A rapid quantitative assay of intact paracetamol tablets by reflectance near-infrared spectroscopy

Near-infrared (NIR) reflectance spectroscopy was used to determine rapidly and non-destructively the content of paracetamol in bulk batches of intact Sterwin 500 mg tablets by collecting NIR spectra in the range 1100-2500 nm and using a multiple linear regression calibration method. The developed NIR method gave results comparable to the British Pharmacopoeia 1993 UV assay procedure, the standard errors of calibration and prediction being 0.48% and 0.71% m/m, respectively. The method showed good repeatability, the standard deviation and coefficient of variation for six NIR assays on the same batch on the same day being 0.14 and 0.16% m/m, respectively, while measurements over six consecutive days gave 0.31 and 0.36% m/m, respectively. Applying the calibration to a parallel test set gave a mean bias of -0.22% and a mean accuracy of 0.45%. The developed method illustrates how the full potential of NIR can be utilised and how the ICH guidelines which recommend the validation of linearity, range, accuracy and precision for pharmaceutical registration purposes can be applied. Duplicate determinations on bulk batches could be performed in under 2 min, allowing the potential use of the method on-line for real time monitoring of a running production process.     

Meeting the International Conference on Harmonisation's Guidelines on Validation of Analytical Procedures: quantification as exemplified by a near-infrared reflectance assay of paracetamol in intact tablets

This Perspective explains how the International Conference on Harmonisation's Guidelines on Validation of Analytical Procedures for quantitative methods can be met by near-infrared (NIR) assays of intact pharmaceutical products. Each of the validation characteristics (accuracy, precision, specificity, detection limit, quantification limit, linearity, range, robustness and system suitability testing) is defined, examined for their relevance to quantitative methods and examples given on how they may be used to demonstrate that near-infrared assays are fit for purpose. Methods for preparing samples for calibration are given in detail. The intention is to provide information so that a pharmaceutical manufacturer could validate a method suitable for an application for a variation of a marketing authorisation for an existing product and use a NIR assay instead of the previous method. The perspective is illustrated in detail using a NIR reflectance assay of paracetamol in intact tablets. This proven assay gives results comparable to the British Pharmacopeia ultraviolet assay for paracetamol, the standard errors of calibration and prediction for the NIR method being 0.48% w/w and 0.71% w/w respectively. The method is also precise, the standard deviation and coefficient of variation for six NIR assays on the same day being 0.14% w/w and 0.16% w/w respectively, while measurements over six consecutive days gave 0.31% w/w and 0.36% w/w respectively.     

Development of a rapid process monitoring method for dry-coated tableting process by using near-infrared spectroscopy

A nondestructive transmittance near-infrared (NIR) method for detecting off-centered cores in dry-coated (DC) tablets was developed as a monitoring system in the DC tableting process. Caffeine anhydrate was used as a core active pharmaceutical ingredient (API), and DC tablets were made by the direct compression method. NIR spectra were obtained from these intact DC tablets using the transmittance method. The reference assay was performed with HPLC. Calibration models were generated by partial least squares (PLS) regression and principal component regression (PCR) utilizing external validations. Hierarchical cluster analysis (HCA) of the results confirmed that NIR spectroscopy correctly detected off-centered cores in DC tablets. We formulated and used the Centering Index (CI) to evaluate the precision of core alignment and generated an NIR calibration model that could correctly predict this index. The principal component (PC) 1 loading vector of the final calibration model indicated that it could specifically detect the misalignment of tablet cores. The model also had good linearity and accuracy. The CIs of unknown sample tablets predicted by the final calibration model and those calculated through the HPLC analysis were closely parallel with each other. These results demonstrate the validity of the final calibration model and the utility of the transmittance NIR spectroscopic method developed in this study as a monitoring system in DC tableting process.     

Quantification of paracetamol in intact tablets using near-infrared transmittance spectroscopy.

Production batch samples of paracetamol tablets and specially prepared out-of-specification batches covering the range 90-110% of the stated amount (500 mg) were analysed by the BP official UV assay and by NIR transmittance spectroscopy. NIR measurements were made on 20 intact tablets from each batch, scanned five times each (10 min measurement time per batch) over the spectral range 6000-11,520 cm-1. An average spectrum was calculated for each batch. Partial least squares (PLS) regression models were set up using a calibration set (20 batches) between the NIR response and the reference tablet paracetamol content (UV). Various pre-treatments of the spectra were examined; the smallest relative standard error of prediction (0.73%) was obtained using the first derivative of the absorbance over the full spectrum. Only two principal components were required for the PLS model to give a good relationship between the spectral information and paracetamol content. Applying this model to the validation set (15 batches) gave a mean bias of -0.08% and a mean accuracy of 0.59% with relative standard deviations of 0.75 and 0.44%, respectively. The proposed method is non-destructive and therefore lends itself to on-line/at-line production control purposes. The method is easy to use and does not require a knowledge of the mass of the tablets.     

Quality control of pharmaceuticals with NIR: From lab to process line

This work describes a general framework for assessing the active pharmaceutical ingredient (API) and excipient concentrations simultaneously in pharmaceutical dosage forms based on laboratory-scale measurements. The work explores the comprehensive development of a near infrared (NIR) analytical protocol for the quantification of the API and excipients of a pharmaceutical formulation. The samples were based on a paracetamol (API) formulation with three excipients: microcrystalline cellulose, talc, and magnesium stearate. The developed method was based on laboratory-scale samples as calibration samples and pilot-scale samples (powders and tablets) as model test samples. Both types of samples were produced according to an experimental design. The samples were measured in reflectance mode in a Fourier-transform NIR spectrometer. Additionally, a new method for determining the minimum number of calibration samples was proposed. It was concluded that the use of laboratory-scale samples to construct the calibration set is an effective way to ensure the concentration variability in the development of calibration models for industrial applications. With this method, both API and excipients can be determined in high-throughput applications in the pharmaceutical industry.     

Qualitative and quantitative analysis of oxytetracycline by near-infrared spectroscopy

Near-infrared (NIR) spectroscopy, in combination with chemometrics, enable the analysis of raw materials without time-consuming sample preparation methods. The aim of our work was to estimate critical parameters in the analytical specification of oxytetracycline, and consequently the development of a method for quantification and qualification of these parameters by NIR spectroscopy. A Karl Fischer (K.F.) titration to determine the water content, a colorimetric assay method, and Fourier transform-infrared (FT-IR) spectroscopy to identify the oxytetracycline base, were used as reference methods, respectively. Multivariate calibration was performed on NIR spectral data using principal component analysis (PCA), partial least-squares (PLS 1) and principal component regression (PCR) chemometric methods. Multivariate calibration models for NIR spectroscopy have been developed. Using PCA and the Soft Independent Modelling of Class Analogy (SIMCA) approach, we established the cluster model for the determination of sample identity. PLS 1 and PCR regression methods were applied to develop the calibration models for the determination of water content and the assay of the oxytetracycline base. Comparing the PLS and PCR regression methods we found out that the PLS is better established by NIR, especially as the spectroscopic data (NIR spectra) are highly collinear and there are many wavelengths due to non-selective wavelengths. The calibration models for NIR spectroscopy are convenient alternatives to the colorimetric method and to the K.F. method, as well as to FT-IR spectroscopy, in the routine control of incoming material.     

Reflectance near-infrared spectroscopic method with Chemometric techniques for simultaneous determination of Chondroitin, glucosamine, and methyl sulfonyl methane

Reflectance near-IR (RNIR) spectroscopy was used for the simultaneous determination of chondroitin (CH), glucosamine (GO), and methyl sulfonyl methane (MSM) in tablets. Simple sample preparation was done by grinding, sieving, and compression of the tablets for improving RNIR spectra. Principal component regression and partial least squares (PLS-1 and PLS-2) were successfully applied to quantify the three components in the studied mixture using information included in RNIR spectra in the range of 4350-9100 cm(-1). The calibration model was developed with drug concentration ranges of 14.5-44.2% (w/w) for CH, 18.4-55.3% (w/w) for GO, and 6-18.6% (w/w) for MSM with addition of tablet excipients to the calibration set in the same ratio as in the tested tablets. The calibration models were evaluated by internal validation, cross-validation, and external validation using synthetic and pharmaceutical preparations. The proposed method was applied for analysis of six batches of the pharmaceutical product. The results of the proposed method were compared with the results of the pharmacopoeial method for the same batch of the pharmaceutical product. No significant differences between the results were found. The RNIR method is accurate and precise, and can be used for QC of pharmaceutical products.     

Determination of total water content in inulin using the volumetric Karl Fischer titration

A new sample preparation method for the water content determination of inulin by volumetric Karl Fischer (KF) titration was developed and compared to the usual method of introducing the sample directly in the methanol-based working medium, modified or not by formamide (1:3, v/v) in order to increase sample solubility. In the proposed method, inulins were externally prepared by dissolving them in pure formamide (2.5:7.5, w/w). The time of analysis of the liquid/liquid reaction of the new method between the dissolved sample and the reaction medium is about 1–2 min, while the usual KF method is stopped after the 10 min delay time. The developed method permits the determination of water included in the crystals of the sample, confirmed by analysing both crystalline and amorphous inulin samples. Another advantage of this new method is its applicability for the water content determination of other polysaccharides that are not readily soluble in the working medium. Moreover, water content determination can be done by any type of volumetric KF titrator, as this proposed technique is not dependent on any additional tools such as a built-in homogeniser or a heatable titration beaker.     

Multivariate near infrared spectroscopy models for predicting methanol and water content in biodiesel

The transesterification of vegetable oils, animal fats or waste oils with an alcohol (such as methanol) in the presence of a homogeneous catalyst (sodium hydroxide or methoxyde) is commonly used to produce biodiesel. The quality control of the final product is an important issue and near infrared (NIR) spectroscopy recently appears as an appealing alternative to the conventional analytical methods. The use of NIR spectroscopy for this purpose first involves the development of calibration models to relate the near infrared spectrum of biodiesel with the analytical data. The type of pre-processing technique applied to the data prior to the development of calibration may greatly influence the performance of the model. This work analyses the effect of some commonly used pre-processing techniques applied prior to partial least squares (PLS) and principal components regressions (PCR) in the quality of the calibration models developed to relate the near infrared spectrum of biodiesel and its content of methanol and water. The results confirm the importance of testing various pre-processing techniques. For the water content, the smaller validation and prediction errors were obtained by a combination of a second order Savitsky-Golay derivative followed by mean centring prior to PLS and PCR, whereas for methanol calibration the best results were obtained with a first order Savitsky-Golay derivative plus mean centring followed by the orthogonal signal correction.     

Simultaneous prediction of ethylene content and melt temperature in melt-state polypropylene by near-infrared spectroscopy and chemometrics.

This paper reports on a simultaneous prediction method for ethylene (C(2)) content and the melt temperature in melt state random polypropylene (RPP) and block polypropylene (BPP) by near-infrared (NIR) spectroscopy and chemometrics. The NIR spectra of RPP and BPP in melt states were measured by a FT-NIR on-line monitoring system. The predicted values of the C(2) content from the RPP or BPP spectra measured at 190 and 250 degrees C were investigated using a calibration model for the C(2) content developed by using each RPP or BPP spectra set measured at 230 degrees C. The errors in the predicted values of the C(2) content depended on the pretreatment methods for each calibration model. It was found that a multiplicative signal correction (MSC) is very effective to compensate for the influence of the change of the RPP or BPP sample temperature on the predicted C(2) content. By using modified MSC, it was demonstrated that simultaneous predictions of the C(2) contents and the relative temperature of the melt RPP and BPP from the NIR spectra could be realized.     

Chemical composition determination of complex organic-aqueous mixtures of alcohols, acetone, acetonitrile, hydrocarbons and water by near-infrared spectroscopy

Near-infrared spectroscopy (NIRS) combined with chemometric techniques has provided fast and precise methods for quality control in the petrochemical industry. In the present work, NIRS multivariate models were used to predict the chemical composition of an organic-aqueous solvent used in an isoprene production plant. The models were developed to predict the mass percent concentration of the components present in the solvent, namely water, acetonitrile, acetone, n-propanol, i-propanol, t-butanol, allyl alcohol and 1,3-dicyclopentadiene (DCPD) plus codimers. Each component concentration was estimated from FT-NIR spectra within the 7300-3900 cm⁻¹ region, using the whole range or only selected regions. Principal component analysis (PCA) was used for exploratory data analysis and partial least-squares models were employed for calibration based on at least 70 samples. The models’ accuracy, in terms of deviation from the primary method (gas chromatography) results, was also confirmed using external validation sets and expressed by the standard error of prediction (SEP) which ranged from 0.18 to 1.27% (w/w). Therefore, the feasibility of performing the chemical composition analysis required for process control of the isoprene unit solvent has been demonstrated. The simple and fast procedure, based on a single NIR spectrum, was able to differentiate between spectroscopic features of water, hydrocarbons, alcohols, ketones and nitrile compounds present in a complex organic-aqueous sample.     

Application of near-infrared spectroscopy in the sugar industry for the detection of betaine

One problem in industrial molasses desugarization is the lack of a fast analytical method for process control. At the moment, control of the chromatographic production process is achieved by detecting refractive index and conductivity. However, since elution of some components takes place only in a narrowly defined time frame, the data gained are insufficient for effective online product quantification. Near-infrared (NIR) spectroscopy was applied to this process by development of a simple method for detection of betaine. Compared to chemometric models currently used, the developed method demonstrates the advantage of requiring only a small calibration set. Additionally, it can easily be transferred to other processes without further re-calibration. Based on the NIR spectrum of betaine, a characteristic peak in the spectrum could be assigned to the molasses compound betaine. A calibration was developed by using dissolved betaine in pure water. Afterwards, the calibration was tested for samples from a molasses desugarization process. The method was than successfully transferred to a complete chromatographic cycle of the industrial molasses desugarization process.     

The transfer between instruments of a reflectance near-infrared assay for paracetamol in intact tablets

This study compares several correction methods to facilitate the transfer of a validated near-infrared (NIR) assay for paracetamol in intact tablets between two reflectance NIR instruments of the same type. Transfer was defined as the ability to accurately predict the true assay value of a sample measured on a NIR system using an assay developed on a different system, and was assessed using a comprehensive set of statistical tests. Direct electronic transfer of the calibration models, representing the NIR assay, was not possible as a result of a definite residual spectrum between instruments. The use of a correction method based on the standardisation of the material used to record the reference spectrum also proved ineffective. Two methods investigated did succeed, the first employed a response surface calculated between the reflectance values of a set of six certified photometric standards measured on both instruments, with all full range partial least square (PLS) regression models subsequently transferred. The next was correction of the spectra from the second instrument utilising the residual spectrum between the mean sample of the validation set measured on both instruments. Through this approach all PLS regression models and also a single multiple linear regression (MLR) model were transferred. As an outcome of this study guidelines are suggested for the transfer of NIR assays along with the criteria deemed necessary to conclusively prove transfer and justify any correction method utilised. The significant criteria were determined to be the paired t-test with both the UV reference assay data and the original NIR assay data, and comparison of the coefficient of multiple determinations.     

Development and validation of a method for active drug identification and content determination of ranitidine in pharmaceutical products using near-infrared reflectance spectroscopy: a parametric release approach

In this paper we describe the strategy used in the development and validation of a near-infrared diffuse reflectance spectroscopy method for identification and quantification of ranitidine in pharmaceutical products (granulates, cores and coated tablets) at-line, with a fiber optic probe. This method was developed in a pharmaceutical industry for routine application, to replace reference methods and was submitted and approved to the National Medicine Regulatory Agency (Infarmed). We consider that this is the first step of a broader parametric release approach to pharmaceutical products.     

Application of transmission near-infrared spectroscopy to uniformity of content testing of intact steroid tablets

Transmission near-infrared (NIR) spectroscopy was used for the rapid and non-destructive determination of the content of a hormone steroid in single intact tablets. Tablets produced for clinical trial purposes containing 5, 10, 15, 20 and 30 mg (2.94, 5.88, 8.82, 11.76 and 17.64% m/m, respectively) were used to develop calibration models without the need to specially prepare any out of specification tablets. Reference values for the individual tablets used in the NIR calibration models and test set were measured by reversed-phase high performance liquid chromatography (HPLC). Partial least squares regression using standard normal variate transformed second-derivative spectra over the range 800 to 1040 nm gave the optimum calibration model with a standard error of calibration of 0.52 mg per tablet. Measurements of an independent test set gave comparable results (standard error of prediction 0.31 mg per tablet). Measurement errors for a single tablet (RSD < 2.5% for a given active level) were sufficiently small to allow the procedure to be applied to pharmacopoeial uniformity of content testing of batches of these tablets and permitted the non-destructive testing of 30 tablets in under 20 min as compared to 6 h by HPLC.     

Development of new analytical methods for the determination of caffeine content in aqueous solution of green coffee beans

Background

This study was conducted to develop fast and cost effective methods for the determination of caffeine in green coffee beans. In the present work direct determination of caffeine in aqueous solution of green coffee bean was performed using FT-IR-ATR and fluorescence spectrophotometry. Caffeine was also directly determined in dimethylformamide solution using NIR spectroscopy with univariate calibration technique.

Results

The percentage of caffeine for the same sample of green coffee beans was determined using the three newly developed methods. The caffeine content of the green coffee beans was found to be 1.52 ± 0.09 (% w/w) using FT-IR-ATR, 1.50 ± 0.14 (% w/w) using NIR and 1.50 ± 0.05 (% w/w) using fluorescence spectroscopy. The means of the three methods were compared by applying one way analysis of variance and at p = 0.05 significance level the means were not significantly different. The percentage of caffeine in the same sample of green coffee bean was also determined by using the literature reported UV/Vis spectrophotometric method for comparison and found to be 1.40 ± 0.02 (% w/w).

Conclusion

New simple, rapid and inexpensive methods were developed for direct determination of caffeine content in aqueous solution of green coffee beans using FT-IR-ATR and fluorescence spectrophotometries. NIR spectrophotometry can also be used as alternative choice of caffeine determination using reduced amount of organic solvent (dimethylformamide) and univariate calibration technique. These analytical methods may therefore, be recommended for the rapid, simple, safe and cost effective determination of caffeine in green coffee beans.

     

Robust calibrations on reduced sample sets for API content prediction in tablets: Definition of a cost-effective NIR model development strategy

Owing to spectral variations from other sources than the component of interest, large investments in the NIR model development may be required to obtain satisfactory and robust prediction performance. To make the NIR model development for routine active pharmaceutical ingredient (API) prediction in tablets more cost-effective, alternative modelling strategies were proposed. They used a massive amount of prior spectral information on intra- and inter-batch variation and the pure component spectra to define a clutter, i.e., the detrimental spectral information. This was subsequently used for artificial data augmentation and/or orthogonal projections. The model performance improved statistically significantly, with a 34–40% reduction in RMSEP while needing fewer model latent variables, by applying the following procedure before PLS regression: (1) augmentation of the calibration spectra with the spectral shapes from the clutter, and (2) net analyte pre-processing (NAP). The improved prediction performance was not compromised when reducing the variability in the calibration set, making exhaustive calibration unnecessary. Strong water content variations in the tablets caused frequency shifts of the API absorption signals that could not be included in the clutter. Updating the model for this kind of variation demonstrated that the completeness of the clutter is critical for the performance of these models and that the model will only be more robust for spectral variation that is not co-linear with the one from the property of interest.     

Using chemometric methods and NIR spectrophotometry in the textile industry

A quantitative and qualitative technique for identification of textiles, moisture measurements, textile coatings and process control was developed, using near infra-red spectroscopy (NIR) in combination with chemometric methods. These applications demonstrate by the use of computer assisted data processing the possibility of identifying textile fibers, not only for quality control but also for online textile recycling processes. In this study, seven various textile fibers (cotton, polyester, viscose, silk, wool, polyacrylonitrile, acetate) were used and all combinations of two factor blends were qualitatively identified using NIR spectroscopy and the chemometric PLS2 method for the calibration.

A quantitative analysis of textile moisture can also be performed with this technique. Water content above 50% does not deliver good results for a calibration set to determine the dampness of fibers. But to measure residual moisture from ≈0.05 up to 50%, the NIR technique is particularly good. Furthermore, the examination shows that the NIR method and chemometric methods can be used in quality- and product-control during the industrial production of upholstery fabrics. With this technique it will be possible to identify nylon flocks and to measure the residual moisture of the flocks and fabric, too.     

Multivariate calibration on NIR data: development of a model for the rapid evaluation of ethanol content in bakery products

A new NIR method based on multivariate calibration for determination of ethanol in industrially packed wholemeal bread was developed and validated. GC-FID was used as reference method for the determination of actual ethanol concentration of different samples of wholemeal bread with proper content of added ethanol, ranging from 0 to 3.5% (w/w). Stepwise discriminant analysis was carried out on the NIR dataset, in order to reduce the number of original variables by selecting those that were able to discriminate between the samples of different ethanol concentrations. With the so selected variables a multivariate calibration model was then obtained by multiple linear regression. The prediction power of the linear model was optimized by a new “leave one out” method, so that the number of original variables resulted further reduced.     

Theoretical analysis of tablet hardness prediction using chemoinformetric near-infrared spectroscopy.

In order to clarify the theoretical basis of the variability in the measurement of tablet hardness by compression pressure, NIR spectroscopic methods were used to predict tablet hardness of the formulations. Tablets (200 mg, 8 mm in diameter) consisting of berberine chloride, lactose, and potato starch were formed at various compression pressures (59, 78, 98, 127, 195 MPa). The hardness and the distribution of micropores were measured. The reflectance NIR spectra of various compressed tablets were used as a calibration set to establish a calibration model to predict tablet hardness by principal component regression (PCR) analysis. The distribution of micropores was shifted to a smaller pore size with increasing compression pressure. The total pore volume of tablets decreased as the compression pressure increased. The hardness increased as the compression pressure increased. The hardness could be predicted using a calibration model consisting of 7 principal components (PCs) obtained by PCR. The relationship between the predicted and the actual hardness values exhibited a straight line, an R(2) of 0.925. In order to understand the theoretical analysis (scientific background) of calibration models used to evaluate tablet hardness, the standard error of cross validation (SEV) values, the loading vectors of each PC and the regression vector were investigated. The result obtained with the calibration models for hardness suggested that the regression vector might involve physical and chemical factors. In contrast, the porosity could be predicted using a calibration model composed of 2 PCs. The relationship between the predicted and the actual total pore volume showed a straight line with R(2) = 0.801. The regression vector of the total pore volume might be due to physical factors.