Development and validation of a near-infrared method for the quantitation of caffeine in intact single tablets

The Use of Near-infrared as Process Analytical Technology (PAT) during 3D Printing Tablets at the Point-of-Care

Fused deposition modelling (FDM) is one of the most researched 3D printing technologies that holds great potential for low-cost manufacturing of personalised medicine. To achieve real-time release, timely quality control is a major challenge for applying 3D printing technologies as a point-of-care (PoC) manufacturing approach. This work proposes the use of a low-cost and compact near-infrared (NIR) spectroscopy modality as a process analytical technology (PAT) to monitor a critical quality attribute (drug content) during and after FDM 3D printing process. 3D printed caffeine tablets were used to manifest the feasibility of the NIR model as a quantitative analytical procedure and dose verification method. Caffeine tablets (0-40% w/w) were fabricated using polyvinyl alcohol and FDM 3D printing. The predictive performance of the NIR model was demonstrated in linearity (correlation coefficient, R²) and accuracy (root mean square error of prediction, RMSEP). The actual drug content values were determined using the reference high-performance liquid chromatography (HPLC) method. The model of full-completion caffeine tablets demonstrated linearity (R² = 0.985) and accuracy (RMSEP =1.4%), indicated to be an alternative dose quantitation method for 3D printed products. The ability of the models to assess caffeine contents during the 3D printing process could not be accurately achieved using the model built with complete tablets. Instead, by building a predictive model for each completion stage of 20%, 40%, 60% and 80%, the model of different completion caffeine tablets displayed linearity (R² of 0.991, 0.99, 0.987, and 0.983) and accuracy (RMSEP of 2.22%, 1.65%, 1.41%, 0.83%), respectively. Overall, this study demonstrated the feasibility of a low NIR model as a non-destructive, low-cost, compact, and rapid analysis dose verification method enabling the real-time release to facilitate 3D printing medicine production in the clinic.

A weighted twin support vector machine as a potential discriminant analysis tool and evaluation of its performance for near-infrared spectroscopic discrimination of the geographical origins of diverse agricultural products

A weighted twin support vector machine (wTWSVM) was proposed as a potential discriminant analysis tool and its utility was evaluated for near-infrared (NIR) spectroscopic identification of the geographical origins of 12 different agricultural products including black soybean and garlic. In the wTWSVM, weights were applied on each variable in the sample spectra to highlight detailed NIR spectral features and the optimal weights to minimize the discrimination error were iteratively searched. Then, the weighted spectra were employed to determine the samples' geographical origins using a TWSVM adopting two non-parallel hyperplanes for the discrimination. For the performance evaluation, SVM, TWSVM, and wTWSVM were separately used for the two-group discriminations and their accuracies were comparatively analyzed. When the SVM and TWSVM accuracies were compared, the improvements by using the TWSVM were significant (95% confidence level) for 10 out of the 12 products. Moreover, the accuracy improvements with the wTWSVM against SVM were significant for all the 12 products. In the case of the TWSVM-wTWSVM accuracy comparison, the improvements by the wTWSVM were also significant for 10 products, thereby demonstrating superior discrimination performance of wTWSVM. Based on the overall results, the wTWSVM could be a potential chemometric tool for discriminant analysis and expandable to other areas such as spectroscopy-based biomedical disease diagnosis and forensic analysis.

Quantitation of drug content in a low dosage formulation by transmission near infrared spectroscopy

A transmission near infrared (NIR) spectroscopic method has been developed for the nondestructive determination of drug content in tablets with less than 1% weight of active ingredient per weight of formulation (m/m) drug content. Tablets were manufactured with drug concentrations of ∼0.5%, 0.7%, and 1.0% (m/m) and ranging in drug content from 0.71 to 2.51 mg per tablet. Transmission NIR spectra were obtained for 110 tablets that constituted the training set for the calibration model developed with partial least squares regression. The reference method for the calibration model was a validated UV spectrophotometric method. Several data preprocessing methods were used to reduce the effect of scattering on the NIR spectra and base the calibration model on spectral changes related to the drug concentration changes. The final calibration model included the spectral range from 11 216 to 8662 cm^-1 the standard normal variate (SNV), and first derivative spectral pretreatments. This model was used to predict an independent set of 48 tablets with a root mean standard error of prediction (RMSEP) of 0.14 mg, and a bias of only -0.05 mg per tablet. The study showed that transmission NIR spectroscopy is a viable alternative for nondestructive testing of low drug content tablets, available for the analysis of large numbers of tablets during process development and as a tool to detect drug agglomeration and evaluate process improvement efforts.

On-line monitoring of vacuum drying of theophylline using NIR spectroscopy: solid-state transitions, water content and semi-empirical modeling

The aim of this work was to monitor in-line and at a real time, the solid-state forms during pharmaceuticals manufacturing. It concerns the dehydration behavior and the solid-state transitions of theophylline in an agitated vacuum contact dryer. First, a near infrared spectroscopy (NIRS) method was performed using a reflectance diffuse probe to measure the in-line and in-situ exact composition of the mixture of different forms of theophylline and water content during drying. A multivariate modeling has been investigated to build a robust model which can predict four components at the same time during drying process. The XRPD analysis was used as a reference method in the process of calibration of NIRS. The indicators of the accuracy in quantitative spectral analysis confirm the robustness of the model and the efficiency of the method of calibration. Second, the kinetics of solid state transformations were investigated. It was shown that the dehydration advanced first by the formation of the metastable anhydrate and after a lag time of the stable one. Once the stable form appeared, formation of the metastable form came to an end. The temperature was found out to be the main factor controlling the overall process rate but also the final contents of the stable and metastable anhydrates for the considered dryer and operating conditions range. Finally, a semi-empirical drying model was proposed and significant quantitative differences were found, particularly at the product temperature which was probably caused by the excessive simplicity of the model.

Simultaneous determination of the impurity and radial tensile strength of reduced glutathione tablets by a high selective NIR-PLS method

This paper establishes a high-throughput and high selective method to determine the impurity named oxidized glutathione (GSSG) and radial tensile strength (RTS) of reduced glutathione (GSH) tablets based on near infrared (NIR) spectroscopy and partial least squares (PLS). In order to build and evaluate the calibration models, the NIR diffuse reflectance spectra (DRS) and transmittance spectra (TS) for 330 GSH tablets were accurately measured by using the optimized parameter values. For analyzing GSSG or RTS of GSH tablets, the NIR-DRS or NIR-TS were selected, subdivided reasonably into calibration and prediction sets, and processed appropriately with chemometric techniques. After selecting spectral sub-ranges and neglecting spectrum outliers, the PLS calibration models were built and the factor numbers were optimized. Then, the PLS models were evaluated by the root mean square errors of calibration (RMSEC), cross-validation (RMSECV) and prediction (RMSEP), and by the correlation coefficients of calibration (R(c)) and prediction (R(p)). The results indicate that the proposed models have good performances. It is thus clear that the NIR-PLS can simultaneously, selectively, nondestructively and rapidly analyze the GSSG and RTS of GSH tablets, although the contents of GSSG impurity were quite low while those of GSH active pharmaceutical ingredient (API) quite high. This strategy can be an important complement to the common NIR methods used in the on-line analysis of API in pharmaceutical preparations. And this work expands the NIR applications in the high-throughput and extraordinarily selective analysis.

Improvement of spectral calibration for food analysis through multi-model fusion

Near-infrared (NIR) spectroscopy will present a more promising tool for quantitative analysis if the predictive ability of the calibration model is further improved. To achieve this goal, a new ensemble calibration method based on uninformative variable elimination (UVE)-partial least square (PLS) is proposed, which is named as ensemble PLS (EPLS), meaning a fusion of multiple PLS models. In this method, different calibration sets are first generated by bootstrap and different PLS models are obtained. Then, the UVE is used to shrink the original variable space into a specific subspace. By repeating this process, a fixed number of candidates PLS member models are obtained. Finally, a smaller part of candidate models are integrated to produce an ensemble model. In order to verify the performance of EPLS, three NIR spectral datasets from food industry were used for illustration. Both full-spectrum PLS and UVEPLS of single models were used as reference. It was found that the proposed method could lead to lower RMSEP (root mean square error of prediction) value than PLS and UVEPLS and such an improvement is statistically significant according to a paired t-test. The results showed that the method is of value to enhance the predictive ability of PLS-based calibration involving complex NIR matrices in food analysis.

Non-destructive detection of adulterated tablets of glibenclamide using NIR and solid-phase fluorescence spectroscopy and chemometric methods

This study describes a method for non-destructive detection of adulterated glibenclamide tablets. This method uses near infrared spectroscopy (NIRS) and fluorescence spectroscopy along with chemometric tools such as Soft Independent Modeling of Class Analogy (SIMCA), Partial Least Squares-Discriminant Analysis (PLS-DA) and Unfolded Partial Least Squares with Discriminant Analysis (UPLS-DA). Both brand name (Daonil) and generic glibenclamide tablets were used for analysis. The levels of glibenclamide in each type of tablet were evaluated by derivative spectrophotometry in the ultraviolet region. The results obtained from the NIR and fluorescence spectroscopy along with those obtained from multivariate data classification show that this combined technique is an effective way to detect adulteration in drugs for the treatment of diabetes. In the future, this method may be extended to detect different types of counterfeit medications.

NIR spectroscopy applications in the development of a compacted multiparticulate system for modified release

The purpose of this study was to utilize near-infrared spectroscopy and chemical imaging to characterize extrusion-spheronized drug beads, lipid-based placebo beads, and modified release tablets prepared from blends of these beads. The tablet drug load (10.5-19.5 mg) of theophylline (2.25 mg increments) and cimetidine (3 mg increments) could easily be differentiated using univariate analyses. To evaluate other tablet attributes (i.e., compression force, crushing force, content uniformity), multivariate analyses were used. Partial least squares (PLS) models were used for prediction and principal component analysis (PCA) was used for classification. The PLS prediction models (R (2) >0.98) for content uniformity of uncoated compacted theophylline and cimetidine beads produced the most robust models. Content uniformity data for tablets with drug content ranging between 10.5 and 19.5 mg showed standard error of calibration (SEC), standard error of cross-validation, and standard error of prediction (SEP) values as 0.31, 0.43, and 0.37 mg, and 0.47, 0.59, and 0.49 mg, for theophylline and cimetidine, respectively, with SEP/SEC ratios less than 1.3. PCA could detect blend segregation during tableting for preparations using different ratios of uncoated cimetidine beads to placebo beads (20:80, 50:50, and 80:20). Using NIR chemical imaging, the 80:20 formulations showed the most pronounced blend segregation during the tableting process. Furthermore, imaging was capable of quantitating the cimetidine bead content among the different blend ratios. Segregation testing (ASTM D6940-04 method) indicated that blends of coated cimetidine beads and placebo beads (50:50 ratio) also tended to segregate.

Near infrared spectroscopy in the development of solid dosage forms

The use of near infrared (NIR) spectroscopy has rapidly grown partly due to demands of process analytical applications in the pharmaceutical industry. Furthermore, newest regulatory guidelines have advanced the increase of the use of NIR technologies. The non-destructive and non-invasive nature of measurements makes NIR a powerful tool in characterization of pharmaceutical solids. These benefits among others often make NIR advantageous over traditional analytical methods. However, in addition to NIR, a wide variety of other tools are naturally also available for analysis in pharmaceutical development and manufacturing, and those can often be more suitable for a given application. The versatility and rapidness of NIR will ensure its contribution to increased process understanding, better process control and improved quality of drug products. This review concentrates on the use of NIR spectroscopy from a process research perspective and highlights recent applications in the field.

Analysis of solid-state transformations of pharmaceutical compounds using vibrational spectroscopy

Objectives

Solid-state transformations may occur during any stage of pharmaceutical processing and upon storage of a solid dosage form. Early detection and quantification of these transformations during the manufacture of solid dosage forms is important since the physical form of an active pharmaceutical ingredient can significantly influence its processing behaviour, including powder flow and compressibility, and biopharmaceutical properties such as solubility, dissolution rate and bioavailability.

Key findings

Vibrational spectroscopic techniques such as infrared, near-infrared, Raman and, most recently, terahertz pulsed spectroscopy have become popular for solidstate analysis since they are fast and non-destructive and allow solid-state changes to be probed at the molecular level. In particular, Raman and near-infrared spectroscopy, which require no sample preparation, are now commonly used coupled to fibreoptic probes and are able to characterise solid-state conversions in-line. Traditionally, uni- or bivariate approaches have been used to analyse spectroscopic data sets; however, recently the simultaneous detection of several solid-state forms has been increasingly performed using multivariate approaches where even overlapping spectral bands can be analysed.

Summary

This review discusses the applications of different vibrational spectroscopic techniques to detect and monitor solid-state transformations possible for crystalline polymorphs, hydrates and amorphous forms of pharmaceutical compounds. In this context, the theoretical basis of solid-state transformations and vibrational spectroscopy and common experimental approaches are described, including recent methods of data analysis.

Quality by design, part I: application of NIR spectroscopy to monitor tablet manufacturing process

To monitor tableting production using near infrared (NIR) spectroscopy, chemometric models were developed to analyze peak compression force, crushing strength and content uniformity. To measure tablet content uniformity, orbifloxacin tablets with drug content ranging from 60 to 90 mg were made and analyzed using ultraviolet (UV) and NIR spectroscopy. To assess the compression force and crushing strength, several batches of tablets were made on a Stokes B2 rotary tablet press and compression force was varied from 360 to 3500 lb. Principal component analysis (PCA) was used to identify tablets with regular and capped tablets breakage patterns. Comparison of statistical parameters showed that partial least squares (PLS) models gave better fit than the multiple linear regression (MLR) models. The best fit PLS models had a standard error of calibration (SEC) and a standard error of prediction (SEP) for content uniformity of 1.13 and 1.36 mg; for compression force of 69.86 and 59.48 lb and for crushing strength 0.55 kP and 0.57 kP, respectively. NIR spectroscopy in combination with multivariate modeling is a rapid and nondestructive technique that could reliably predict content uniformity, compression force and crushing strength for orbifloxacin tablets.

Variable selection, outlier detection, and figures of merit estimation in a partial least-squares regression multivariate calibration model. A case study for the determination of quality parameters in the alcohol industry by near-infrared spectroscopy

Practical implementation of multivariate calibration models has been limited in several areas due to the requirement of appropriate development and validation to prove their performance to standardization agencies. Herein, a detailed description of the application of multivariate calibration based on partial least-squares regression models (PLSR) for the determination of soluble solids (BRIX), polarizable sugars (POL), and reducing sugars (RS) in sugar cane juice, based on near infrared spectroscopy (NIR), for the alcohol industries is presented. The development of the models, including variable selection and outlier elimination, and their validation by determination of figures of merit, such as accuracy, precision, sensitivity, analytical sensitivity, prediction intervals, and limits of detection and quantification, are described for a representative data set of 1381 sugar cane samples. Values estimated by PLSR are compared with appropriate reference methods, where the results indicated that the PLSR models can be used in the alcohol industry as an alternative to refractometry and lead clarification before polarization measurements (standard methods for BRIX and POL, respectively). For RS, the results of a titration reference method were compared with the PLSR estimates and also with an estimate based on BRIX and POL values, as actually used in the alcohol industry. The PLSR method presented a better agreement with the titration method. However, the results indicated that the RS estimates from both PLSR and those based on the BRIX and POL values, actually used, should be improved to a safe determination of RS.

Validation of manufacturing process of Diltiazem HCl tablets by NIR spectrophotometry (NIRS)

The goal of this study was to apply the Process Analytical Technology FDA's initiative in pharmaceutical tablets manufacturing. Near Infrared Spectrophotometry (NIRS) was used as a non-destructive, very fast technique requiring no sample preparation. Direct compression powder blends containing Diltiazem HCl as a model drug were pressed into tablets for the calibration and the validation steps. First, a partial least squares model was built to calibrate the NIR spectrometer. Then, this model was validated and compared with a validated UV spectrophotometry reference method. For this comparison, the Bland and Altman's statistical method was applied. The manufacturing process was validated by producing three batches at three different concentration levels. The NIR analysis of these batches was performed during 3 days. This study shows that NIRS can be used to validate the whole manufacturing process and not only as an analytical method for tablets assay. NIRS is an interesting tool to show possible variations during the manufacturing process which could lead the finished product to fall outside of specifications.

Quantitative determination of soybean meal content in compound feeds: comparison of near-infrared spectroscopy and real-time PCR

Standard methods for determining the raw material content of compound feed are little exploited, except for the identification of meat and bone meal in feeds. In this work, near-infrared (NIR) spectroscopy and real-time polymerase chain reaction (PCR) were applied in order to establish new and fast methods for quantification of soybean meal content in compound feeds. The best prediction quality was achieved by using a model based on NIR spectroscopy (R2 = 0.9857, standard error of cross-validation 1.1065). Furthermore, a sensitive qualitative detection method by using the real-time PCR was developed (R2 = 0.976, slope -3.7599). Finally, the differences between the real-time PCR result and the NIR spectroscopy result for a given sample were also treated, and we found that the NIR spectroscopy method provided quite accurate results which approach closely those of the real-time PCR method.

A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies

Near-infrared spectroscopy (NIRS) is a fast and non-destructive analytical method. Associated with chemometrics, it becomes a powerful tool for the pharmaceutical industry. Indeed, NIRS is suitable for analysis of solid, liquid and biotechnological pharmaceutical forms. Moreover, NIRS can be implemented during pharmaceutical development, in production for process monitoring or in quality control laboratories. This review focuses on chemometric techniques and pharmaceutical NIRS applications. The following topics are covered: qualitative analyses, quantitative methods and on-line applications. Theoretical and practical aspects are described with pharmaceutical examples of NIRS applications.

Transfer of multivariate classification models between laboratory and process near-infrared spectrometers for the discrimination of green Arabica and Robusta coffee beans

Analogous to the situation found in calibration, a classification model constructed from spectra measured on one instrument may not be valid for prediction of class from spectra measured on a second instrument. In this paper, the transfer of multivariate classification models between laboratory and process near-infrared spectrometers is investigated for the discrimination of whole, green Coffea arabica (Arabica) and Coffea canefora (Robusta) coffee beans. A modified version of slope/bias correction, orthogonal signal correction trained on a vector of discrete class identities, and model updating were found to perform well in the preprocessing of data to permit the transfer of a classification model developed on data from one instrument to be used on another instrument. These techniques permitted development of robust models for the discrimination of green coffee beans on both spectrometers and resulted in misclassification errors for the transfer process in the range of 5-10%.

Construction of universal quantitative models for determination of roxithromycin and erythromycin ethylsuccinate in tablets from different manufacturers using near infrared reflectance spectroscopy

Universal quantitative models using NIR reflectance spectroscopy were developed for the analysis of API contents (active pharmaceutical ingredient) in roxithromycin and erythromycin ethylsuccinate tablets from different manufacturers in China. The two quantitative models were built from 78 batches of roxithromycin samples from 18 different manufacturers with the API content range from 19.5% to 73.9%, and 66 batches erythromycin ethylsuccinate tablets from 36 manufacturers with the API content range from 28.1% to 70.9%. Three different spectrometers were used for model construction in order to have robust and universal models. The root mean square errors of cross validation (RMSECV) and the root mean square errors of prediction (RMSEP) of the model for roxithromycin tablets were 1.84% and 1.45%, respectively. The values of RMSECV and RMSEP of the model for erythromycin ethylsuccinate tablets were 2.31% and 2.16%, respectively. Based on the ICH guidelines and characteristics of NIR spectroscopy, the quantitative models were then evaluated in terms of specificity, linearity, accuracy, precision, robustness and model transferability. Our study has shown that it is feasible to build a universal quantitative model for quick analysis of pharmaceutical products from different manufacturers. Therefore, the NIR method could be used as an effective method for quick, non-destructive inspection of medicines in the distribution channels or open market.

Simultaneous quantitation of five active principles in a pharmaceutical preparation: Development and validation of a near infrared spectroscopic method

A near infrared spectroscopic method for the simultaneous determination of the active principles paracetamol, ascorbic acid, dextrometorphan hydrobromide, caffeine and chlorpheniramine maleate in a pharmaceutical preparation was developed. The five active principles are quantified using a partial least-squares regression method (PLS1). The proposed method is applicable over a wide analyte concentration range (0.04-6.50 wt.%), so it requires careful selection of the calibration set. Also, there is the difficulty of ensuring thorough homogenization of the product. The method was validated in accordance with the ICH standard and the EMEA validation guidelines for NIR spectroscopy by determining its selectivity, linearity, accuracy, precision and robustness. Based on the results, it is an effective alternative to existing choices (HPLC and redox titrimetry) for the same purpose.

Using terahertz pulsed spectroscopy to quantify pharmaceutical polymorphism and crystallinity

Terahertz pulsed spectroscopy (TPS) is a new technique that is capable of eliciting rich information when investigating pharmaceutical materials. In solids, it probes long-range crystalline lattice vibrations and low energy torsion and hydrogen bonding vibrations. These properties make TPS potentially an ideal tool to investigate crystallinity and polymorphism. In this study four drugs with different solid-state properties were analyzed using TPS and levels of polymorphism and crystallinity were quantified. Carbamazepine and enalapril maleate polymorphs, amorphous, and crystalline indomethacin, and thermotropic liquid crystalline and crystalline fenoprofen calcium mixtures were quantified using partial least-squares analysis. Root-mean-squared errors of cross validation as low as 0.349% and limits of detection as low as approximately 1% were obtained, demonstrating that TPS is an analytical technique of potential in quantifying solid-state properties of pharmaceutical compounds.

Real-time near-infrared monitoring of content uniformity, moisture content, compact density, tensile strength, and young's modulus of roller compacted powder blends

A method for real-time in-line near-infrared (NIR) monitoring of roller compaction is reported. Multivariate analysis using partial least square projections to latent structures (PLS) was used to relate the spectral data with key compact attributes: content uniformity, moisture content, relative density, tensile strength, and Young's modulus. NIR calibration curves were generated using the spectral data collected on simulated ribbons, that is, tablets prepared under uni-axial compression, and tested on the data collected on another set of simulated ribbons and by monitoring the ribbons as they exited the roller compactor. For all compact attributes, the NIR predicted values agreed well with the values measured using a reference method.

Figures of merit for the determination of the polymorphic purity of carbamazepine by infrared spectroscopy and multivariate calibration

Polymorphism is an important property in the quality control of pharmaceutical products. In this regard, partial least squares regression and the net analytical signal were used to build and validate a multivariate calibration model using diffuse reflectance infrared spectroscopy in the region of 900-1100 cm(-1) for the determination of the polymorphic purity of carbamazepine. Physical mixtures of the polymorphs were made by weight, from 80 to 100% (w/w) form III mixed with form I. Figures of merit, such as sensitivity, analytical sensitivity, selectivity, confidence limits, precision (mean, repeatability, intermediate), accuracy, and signal-to-noise ratio were calculated. Feasible results were obtained with maximum absolute error of 2% and an average error of 0.53%, indicating that the proposed methodology can be used by the pharmaceutical industry as an alternative to the X-ray diffraction (United States Pharmacopoeiamethod).

Continuous Wavelet Transform Applied to Removing the Fluctuating Background in Near-Infrared Spectra

A novel method based on continuous wavelet transform (CWT) was proposed as a preprocessing tool for the near-infrared (NIR) spectra. Due to the property of the vanishing moments of the wavelet, the fluctuating background of the NIR spectra can be successfully removed through convolution of the spectra with an appropriate wavelet function. The vanishing moments of a wavelet and the scale parameter are two key factors that govern the result of the background elimination. The result of its application to both the simulated spectra and the NIR spectra of tobacco samples demonstrates that CWT is a competitive tool for removing fluctuating background in spectra.

Determination of chlorogenic acid in plant samples by using near-infrared spectrum with wavelet transform preprocessing

By theoretical analysis, it is found that wavelet transform (WT) with a wavelet function can be regarded as a smoothing and a differentiation process, and that the order of differentiation is determined by the vanishing moment, which is an important property of a wavelet function. Therefore, a method based on the continuous wavelet transform (CWT) for removing the background in the near-infrared (NIR) spectrum is proposed, and it is used in the determination of the chlorogenic acid in plant samples as a preprocessing tool for partial least square (PLS) modeling. It is shown that the benefit of the proposed method lies not only in its performance to improve the quality of PLS model and the prediction precision, but also in its simplicity and practicability. It may become a convenient and efficient tool for preprocessing NIR spectral data sets in multivariate calibration.

Determination of the thickness of plastic sheets used in blister packaging by near infrared spectroscopy: development and validation of the method

A near infrared (NIR) quantitative analysis method was developed for determining the thickness of PVC-based plastic sheets used as pharmaceutical packs. Samples that can be analyzed are transparent films made of polyvinyl chloride (PVC), PVC coated with polyvinylidene dichloride (PVDC) or PVC coated with Thermoelast (TE) and PVDC. The method, based on a partial least squares (PLS) algorithm, is used together with a previously developed NIR identification method to acquire simultaneously qualitative and quantitative information about the samples. Validation of the quantitative method was conducted according to the very recent European Agency for the Evaluation of Medicinal Products (EMEA) guidance on the use of NIR spectroscopy. Suggestions were made for a better statistical evaluation of the calibration model prior to validation. Validation consisted of the study of specificity, accuracy (mean recovery from the reference values was 99.56%), precision (repeatability and intermediate precision were <0.6%), linearity, quantification limit (41 microm), and robustness of the method. This demonstration of the applicability of NIR spectroscopy as a validated quality control tool for pharmaceutical packaging films will hopefully facilitate the acceptance of NIR spectroscopy in pharmaceutical laboratories.