 10.26850/1678-4618eqj.v48.1.2023.p72-94 Spectrochim. Acta A Mol. Biomol. Spectrosc. 2021, 250, 119346. https://doi.org/10.1016/j.saa.2020.119346 Swamy, G. K.; Surekha, M. L.; Krishna, M. M. Development and validation of RP-HPLC method for simultaneous estimation of metformin and sitagliptin in bulk and tablet dosage forms. Journal of Pharmaceutical and Medicinal Chemistry. 2020, 6 (1), 15–20. Tsvetkova, B.; Kostova, B.; Pencheva, I.; Zlatkov, A.; Rachev, D.; Peikov, P. Validated LC method for simultaneous analysis of paracetamol and caffeine in model tablet formulation. Int. J. Pharm. Pharm. Sci. 2012, 4 (Suppl. 4), 680–684. Uddin, M.; Mondol, A.; Karim, M.; Jahan, R.; Rana, A. Chemometrics assisted spectrophotometric method for simultaneous determination of paracetamol and caffeine in pharmaceutical formulations. Bangladesh J. Sci. Ind. Res. 2019, 54 (3), 215–222. https://doi.org/10.3329/bjsir.v54i3.42673 United States Pharmacopeia and the National Formulary (USP 43 - NF 38). The United States Pharmacopeial Convention; 2020. https://www.uspnf.com/notices/usp- nf-final-print-edition (accessed 2022-06-09). Vichare, V.; Mujgond, P.; Tambe, V.; Dhole, S. N. Simultaneous Spectrophotometric Determination of Paracetamol and Caffeine in Tablet Formulation. Int. J. PharmTech Res. 2010, 2 (4), 2512–2516. Vu Dang, H.; Thu, H. T. T.; Ha, L. D. T.; Mai, H. N. 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, 2020, 8107571. https://doi.org/10.1155/2020/8107571 Walash, M. I.; Belal, F. F.; El-Enany, N. M.; El- Maghrabey, M. H. Synchronous fluorescence spectrofluorimetric method for the simultaneous determination of metoprolol and felodipine in combined pharmaceutical preparation. Chem. Central J. 2011, 5, 70. https://doi.org/10.1186/1752-153X-5-70 Zhu, L.; Wu, H.-L.; Xie, L.-X.; Fang, H.; Xiang, S.-X.; Hu, Y.; Liu, Z.; Wang, T.; Yu, R.-Q. A chemometrics- assisted excitation–emission matrix fluorescence method for simultaneous determination of arbutin and hydroquinone in cosmetic products. Analytical Methods. 2016, 8 (24), 4941–4948. https://doi.org/10.1039/C6AY00821F https://revista.iq.unesp.br/index.php/ecletica https://doi.org/10.26850/1678-4618eqj.v48.1.2023.p72-94 https://doi.org/10.1016/j.saa.2020.119346 https://doi.org/10.1016/j.saa.2020.119346 https://doi.org/10.3329/bjsir.v54i3.42673 https://doi.org/10.3329/bjsir.v54i3.42673 https://doi.org/10.3329/bjsir.v54i3.42673 https://doi.org/10.3329/bjsir.v54i3.42673 https://doi.org/10.3329/bjsir.v54i3.42673 https://doi.org/10.3329/bjsir.v54i3.42673 https://www.uspnf.com/notices/usp-nf-final-print-edition https://www.uspnf.com/notices/usp-nf-final-print-edition https://www.uspnf.com/notices/usp-nf-final-print-edition https://www.uspnf.com/notices/usp-nf-final-print-edition https://doi.org/10.1155/2020/8107571 https://doi.org/10.1155/2020/8107571 https://doi.org/10.1155/2020/8107571 https://doi.org/10.1155/2020/8107571 https://doi.org/10.1155/2020/8107571 https://doi.org/10.1155/2020/8107571 https://doi.org/10.1155/2020/8107571 https://doi.org/10.1186/1752-153X-5-70 https://doi.org/10.1186/1752-153X-5-70 https://doi.org/10.1186/1752-153X-5-70 https://doi.org/10.1186/1752-153X-5-70 https://doi.org/10.1186/1752-153X-5-70 https://doi.org/10.1186/1752-153X-5-70 https://doi.org/10.1039/C6AY00821F https://doi.org/10.1039/C6AY00821F https://doi.org/10.1039/C6AY00821F https://doi.org/10.1039/C6AY00821F https://doi.org/10.1039/C6AY00821F https://doi.org/10.1039/C6AY00821F https://doi.org/10.1039/C6AY00821F Original Article revista.iq.unesp.br 90 Eclética Química, vol. 48, n. 1, 2023, 72-94 ISSN: 1678-4618 DOI: 10.26850/1678-4618eqj.v48.1.2023.p72-94 Appendix Table A1. Results of optimum number of principal factors of sitagliptin for PLS models. Method Components to evaluate Number of components evaluated Number of components selected Cross-validation (Leave-one-out) Set 10 8 Model selection and validation for sitagliptin Components (pred.) X Variance Error R-sq Press R-sq (Pred) 1 0.577399 139.559 0.74980 188.677 0.661736 2 0.999466 1.892 0.99661 2.565 0.995402 3 0.999858 1.036 0.99814 1.568 0.997189 4 0.999888 0.534 0.99904 1.577 0.997172 5 0.999929 0.370 0.99934 1.304 0.997663 6 0.999937 0.116 0.99979 1.390 0.997508 7 0.999946 0.048 0.99991 1.404 0.997482 8 0.999950 0.006 0.99999 1.291 0.997686 9 0.002 1.00000 1.332 0.997612 10 0.001 1.00000 1.335 0.997607 Table A2. Results of optimum number of principal factors of metformin hydrochloride for PLS models. Method Components to evaluate Number of components evaluated Number of components selected Cross-validation (Leave-one-out) Set 10 8 Model selection and validation for metformin hydrochloride Components (pred.) X Variance Error R-sq Press R-sq (Pred) 1 0.743512 14.9504 0.92525 18.7639 0.906180 2 0.999466 0.1424 0.99929 0.1877 0.999062 3 0.999855 0.1157 0.99942 0.1763 0.999119 4 0.999901 0.0742 0.99963 0.1759 0.999120 5 0.999927 0.0394 0.99980 0.1527 0.999237 6 0.999937 0.0143 0.99993 0.1613 0.999194 7 0.999946 0.0059 0.99997 0.1565 0.999217 8 0.999952 0.0023 0.99999 0.1469 0.999265 9 0.0006 1.00000 0.1502 0.999249 10 0.0002 1.00000 0.1523 0.999239 Table A3. The constant and coefficients at each wavelength of sitagliptin and metformin hydrochloride for PLS models. Sitagliptin Metformin hydrochloride Constant –1.1712 Constant 0.4045 Wavelength (nm) Coefficients Wavelength (nm) Coefficients Wavelength (nm) Coefficients Wavelength (nm) Coefficients 270 –8.554 234.8 0.6532 270 –5.2653 234.8 0.0527 269.8 36.5022 234.6 0.2441 269.8 –3.4147 234.6 0.0577 269.6 –31.8693 234.4 1.2605 269.6 –10.9734 234.4 0.0604 269.4 –25.5432 234.2 0.49 269.4 10.849 234.2 –0.0832 269.2 –65.3233 234 0.1544 269.2 –1.7155 234 0.1432 269 –23.0727 233.8 –0.2522 269 9.9911 233.8 –0.0113 268.8 –11.3679 233.6 –0.5453 268.8 –13.4665 233.6 0.2214 268.6 –5.5928 233.4 –0.344 268.6 4.7408 233.4 0.2054 268.4 –4.1696 233.2 0.8311 268.4 1.394 233.2 0.1359 268.2 –14.1503 233 0.1554 268.2 –4.088 233 0.0633 268 –19.1888 232.8 –0.059 268 –11.7374 232.8 0.1427 267.8 –40.9486 232.6 –0.7372 267.8 –7.8427 232.6 0.0949 267.6 –22.6885 232.4 –0.6594 267.6 –10.4613 232.4 0.1963 https://revista.iq.unesp.br/index.php/ecletica https://doi.org/10.26850/1678-4618eqj.v48.1.2023.p72-94 Original Article revista.iq.unesp.br 91 Eclética Química, vol. 48, n. 1, 2023, 72-94 ISSN: 1678-4618 DOI: 10.26850/1678-4618eqj.v48.1.2023.p72-94 267.4 5.516 232.2 0.6939 267.4 2.0351 232.2 –0.0132 267.2 –50.7754 232 0.6551 267.2 13.406 232 0.0935 267 –9.302 231.8 –0.995 267 1.6967 231.8 0.1395 266.8 –10.1594 231.6 –0.2627 266.8 –2.8888 231.6 0.1843 266.6 18.2208 231.4 –0.5025 266.6 –13.25 231.4 0.1687 266.4 6.7485 231.2 0.0497 266.4 2.8032 231.2 0.1305 266.2 23.5144 231 –0.7094 266.2 0.6477 231 0.1136 266 –9.3416 230.8 0.205 266 –8.4888 230.8 0.0976 265.8 21.6262 230.6 0.152 265.8 2.8843 230.6 –0.2017 265.6 15.7812 230.4 –0.069 265.6 –0.3461 230.4 –0.1925 265.4 36.272 230.2 –0.5146 265.4 4.9661 230.2 0.1252 265.2 23.198 230 –0.9981 265.2 –10.2205 230 0.1216 265 22.0233 229.8 –0.7537 265 –8.7358 229.8 0.0951 264.8 4.3131 229.6 0.0623 264.8 3.7949 229.6 0.4342 264.6 –2.4513 229.4 0.3702 264.6 –1.1286 229.4 –0.077 264.4 16.4876 229.2 –0.4447 264.4 –2.8178 229.2 0.4234 264.2 4.741 229 0.6679 264.2 3.2968 229 0.0984 264 13.294 228.8 0.2232 264 0.8386 228.8 0.3764 263.8 2.2629 228.6 –0.7047 263.8 –1.3713 228.6 0.2014 263.6 –9.7468 228.4 –0.2404 263.6 0.98 228.4 –0.0309 263.4 –13.6458 228.2 –0.671 263.4 8.5777 228.2 0.1123 263.2 13.8386 228 –0.7128 263.2 –4.6343 228 0.1859 263 –1.2977 227.8 –0.4311 263 –4.7391 227.8 0.2667 262.8 –4.2576 227.6 –0.6824 262.8 0.7491 227.6 0.378 262.6 8.4991 227.4 –0.1933 262.6 –6.8955 227.4 0.3419 262.4 10.8477 227.2 –1.2674 262.4 –4.3476 227.2 –0.0296 262.2 4.3527 227 –0.1852 262.2 –7.5522 227 0.1174 262 27.7793 226.8 –0.723 262 2.1918 226.8 0.182 261.8 –16.3076 226.6 –1.5414 261.8 0.0193 226.6 0.2658 261.6 22.6114 226.4 –0.1781 261.6 2.6963 226.4 0.4972 261.4 –4.7696 226.2 –2.6872 261.4 –1.5696 226.2 –0.2154 261.2 –3.511 226 –1.0534 261.2 4.3536 226 0.0702 261 7.9681 225.8 –0.165 261 –3.1001 225.8 0.6035 260.8 41.8407 225.6 –0.2283 260.8 –1.8041 225.6 0.4791 260.6 33.643 225.4 –1.6467 260.6 3.999 225.4 0.5571 260.4 13.2769 225.2 –1.2369 260.4 3.9561 225.2 0.2447 260.2 10.0929 225 –0.5339 260.2 3.1868 225 0.3378 260 9.8518 224.8 –1.3898 260 –2.1213 224.8 0.594 259.8 43.043 224.6 –0.8225 259.8 –1.7967 224.6 0.1454 259.6 5.1607 224.4 –0.6431 259.6 –9.4247 224.4 0.1927 259.4 –34.1511 224.2 –1.1628 259.4 –17.384 224.2 0.3319 259.2 11.5158 224 –1.2444 259.2 –6.5233 224 0.305 259 –14.383 223.8 –0.8308 259 7.4788 223.8 0.4529 258.8 14.5878 223.6 –0.4681 258.8 15.1635 223.6 0.4367 258.6 4.5959 223.4 –0.0295 258.6 3.1614 223.4 0.5613 258.4 –18.8881 223.2 –1.6294 258.4 2.0012 223.2 0.1711 258.2 7.6601 223 –0.7336 258.2 11.4035 223 0.5318 258 4.6961 222.8 –1.8822 258 –5.1932 222.8 0.109 257.8 27.7054 222.6 –1.1367 257.8 10.9597 222.6 0.3162 257.6 0.988 222.4 –1.1225 257.6 10.405 222.4 0.3399 257.4 –12.5001 222.2 –0.8589 257.4 8.7258 222.2 0.3429 257.2 –15.2382 222 –1.6734 257.2 –0.8162 222 0.6074 257 –3.0942 221.8 –0.679 257 1.0974 221.8 0.579 256.8 –4.4602 221.6 0.1903 256.8 –1.5909 221.6 0.1961 256.6 1.7788 221.4 0.089 256.6 –5.2308 221.4 0.23 256.4 27.5921 221.2 –1.1892 256.4 9.6734 221.2 –0.0033 256.2 –18.0223 221 –1.5961 256.2 –2.957 221 0.2731 256 33.2932 220.8 –0.1513 256 8.4664 220.8 0.5852 255.8 –2.1517 220.6 –0.158 255.8 0.3689 220.6 0.3382 255.6 –13.3572 220.4 –0.3497 255.6 7.3895 220.4 0.8263 255.4 –18.1868 220.2 –0.3168 255.4 4.4221 220.2 0.6111 https://revista.iq.unesp.br/index.php/ecletica https://doi.org/10.26850/1678-4618eqj.v48.1.2023.p72-94 Original Article revista.iq.unesp.br 92 Eclética Química, vol. 48, n. 1, 2023, 72-94 ISSN: 1678-4618 DOI: 10.26850/1678-4618eqj.v48.1.2023.p72-94 255.2 26.0096 220 –0.8101 255.2 7.9959 220 0.2208 255 9.8431 219.8 0.0445 255 9.3932 219.8 0.2442 254.8 –27.3872 219.6 –0.9638 254.8 –1.395 219.6 0.1875 254.6 20.7778 219.4 –1.6612 254.6 –2.7202 219.4 0.1146 254.4 –1.2528 219.2 –1.7879 254.4 –4.9913 219.2 0.2444 254.2 –13.5772 219 –1.2254 254.2 –1.6811 219 0.1439 254 –23.6382 218.8 0.3546 254 –4.9847 218.8 0.4906 253.8 –13.4889 218.6 –1.0438 253.8 8.1626 218.6 0.1345 253.6 2.8508 218.4 –0.5056 253.6 1.9851 218.4 0.1787 253.4 23.3554 218.2 –0.4821 253.4 0.8798 218.2 0.131 253.2 –11.656 218 0.9343 253.2 –4.1621 218 0.3666 253 –10.1518 217.8 –0.8597 253 4.5813 217.8 0.6368 252.8 –7.8638 217.6 –1.1187 252.8 –5.3028 217.6 –0.0903 252.6 1.4537 217.4 –0.5789 252.6 –0.1427 217.4 0.1755 252.4 –11.0952 217.2 –0.15 252.4 –1.3668 217.2 0.1429 252.2 –8.0002 217 0.7244 252.2 1.6172 217 0.097 252 2.9909 216.8 –1.5768 252 –4.355 216.8 –0.0646 251.8 13.7498 216.6 0.008 251.8 –4.7497 216.6 0.1884 251.6 –2.0345 216.4 –0.4139 251.6 1.0337 216.4 –0.067 251.4 –9.1847 216.2 –0.4189 251.4 –4.5599 216.2 0.0647 251.2 2.0637 216 –1.2419 251.2 –1.7805 216 0.2227 251 –7.3252 215.8 –0.2577 251 –1.8669 215.8 –0.1081 250.8 0.5645 215.6 1.3402 250.8 0.8755 215.6 0.058 250.6 –4.0354 215.4 0.3949 250.6 1.6955 215.4 0.2037 250.4 –1.5513 215.2 0.5238 250.4 –0.6681 215.2 –0.1017 250.2 6.2078 215 0.249 250.2 –2.0834 215 –0.1637 250 –5.6244 214.8 –0.3013 250 0.1999 214.8 –0.1404 249.8 –5.9114 214.6 1.2798 249.8 –1.3228 214.6 –0.1157 249.6 –3.5399 214.4 –1.4815 249.6 –1.4603 214.4 0.1955 249.4 –1.0941 214.2 1.7537 249.4 0.9512 214.2 0.4072 249.2 –3.1039 214 1.6824 249.2 0.4518 214 0.173 249 –3.6641 213.8 2.1526 249 –0.9982 213.8 0.2943 248.8 –0.5871 213.6 1.8704 248.8 0.8471 213.6 0.2654 248.6 –3.7437 213.4 0.581 248.6 –1.7456 213.4 –0.444 248.4 0.8308 213.2 –1.3783 248.4 0.4118 213.2 –0.1354 248.2 –0.8122 213 0.792 248.2 0.5814 213 –0.1364 248 0.7908 212.8 1.164 248 –0.9923 212.8 0.0905 247.8 –1.3825 212.6 2.2452 247.8 –0.057 212.6 0.131 247.6 2.7731 212.4 0.1635 247.6 –1.3365 212.4 0.0719 247.4 0.6642 212.2 0.4482 247.4 –0.5276 212.2 0.2397 247.2 0.4524 212 0.4644 247.2 0.6951 212 –0.7323 247 0.1823 211.8 0.3519 247 –0.8055 211.8 0.0588 246.8 –0.1467 211.6 2.3998 246.8 –0.0386 211.6 –0.4235 246.6 –2.5052 211.4 2.1745 246.6 –1.6217 211.4 0.1408 246.4 –0.5324 211.2 0.0758 246.4 –1.0455 211.2 –0.0643 246.2 0.4933 211 0.4929 246.2 –0.0042 211 0.3936 246 1.6089 210.8 1.2011 246 –0.5154 210.8 0.2329 245.8 3.8558 210.6 2.4589 245.8 0.5417 210.6 0.0627 245.6 0.8283 210.4 1.5865 245.6 –0.2551 210.4 –0.6584 245.4 0.6869 210.2 1.3387 245.4 –0.7264 210.2 –0.2923 245.2 0.8776 210 1.0135 245.2 –0.4832 210 0.284 245 0.1365 209.8 1.659 245 0.2298 209.8 0.068 244.8 2.305 209.6 –1.298 244.8 –0.3353 209.6 –0.0156 244.6 0.2397 209.4 –1.4035 244.6 –0.2579 209.4 –0.2137 244.4 0.652 209.2 1.3169 244.4 0.0854 209.2 –0.0601 244.2 –0.8754 209 3.0337 244.2 –0.3155 209 –0.1924 244 1.0819 208.8 0.6519 244 –0.2405 208.8 –0.1346 243.8 2.4943 208.6 2.6027 243.8 0.1475 208.6 0.4873 243.6 0.4038 208.4 –0.8664 243.6 –0.3628 208.4 0.6161 243.4 1.1649 208.2 –0.5923 243.4 –0.3957 208.2 –0.9718 243.2 0.9246 208 0.57 243.2 –0.3946 208 –0.324 https://revista.iq.unesp.br/index.php/ecletica https://doi.org/10.26850/1678-4618eqj.v48.1.2023.p72-94 Original Article revista.iq.unesp.br 93 Eclética Química, vol. 48, n. 1, 2023, 72-94 ISSN: 1678-4618 DOI: 10.26850/1678-4618eqj.v48.1.2023.p72-94 243 0.6146 207.8 2.1778 243 –0.2858 207.8 0.1352 242.8 0.0615 207.6 2.404 242.8 –0.4416 207.6 –0.0218 242.6 1.3358 207.4 0.0886 242.6 –0.1321 207.4 0.2638 242.4 2.3687 207.2 –0.2496 242.4 –0.2817 207.2 –0.4744 242.2 0.2692 207 1.8095 242.2 –0.2462 207 0.4651 242 0.515 206.8 2.6284 242 –0.0534 206.8 0.4709 241.8 1.2582 206.6 1.8385 241.8 –0.1296 206.6 –1.4419 241.6 0.5053 206.4 0.7265 241.6 0.1142 206.4 –0.7179 241.4 1.09 206.2 0.1278 241.4 –0.3483 206.2 1.3186 241.2 0.908 206 –2.309 241.2 –0.237 206 –1.4024 241 0.9199 205.8 1.0478 241 0.0217 205.8 0.7645 240.8 –1.2642 205.6 1.0052 240.8 –0.1368 205.6 –0.1055 240.6 0.7109 205.4 –2.4657 240.6 –0.0489 205.4 0.062 240.4 0.3987 205.2 –0.5511 240.4 0.0043 205.2 –0.0275 240.2 1.0164 205 1.2526 240.2 0.0713 205 –0.7603 240 0.5111 204.8 –0.4337 240 –0.0566 204.8 0.0593 239.8 0.5838 204.6 0.3184 239.8 –0.1276 204.6 1.1632 239.6 0.2768 204.4 –1.8168 239.6 –0.2804 204.4 0.4192 239.4 0.1573 204.2 4.7495 239.4 –0.2246 204.2 0.2375 239.2 0.2461 204 –3.6937 239.2 –0.1877 204 –0.5439 239 0.7206 203.8 –1.3017 239 –0.046 203.8 0.4817 238.8 0.7035 203.6 3.4068 238.8 0.237 203.6 –0.1949 238.6 0.7723 203.4 –1.7004 238.6 –0.1562 203.4 0.1481 238.4 1.1926 203.2 1.0425 238.4 0.1137 203.2 0.4436 238.2 0.4957 203 2.7366 238.2 0.1866 203 0.1096 238 0.1993 202.8 0.2214 238 0.0011 202.8 0.9499 237.8 –0.5919 202.6 –0.9995 237.8 –0.1311 202.6 0.005 237.6 –0.2045 202.4 –0.4386 237.6 –0.2809 202.4 –0.1008 237.4 0.2951 202.2 1.5183 237.4 0.0924 202.2 –1.0241 237.2 0.2763 202 3.9352 237.2 –0.0682 202 0.8222 237 0.3454 201.8 –3.9026 237 0.123 201.8 0.185 236.8 0.1578 201.6 –4.6102 236.8 –0.0583 201.6 0.2563 236.6 –0.8305 201.4 –3.1056 236.6 –0.1394 201.4 0.0675 236.4 0.1178 201.2 0.5686 236.4 0.0475 201.2 –0.9555 236.2 0.3159 201 –2.3895 236.2 0.0784 201 0.0037 236 0.6161 200.8 –0.3335 236 0.0782 200.8 –1.1175 235.8 0.9633 200.6 –1.1089 235.8 0.1337 200.6 –0.4999 235.6 0.2546 200.4 0.8255 235.6 –0.0865 200.4 1.5759 235.4 0.3716 200.2 0.2345 235.4 0.042 200.2 –0.4567 235.2 –0.1638 200 2.0276 235.2 0.173 200 1.3505 235 0.409 235 0.0591 Table A4. Results of the principal components coefficients of sitagliptin and metformin hydrochloride for PCR model. Mixture No. Sitagliptin (μg mL–1) Metformin hydrochloride (μg mL–1) Z1 Z2 Z3 Z4 Z5 Z6 1 13.36 8.00 10.84294 2.185204 –0.0434694 0.0576479 –0.0139843 –0.1021771 2 13.36 10.00 12.69862 1.845221 –0.0622337 0.0725021 –0.0115599 –0.1146071 3 13.36 12.00 14.43255 1.621218 –0.0746509 0.0707957 –0.0140199 –0.1112691 4 13.36 14.00 16.44175 1.265448 –0.0755674 0.0880404 –0.0115464 –0.1338847 5 13.36 16.00 18.27641 1.059148 –0.0853648 0.0881577 –0.0111338 –0.1365247 6 16.70 8.00 11.5361 2.966444 –0.0537313 0.0724661 –0.0113965 –0.1132784 7 16.70 10.00 13.4231 2.646652 –0.0779432 0.077781 –0.0093232 –0.1186342 8 16.70 12.00 15.26954 2.319471 –0.0541354 0.0951629 –0.0129627 –0.1177926 9 16.70 14.00 17.10382 2.172555 –0.0758528 0.0893094 0.0015372 –0.1023928 10 16.70 16.00 18.95773 1.866853 –0.0944023 0.0769914 0.0023791 –0.1136455 11 20.04 8.00 12.17808 3.663929 –0.0583895 0.0667501 –0.0093447 –0.102952 12 20.04 10.00 14.02957 3.384557 –0.0636098 0.0895632 –0.0125965 –0.1159239 13 20.04 12.00 15.83709 3.006622 –0.0476592 0.1091312 –0.000479 –0.1135108 14 20.04 14.00 17.75696 2.836101 –0.1076307 0.0624804 –0.013428 –0.119916 15 20.04 16.00 19.77072 2.589099 –0.0620704 0.0841685 –0.0075716 –0.0988977 https://revista.iq.unesp.br/index.php/ecletica https://doi.org/10.26850/1678-4618eqj.v48.1.2023.p72-94 Original Article revista.iq.unesp.br 94 Eclética Química, vol. 48, n. 1, 2023, 72-94 ISSN: 1678-4618 DOI: 10.26850/1678-4618eqj.v48.1.2023.p72-94 16 23.38 8.00 13.05221 4.431188 –0.0777075 0.0878319 –0.009271 –0.118186 17 23.38 10.00 14.89838 4.123749 –0.0764096 0.0853351 –0.0015347 –0.104407 18 23.38 12.00 16.63802 3.798361 –0.0655255 0.1114206 0.0043972 –0.0903928 19 23.38 14.00 18.63537 3.59354 –0.0955316 0.0659751 –0.0306461 –0.106344 20 23.38 16.00 20.41229 3.306786 –0.0370991 0.1190475 –0.0191721 –0.0912103 21 26.72 8.00 13.74611 5.092887 –0.0891428 0.105328 –0.0314232 –0.1326535 22 26.72 10.00 15.43337 4.782102 –0.072408 0.0838503 0.0301399 –0.1550265 23 26.72 12.00 17.35671 4.551464 –0.0895119 0.0670222 –0.0563766 –0.1125212 24 26.72 14.00 18.98121 4.28249 –0.0897928 0.0394045 0.0239509 –0.0873848 25 26.72 16.00 21.00168 3.901466 0.010285 0.0441581 –0.017422 –0.1344944 https://revista.iq.unesp.br/index.php/ecletica https://doi.org/10.26850/1678-4618eqj.v48.1.2023.p72-94