A near-infrared spectroscopy method to determine aminoglycosides in pharmaceutical formulations

Vibrational Spectroscopy 56 (2011) 184–192

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A near-infrared spectroscopy method to determine aminoglycosides in pharmaceutical formulations Mafalda Cruz Sarraguc¸a, Sandra Oliveira Soares, João Almeida Lopes ∗ REQUIMTE, Departamento de Química, Faculdade de Farmácia, Universidade do Porto, Rua Aníbal Cunha 164, 4090-030 Porto, Portugal

a r t i c l e

i n f o

Article history: Received 7 June 2010 Received in revised form 17 January 2011 Accepted 14 February 2011 Available online 18 February 2011 Keywords: Aminoglycosides Neomycin sulphate Near-infrared spectroscopy Partial least squares regression Pharmaceutical industry

a b s t r a c t The analytical determination of aminoglycosides in pharmaceutical formulations is very difficult due to the lack of chromophores or fluorophores. Several analytical methods have been developed along the years mainly based on derivatization reactions. The European Pharmacopeia (EP) and the United States Pharmacopeia (USP) describe a microbiological assay to the quantification of aminoglycosides. Near infrared spectroscopy (NIRS) can be used alternatively to analyse aminoglycosides without the need of derivatization reactions or other type of sample processing. A new NIRS based method was developed for the analysis of the aminoglycoside antibiotic neomycin. The method was developed with samples based on a commercial formulation containing neomycin sulphate and three excipients: lactose, talc and magnesium stearate. Synthetic and doped samples were manufactured for this purpose. Three lots of a commercial solid formulation were also used to assess the validity of the method to quantify neomycin sulphate in the industrial pharmaceutical product. The method proposes measurements in reflectance mode using a Fourier-transform near infrared (FT-NIR) spectrometer. Partial least squares regression was the multivariate method adopted to calibrate the NIR spectra with the neomycin sulphate mass fraction. The concentration of neomycin sulphate present in the commercial samples was confirmed by HPLC with pre-column derivatization with phenylisocyanate. Results show that neomycin sulphate was determined successfully in the commercial samples using the method calibrated with the doped samples (mass fraction error of 6.6%). Moreover, the synthetic samples were found to be unqualified to develop the method, producing a biased calibration. © 2011 Elsevier B.V. All rights reserved.

1. Introduction Aminoglycosides are a class of antibiotics with similar structure characterized by two or more aminosugars groups linked by glycoside bonds to an aminocyclitol component [1,2]. The structure similarity causes analogous chemical and pharmacological properties. This class of compounds is stable, water soluble, with a wide antimicrobial spectrum and powerful sterilization capacity, being absorbed and drained without difficulty [3]. The low price at which they are present in the market makes them very popular and widely used in human and veterinary applications to treat infections caused by Gram-negative and Gram-positive bacteria [4,5]. Neomycin is a broad spectrum aminoglycoside antibiotic produced by the growth of certain selected strains of Streptomyces frediae [6]. Neomycin in the sulphate form is a common aminoglycoside indicated for treatment of gastrointestinal infections [3,7,8]. Neomycin sulphate is mainly composed by neomycin B, the com-

∗ Corresponding author. Tel.: +351 222078994; fax: +351 222078961. E-mail address: [email protected] (J.A. Lopes). 0924-2031/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.vibspec.2011.02.007

ponent with the highest antibiotic activity and its stereoisomer neomycin C. The European Pharmacopeia (EP) limits the neomycin C content to 3.0–15% in neomycin sulphate formulations [6]. The United States Pharmacopeia (USP) do not establish any limit for neomycin C. Small amounts of minor constituents can be found in commercial samples, the most relevant is neomycin A or neamine [9]. The lack of chromophores or fluorophores makes the aminoglycosides analytical determination very difficult. The EP and USP state that the quantification of neomycin sulphate is performed using a microbiological assay. A high performance liquid chromatography (HPLC) method with pulse amperometric detection (PAD) assay is described by the EP for neomycin sulphate identification and related substances. The USP monograph describes a thin-layer chromatographic identification test [6,10]. Microbiological assays are semi-quantitative methods, labour intensive, with low reliability, precision and accuracy. Therefore, different alternative analytical methods have been developed along the years to quantify aminoglycosides in several matrices. The majority of them are based on HPLC analysis with pre- and post-column derivatization and UV/Vis or fluorescence detection [11–18]. HPLC with electrochemical [1,8,9,19–23] and evaporative

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light scattering [24,25] detections are also used since no derivatization reaction is needed. Methods using mass spectroscopy were also proposed [26–30]. Reviews on this subject, for the determination of aminoglycosides in general and neomycin sulphate in particular can also be found [2,4,5,31]. The preponderance of the referred analytical methods often requires specialized and expensive equipments, with laborious, long and sometimes difficult analytical procedures involving a considerable consumption of reagents. Near infrared spectroscopy (NIRS) is an analytical technique that offers several advantages over other instrumental methods. NIRS analyses are fast, cost effective and do not require considerable samples processing. Its use in the pharmaceutical field has been increasing along the years [32–34]. The particular specificities of aminoglycosides that makes them very difficult to analyse can be overcome with the use of NIRS, since these molecules are highly active in the near-infrared region. A drawback of the NIR methodology is that a different method or calibration is required for each pharmaceutical form. Because NIRS is a non-selective analytical method, excipients and other near-infrared active molecules will act as interferents. Therefore, different commercial products, even using the same active pharmaceutical ingredient (API) will require different calibrations. However, the development of these calibrations is straightforward provided that an efficient and robust strategy is followed [35]. The use of NIRS in the quantification of neomycin sulphate in pharmaceutical solid formulations is discussed in this work. This method was based on the determination of global neomycin content without discrimination of the different neomycin forms that are present in the formulation. In this way, the developed method was intended to specifically determine the neomycin sulphate content in a commercial pharmaceutical formulation (25 mg of neomycin sulphate in 150 mg tablets). The final goal was to demonstrate that the NIRS method was sensible to the content of aminoglycosides in general in pharmaceutical formulations and not produce a validation method for routine analysis. The authors acknowledge that the ICH guidelines [36] recommend the study of linearity, range, accuracy and precision, both short and long term and robustness, for analytical procedures which are to be used for pharmaceutical registration purposes. However, this was not in the scope of this work but can be done following a straightforward procedure by the methodology end-user. Two different approaches for developing the NIRS based method were followed. The first one, considered the utilization of synthetic samples (in powder form) for developing the calibration. This is the easier and most flexible method since it requires only laboratory facilities. The second method considered doped samples. In this method samples were based on the macerated tablets of the commercial formulation. The base powder was doped with neomycin sulphate to produce higher API content samples, and diluted with a placebo to produce lower API content samples. In both approaches (synthetic and doped) an experimental design was used. The samples spectra were correlated with the mass fraction of neomycin sulphate present in the samples using partial least squares regression (PLS). The concentration of neomycin sulphate present in the commercial samples was confirmed by HPLC with pre-column derivatization with phenylisocyanate.

2. Theory The multivariate technique used to relate the concentration of neomycin sulphate with the NIR spectra, was PLS with leaveone-out cross-validation [37]. This technique is commonly used in chemometrics analyses and is applied with the objective to establish a model for the analysis of unknown samples to determine physical or chemical properties [38,39]. To assess the PLS model

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accuracy (bias), the root mean square error of cross-validation (RMSECV), estimated according to Eq. (1) was used.



RMSECV =

t

(yC − yˆ C ) × (yC − yˆ C ) NC

(1)

In Eq. (1), yˆ C and yC are the PLS cross-validation estimate and the measured reference value, respectively. NC is the number of calibration samples. The model robustness was evaluated in terms of the root mean square error of prediction (RMSEP).



RMSEP =

t

(yP − yˆ P ) × (yP − yˆ P ) NP

(2)

In Eq. (2), yˆ P is the PLS prediction value of the ith sample, yP is the reference value for the same sample and NP is the number of prediction samples. Model performance was assessed using the range error ratio (RER). This ratio is calculated by dividing the amplitude of each parameter range by the RMSECV value. With a RER > 10 the model is considered acceptable for quality control purposes [40,41]. In a univariate analytical technique, uncertainty is assessed by the standard deviation of replicates. In multivariate techniques such as NIRS, the estimation of the model uncertainty is not straightforward. To overcome this difficulty, a statistical technique called bootstrapping can be used. This method generates an ensemble of samples by sampling with replacement from the original data set [42]. This technique is used to generate a large number of new data sets, each one with the same size of the original data set. These datasets yield an ensemble of estimations that can be used to obtain statistical parameters such as the standard deviation [43]. This technique was used in this work to estimate the relative standard deviation (RSD) of each developed model. The figures-of-merit limit of detection (LOD), sensitivity (SEN) and selectivity (SEL) were calculated for each modelled parameter using the net analyte signal (NAS) theory. Using the NAS theory the figures-of-merit of a multivariate method can be easily determined as in an univariate method [44]. Further details on the calculation of figures of merit based on the NAS theory can be found elsewhere [45]. All calculations were carried out using Matlab version 6.5 release 13 (MathWorks, Natick, MA) and the PLS Toolbox version 3.5 (Eigenvector Research, Manson, WA). 3. Experimental 3.1. Sample preparation The number of samples for calibration is always an issue when dealing with NIRS. Some authors proposed methods to determine the optimal number of calibration samples [34,46] but also referred that the optimal number is always dependent on the problem itself. The number of calibration samples should be large enough to produce enough concentration variability aiming at model robustness. Nevertheless, a minimum number of samples per significant component is always recommended [47]. In this particular case 30 samples were found to be enough to comply with the requirements and objectives of the work. The proposed method is intended to determine the neomycin sulphate content on commercial product (16.5%) containing lactose, talc and magnesium stearate as excipients. Synthetic samples containing the same API and excipients as the commercial sample were prepared. Samples were prepared by mixing the API and different placebos containing the excipients. Five placebos were prepared with the three excipients. Excipients in placebos were varied between 67 and 73% for lactose, 23–28% for talc and 4.6–5.6% for magnesium stearate. The range for each component was calculated by varying a fixed percentage around the nominal concentration in the commercial product. These used mass

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fractions are much larger than those expected to exist in the commercial product but were found to be important to evaluate the method robustness in terms of the estimation of the neomycin concentration. This procedure was used to avoid indirect correlation between the NIR spectra and API content. Concentrations in placebos were defined according to an experimental design (D-optimal design) and prepared by weighing the individual components and mixing them in a shaker mixer (Turbula WAB T2F, Switzerland) for 15 min. Samples were prepared by weighing the placebos and neomycin sulphate, and mixing them in a shaker mixer (Turbula WAB T2F, Switzerland) for 30 min prior to NIRS analyses. The API mass fraction range was varied between 0.01 and 0.30 for the calibration samples and between 0.02 and 0.29 for the test samples. Synthetic samples were prepared using excipients from different manufacturers from those used by the pharmaceutical company to produce the commercial product. It is known that this may cause problems while applying the model to the commercial product. Therefore, a second type of samples was prepared. These samples, hereby called doped samples, were prepared using macerated tablets of the commercial product. Based on the macerated tablets, high and low API concentration samples were prepared by adding placebos (the same used for the synthetic samples) and neomycin sulphate in adequate amounts to cover the concentration range defined. It should be said that placebos were also used to produce samples with high API concentrations. For each type of samples, a total of 45 formulations were produced. From these, 30 were selected for method calibration and 15 for testing. To assess the ability of the two calibrations (one based on the synthetic and one based on the doped samples) to quantify neomycin sulphate in the commercial product, three different lots of the commercial product were tested. Tablets from each product lot were macerated before the spectral analysis. 3.2. High performance liquid chromatography The chromatographic system used to confirm the concentration of neomycin sulphate in the commercial samples was a Jasco LC-200 Plus (USA), equipped with a pump PU-2080 PLUS and a multiwavelength detector MD-2015 Plus. The chromatographic system was controlled by the interface L-Net II/ADC. Data integration and processing were performed with the ChromNav software (Jasco, USA). The chromatographic conditions were set as described in [14]. The HPLC column used was a Chromolith (Merck) C18 (4.6 mm i.d. × 100 mm length). The mobile phase was prepared by mixing 40% (% v/v) of acetonitrile (ACN) with water and 0.1% (% v/v) of trifluoroacetic acid (TFA). The mobile phase was degassed before used. The flow rate was set at 1.5 ml min−1 and the UV detector set at a wavelength of 240 nm. All solutions were prepared with ultrapure Milli-Q water. A standard solution of neomycin sulphate of 1.0 mg ml−1 was prepared and diluted in water to prepare the following concentrations: 30, 20, 10 and 5 ␮g ml−1 . A 500 ␮l aliquot of each standard solution was added to 250 ␮l of phenylisocyanate solution (0.5 mg ml−1 in acetonitrile) and 250 ␮l of triethylamine solution (0.5 mg ml−1 in acetonitrile). The mixtures were reacted at room temperature. ACN (HPLC grade) and TFA (99%) were purchased from Sigma. Phenylisocyanate (>98%) was purchased from Aldrich. Triethylamine (>99%) was purchased from Merck. Neomycin sulphate (>99%) was acquired from Fragon Iberica. 3.3. Acquisition of near infrared spectra Near infrared spectra were recorded on a Fourier-transform NIRS analyser (FTLA 2000, ABB, Québec, Canada) equipped with a powder sampling accessory (ACC101, ABB, Québec, Canada) for diffuse reflectance measurements with a 6 mm diameter illumina-

tion area and an indium–gallium–arsenide (InGaAs) detector. The equipment was controlled via the Bomem-GRAMS software (ABB, Québec, Canada). Each spectrum was acquired with a 2 cm−1 resolution over a wavenumber range between 10,000 and 4000 cm−1 and recorded as the average of 64 scans. The measurements were performed in diffuse reflectance mode using the powder sampling accessory. In the beginning of the measurements and every hour after, a background spectrum was taken by placing the reference material PTFE (Teflon) over the sampling window. Three measurements were made for each sample and the averaged spectrum was considered. 3.4. Data processing The spectral data was pre-processed to remove interferences such as, baseline drifts, light scattering effects and other instrumental variations. The pre-processing methods aim at removing interferences due to physical phenomena (e.g., differences in the particle size distribution). However, it’s known to be very difficult to remove entirely the effects of uncontrolled physical phenomena from the NIR spectra [48] using simple pre-processing methods. A number of pre-processing methods were tested: Savitzky–Golay filter with different filter widths, derivatives, standard normal variate (SNV) and multiplicative scatter correction (MSC). Fig. 1 shows the SNV pre-processed spectra from the synthetic and doped samples used for calibration. 4. Results and discussion An HPLC chromatogram of a neomycin sulphate standard injected in a concentration of 30 ␮g ml−1 is shown in Fig. 2. As can be seen the two main components of neomycin, neomycin B and C are separated by the chromatographic method. Apart from these two peaks and the solvent peak no other peaks are present in the chromatogram implying that the method is specific to neomycin. The regression equation for the calibration curve considering four neomycin sulphate standards (5–30 ␮g ml−1 ) was: Area = 3.9 × 104 [Neomycin sulphate/mg ml−1 ] + 2.2 × 105 , with a determination coefficient of 0.999. The error obtained for the three lots analysed varied from 100.42 to 109.60% of the nominal value. The obtained errors are within the limits established by the Portuguese Pharmacopeia [49]. The discussion of the results was divided in three parts. First, the results regarding the synthetic samples are addressed. Afterwards, the results concerning the doped samples are discussed. To finalize, a comparison between calibrations based on the two types of samples was made focussing on the differences between them and how they affected the results. 4.1. Synthetic samples To construct the calibration model the spectra from the 30 selected samples were correlated with the neomycin sulphate mass fraction using a PLS model. The number of latent variables was optimized using the leave-one-out cross-validation method. The best pre-processing method was chosen based on the lowest RMSECV value achieved. SNV followed by mean-centered (MNCN) was the pre-processing method adopted. The profile of the RMSECV in function of the latent variables is depicted in Fig. 3a. Ten latent variables were needed to reach the lowest cross-validation error. The developed PLS model scores and loadings for the first and third PLS components were analysed. The first PLS component (97.7% of retained variance in Y) accounted for the differences in neomycin sulphate concentrations. The corresponding loading shows a sig-

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Fig. 1. SNV pre-processed NIR spectra of the synthetic and doped samples used for calibration.

nificant analogy with the neomycin sulphate NIR pure spectrum. However, the influence of some of the excipients is also present. It is an example a peak appearing at 7140 cm−1 , an intrinsic feature of talc (see Fig. 1). The ninth loading was chosen because the

cross-validation error decrease is not significant between the ninth and tenth components. Also, the multivariate technique used in this work does not remove some of the information orthogonal to the neomycin sulphate mass fraction, therefore, is possible and normal

Fig. 2. Chromatogram of a neomycin sulphate standard solution at a concentration of 30 ␮g ml−1 .

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Fig. 3. Profile of the RMSECV value in function of the number of latent variables for the synthetic (a) and doped samples (b).

that some components appear although not contributing to a substantial decrease in the calibration error. The evolution of the ninth component can be related to the change in talc concentration. The corresponding loading is mainly dominated by the talc spectrum features. Ten latent variables were kept in the model since this was the indication of the cross-validation. The calibration results are summarized in Table 1. For 10 latent variables a RMSECV of 0.004 was obtained corresponding to a relative error (ratio between the RMSECV and the average parameter value) of 2.7%. A determination coefficient of 0.998 and a mean RSD value of 2.57% were obtained. The RER value of 66.0, significantly higher then 10, indicates that the model is adequate for quality control purposes. To test the calibration model, the 15 test samples spectra were projected onto the calibration model and the results were assessed based on the RMSEP error value. The determination coefficient, mean relative standard deviation, limit of detection, sensitivity and selectivity were also determined. These statistics are summarized in Table 2. A RMSEP value of 0.009 corresponding to a relative error of 5.8% was obtained with a mean RSD of 2.2%. The determination coefficient obtained was 0.998. The prediction error lower than 6% with good reproducibility is quite satisfactory for NIR based predictions. Since the main objective of this work is to develop a new method to quantify neomycin sulphate for quality control purposes in industrially manufactured samples, three different lots of a commercial brand of neomycin sulphate (in the form of macerated tablets) were used to estimate the prediction ability of the developed calibration. The spectra from the three lots were projected into the calibration model. The prediction error and determination coefficient were determined (Table 3). A RMSEP value of 0.177 (101.1%) was found. It is clear that the calibration model does not work in predicting the commercial samples. The calibration model overestimates the neomycin concentration in all three lots. This problem may be caused by differences between the synthetic (used

for calibration) and the commercial form, namely, different excipients manufacturers and physical properties. The spectral region between 7500 and 8820 cm−1 (typically associated with physical properties) was removed. The idea was to verify if this region could compromise the model prediction ability by conveying unwanted physical properties. It was found that removing this range, results for the prediction of test samples and the commercial samples were not improved. As it was already referenced in previous studies the information regarding the particle size in the NIR spectrum is not concentrated on these areas but throughout the spectrum [50]. 4.2. Doped samples A similar procedure was followed for the doped samples. The pre-processing method that gave the lowest RMSECV value was SNV followed by MNCN. The lowest RMSECV value was obtained with nine latent variables. The profile of the RMSECV as function of the number of latent variables is shown in Fig. 3b. The scores and loadings for the first and sixth components are plotted in Fig. 4. The sixth and not the ninth component was selected because the cross-validation errors difference is very small and found to be statistically irrelevant (95% confidence level). However, as in the case of the calibration using the synthetic samples, the number of components indicated by cross-validation was used to construct the model. The increase in neomycin sulphate concentration can be followed in the first component. The corresponding loading shows that some excipients are also being considered in this component (e.g., the sharp peak at 7140 cm−1 characteristic of talc). In the sixth component, the dominant phenomenon that is being modelled is the amount of placebo added to the samples, which is reflected in the loading that has the main features from the three excipients. A RMSECV of 0.005 corresponding to a relative error of 2.9% was obtained for the calibration (Table 1). The determination coefficient

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Table 1 Cross-validation results for the developed NIR method using synthetic and doped samples. Samples

Pre-processing method

Synthetic Doped

SNV + MNCN SNV + MNCN

Latent variables 10 9

RMSECV (mass fraction)

Relative error (%)

R2

RSD (mean) (%)

RER

0.004 0.005

2.7 2.9

0.998 0.997

2.57 1.99

65.95 62.34

Table 2 Test results and figures-of-merit for the NIR method using synthetic and doped samples. Samples

RMSEP (mass fraction)

Relative error (%)

R2

RSD (mean) (%)

LOD (mass fraction)

SEN (dimensionless)

Mean SEL (dimensionless)

Synthetic Doped

0.009 0.004

5.8 2.6

0.998 0.998

2.25 2.85

0.03 0.01

5.72 10.46

0.12 0.22

Table 3 Commercial lots results for the NIR based prediction using synthetic and doped samples. Samples

RMSEP

Relative error (%)

Synthetic Doped

0.177 0.012

101.1 6.6

is of 0.997 and the mean RSD of 1.99%. A RER value of 62.3 suggests that the model is suitable for use in quality control. Spectra obtained from the 15 test samples were projected onto this model to validate it. The model robustness was assessed based on the determination of the RMSEP error value. The results are collected in Table 2. A relative error of prediction of 2.6% with a determination coefficient of 0.998 and a mean RSD of 2.8% shows that the NIR based method is suitable for these determinations. The same three commercial lots were also predicted using this calibration model (Table 3). The prediction relative error value is somewhat higher to the one determined with the test samples

(6.6%). The plot of neomycin sulphate mass fraction versus the predicted NIR neomycin sulphate mass fraction is shown in Fig. 5 for the calibration, test and commercial samples. These results clearly show that the doped samples can be used to predict the commercial samples. The differences between the synthetic and doped samples will be analysed next to understand the reason why the synthetic samples failed to predict accurately the commercial samples. 4.3. Synthetic versus doped samples To understand the spectral differences between the synthetic and doped samples, a principal component analysis (PCA) [37] was performed using the spectra of the synthetic and doped samples together. The spectral pre-processing used was SNV followed by MNCN. The captured variance was 96.1% in the first two components (Fig. 6). In the first component, the increase in the neomycin sulphate can be followed for both types of samples. It is observed that the main difference between the synthetic and doped samples occurs in the second component. Additionally, differences in

Fig. 4. Scores and loadings from the first and sixth components for the doped samples based PLS model.

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Fig. 5. Doped samples neomycin sulphate mass fraction versus NIR predicted neomycin sulphate mass fraction for the calibration (), test () and (

the second component also appear within the synthetic and doped samples. The scores increase on the second component is due to the increase of the placebo added to the samples, in the case of the synthetic samples. In the doped samples two phenomena are present: (1) the addition of placebo and (2) the addition of the macerated tablets. Since the main difference between the two types of samples is the addition of the macerated tablets, it can be concluded that

) commercial lots.

the difference in the results obtained for the synthetic and doped samples is due to the chemical and physical differences arising by the addition of the macerated tablets. The loading of the second component (Fig. 7) is very similar to a mixture spectrum, reflecting the referred phenomenon. The chemical differences between the synthetic and doped samples arising from the addition of the macerated tablets are due to the differences in the excipients used in the

Fig. 6. PCA score plot for the synthetic (䊉) and doped () calibration samples.

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Fig. 7. First and second loadings of the PCA performed on the synthetic and doped calibration samples.

placebos and the excipients contained in the commercial tablets. Also, physical differences, mainly particle size, are also present. The added placebos are very fine powders and the commercial samples that were macerated using a ceramic mortar, have coarser particles. The difference in particle size is not directly visible in the loading of the second component; however, as already discussed, the information regarding the particle size in NIR spectra is present throughout the spectrum.

for the quality control of neomycin sulphate in pharmaceutical formulations. Because the aminoglycosides family is very similar in chemical terms the method developed in this work can be used to quantify others aminoglycosides. A specific calibration is required for each commercial product, although the hereby described procedure is generic. Therefore, the adaptation of this procedure to other commercial forms is straightforward. Acknowledgement

5. Conclusions In this work a new method using NIRS to determine neomycin sulphate in pharmaceutical solid forms was proposed. A first approach using synthetic samples to develop the method was attempted. The calibration model was successful in predicting neomycin sulphate in samples with the same characteristics as the calibration samples but failed to predict it in commercial samples. To overcome this problem, a second set of samples based on the commercial form (doped samples based on macerated tablets of the commercial form) proved to be more reliable, yielding a robust method encompassing sufficient variability to allow accurate predictions of the commercial samples. This method successfully predicted neomycin sulphate in commercial samples with a relative error of 6.6%. It was verified using principal component analysis that the main difference between both types of samples was the addition of the macerated tablets with different chemical and physical properties (particle size). The average particle size of the synthetic samples was lower due to the fine particles of the excipients used as placebos (the macerated tablets had a more coarse grain). It was shown that the use of NIRS is an alternative method to the microbiological reference method with several advantages. It is faster, easier, less expensive, more accurate and precise. Even compared with other methods such as HPLC or amperometric methods, the use of NIRS is a viable alternative. Errors obtained around 6.6% in the prediction of commercial batches show that NIRS can be used

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