Near-infrared Method Research Articles

Development, validation and influence factor analysis of a near-infrared method for the molecular weight determination of xanthan gum

A practical molecular weight determination model of xanthan gum (XG), based on near-infrared (NIR) spectroscopy, was built in this study. Two sample measurement modules, integrating sphere module and fiber-optic probe module, were compared, and the best partial least square (PLS) regression model was based on fiber-optic probe module. The values of coefficient of determination in calibration (R2c), coefficient of determination in prediction (R2p), residual predictive deviation (RPD) and root mean square error of prediction (RMSEP) were 0.967, 0.975, 6.028 and 0.250×106Da, respectively. The molecular weight range, linearity, accuracy and precision of the established method were also validated. Furthermore, influence factors on this method were discussed in order to establish an appropriate measurement protocol. Results showed that the proposed NIR method may be suitable for practical applications in manufacturing plants and probably be accepted as a good alternative approach for fast determination of molecular weight of XG in production process.

Analysis of powder phenomena inside a Fette 3090 feed frame using in-line NIR spectroscopy

New analytical methods are needed to understand and optimize the processes by which tablets are produced. Fette 3090 tablet presses are commonly used in the pharmaceutical industry. A near-infrared (NIR) probe was installed into a Fette 3090 feed frame to understand and monitor the die filling process. The second objective was to analyze in detail the different factors that could affect the prediction of the developed NIR calibration models. Two monitoring positions for NIR spectrometers were evaluated; one at each side of the feed frame. A powder wave behavior caused by the paddle motion was observed inside the feed frame. The study also revealed that NIR spectra can help in the understanding of powder flow inside the feed frame. It was demonstrated that NIR spectra baselines can also be used to determine changes in mass inside the feed frame. The new NIR method showed that the paddle wheel speed has a significant impact in the powder dynamics inside the feed frame. The baselines of the NIR spectra depended on the mass hold-up inside the feed frame and paddle wheel speed. Studies using blends were performed to develop a NIR calibration model based on the feed frame system dynamics to determine acetaminophen drug concentration variability during the die filling process. The study found that variation in the distance from the powder to the probe due to paddle wheel speed has a significant effect on the NIR prediction. This study found that with NIR spectroscopy, blend uniformity can be assessed with high accuracy during the die filling process using the corresponding paddle wheel speed in-line calibration model. NIR was demonstrated to be a good development tool for the in-line monitoring of powder during the die filling process.

Classification of the waxy condition of durum wheat by near infrared reflectance spectroscopy using wavelets and a genetic algorithm

Near infrared (NIR) reflectance spectroscopy has been applied to the problem of differentiating four genotypes of durum wheat: ‘waxy’, Wx A1 null null, wx-B1 null and wild type. The test data consisted of 95 NIR reflectance spectra of wheat samples obtained from a USDA-ARS wheat breeding program. A two-step procedure for pattern recognition analysis of NIR spectral data was employed. First, the wavelet packet transform [14,15] was applied to the NIR reflectance data using wavelet filters at different scales to extract and separate low-frequency signal components from high frequency noise components. By applying these filters, each reflectance spectrum was decomposed into wavelet coefficients that represented the sample's constituent frequencies. Second, wavelet coefficients characteristic of the waxy condition of the wheat samples were identified using a genetic algorithm for pattern recognition. The pattern recognition GA employed both supervised and unsupervised learning to identify wavelet coefficients that optimized clustering of the spectra by genotype in a plot of the two largest principal components of the data. By sampling key feature subsets, scoring their PC plots, and tracking those genotypes and samples that were difficult to classify, the pattern recognition GA was able to identify a set of wavelet coefficients whose PC plot showed clustering of the wheat samples on the basis of their ‘waxy’ condition. Object validation was also performed to assess the predictive ability of the proposed NIR method to identify the ‘waxy’ condition of the wheat. An overall classification success rate of 78% was achieved for the spectral data.

Development of a comprehensive near infrared spectroscopy calibration model for rapid measurements of moisture content in multiple pharmaceutical products

Near infrared (NIR) spectroscopy has been widely used for the determination of water content in a wide variety of samples. With few exceptions, all methods employ a calibration model developed and applicable for a single product. The current study describes a NIR method using a single, comprehensive calibration model to predict the water content in tablets containing different active pharmaceutical ingredients (API). The calibration model was developed for water content range of 2–13% (w/w) using tablets containing three different APIs and different formulation compositions. To develop a robust comprehensive model, individual calibration models were sequentially developed starting from a simple model for one product to including tablets from all three projects in the final model using partial least square analysis method. Data pretreatments and spectral region selections were performed during the method development to optimize the number of factors and the correlation coefficients for cross-validation and prediction by the comprehensive model. The model reliably predicted the water content in tablet samples of these three products, and can be updated for water measurements of new drug products by adding to the model two samples of the new product for calibration purpose.

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 (Rc) and prediction (Rp). 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.

Advances in Instrumental Technology, Industry Guidance and Data Management Systems Enabling the Widespread Use of near Infrared Spectroscopy in the Pharmaceutical/Biopharmaceutical Sector

Smaller, faster and more robust instrumentation combined with improved regulatory and industry guidance and advanced data management systems have all contributed to making the near infrared (NIR) spectroscopy technique a viable alternative to traditional in-process and final-release testing strategies in the pharmaceutical/biopharmaceutical industry. The NIR method cannot be used to replace all testing methods, but when it can be applied, it provides greater insights into the state of a process or product. It also provides real- or near-real-time quality information, allowing process operators, Quality Assurance personnel or Advanced Process Control systems to take a proactive, rather than reactive, approach to quality during the manufacturing process. This review summarises the developments in instrumentation, regulatory/industry guidance and information management systems that better enable the implementation of NIR technology into routine laboratory and process applications. A short review comparing the NIR and Raman techniques is provided, outlining the complementary nature of the two methods along with a discussion on how the two methodologies have experienced a similar evolution over the past 14 years.

An Evaluation of Content Uniformity of Tablets Prepared by Quasi-Continuous Drug Coating Using near Infrared and Raman Spectroscopy

In recent years, there has been increased interest in continuous manufacturing to improve manufacturing efficiencies. The Supercell coater is an example of a quasi-continuous coater capable of integration as an in-line unit operation for handling sub-batches of tablets. The aim of this study was to evaluate the drug-content uniformity of tablets prepared by quasi-continuous drug coating using the Supercell coater. In addition, near infrared (NIR) and Raman spectroscopy were evaluated in parallel as process analytical technology tools for the monitoring of drug content in the coated tablets. Results showed that the Supercell coater was capable of coating tablets continuously with interbatch relative standard deviations of 2.2–4.9%, as measured by NIR spectroscopy and 1.4–5.0%, as measured by Raman spectroscopy. Both NIR and Raman spectroscopic methods were shown to be useful as non-destructive and rapid at-line monitoring tools to evaluate the drug-content uniformity of coated tablets. The spectra obtained from future exhaustive online measurements of tablets may be used as product fingerprints for real-time quality assurance.

A Process Analytical Technology (PAT) approach to control a new API manufacturing process: Development, validation and implementation

Pharmaceutical companies are progressively adopting and introducing Process Analytical Technology (PAT) and Quality-by-Design (QbD) concepts promoted by the regulatory agencies, aiming the building of the quality directly into the product by combining thorough scientific understanding and quality risk management. An analytical method based on near infrared (NIR) spectroscopy was developed as a PAT tool to control on-line an API (active pharmaceutical ingredient) manufacturing crystallization step during which the API and residual solvent contents need to be precisely determined to reach the predefined seeding point. An original methodology based on the QbD principles was designed to conduct the development and validation of the NIR method and to ensure that it is fitted for its intended use. On this basis, Partial least squares (PLS) models were developed and optimized using chemometrics methods. The method was fully validated according to the ICH Q2(R1) guideline and using the accuracy profile approach. The dosing ranges were evaluated to 9.0-12.0% w/w for the API and 0.18-1.50% w/w for the residual methanol. As by nature the variability of the sampling method and the reference method are included in the variability obtained for the NIR method during the validation phase, a real-time process monitoring exercise was performed to prove its fit for purpose. The implementation of this in-process control (IPC) method on the industrial plant from the launch of the new API synthesis process will enable automatic control of the final crystallization step in order to ensure a predefined quality level of the API. In addition, several valuable benefits are expected including reduction of the process time, suppression of a rather difficult sampling and tedious off-line analyses.

Rapid determination of sugar content in corn stover hydrolysates using near infrared spectroscopy

Near infrared (NIR) spectroscopy provides rapid determination method for biomass characterization. The objective of this study is to use NIR to model and predict contents of monosaccharides in biomass hydrolysates. A uniform distribution of pretreatment conditions was used to generate representative samples that cover wide ranges of sugar concentration for model development. The formation of glucose, xylose, and arabinose was studied via response surface methodology and 3D models were provided to show the effects of pretreatment conditions. The NIR models developed with partial least squares are able to provide excellent and good prediction for glucose and xylose concentration in biomass hydrolysates, respectively. Data transformation did not increase model performance, but the reduced wavelength range improved model prediction for all the sugar contents. The NIR method significantly reduced the time and cost of sugar determination.

Nondestructive determination of paper properties of Populus × euramericana with near infrared spectroscopy

Five Populus × euramericana wood samples representing three different sites were selected and near infrared (NIR) spectra were obtained. For these sections, basis weight, brightness and three mechanical properties (tensile index, tearing index and bursting index) were determined by standard analytical methods. Calibrations were developed for each paper property using the NIR spectra, data on paper properties, using partial least squares (PLS) regression. The results show that the coefficients of correlation of calibration and validation for basis weight were 0.8824 and 0.8299, respectively; the standard error of calibration (SEC) and prediction (SEP) were 1.150 and 1.170, respectively. In testing for brightness, the correlation coefficient of calibration was 0.9621 and for validation 0.9612, while the SEC and SEP were 0.997 and 1.300, respectively; paper brightness and NIR spectroscopy were highly correlated. NIR spectroscopy can be used to predict tensile, tearing and bursting indices of paper samples rapidly. We found that the paper properties fitted by NIR and GB methods were highly correlated. The coefficients of correlation of calibration and validation for basis weight exceeded 0.8000, while the SEC and SEP were very small. These results reveal that the five paper properties of Populus × euramericana and those predicted by the NIR model were highly correlated. We conclude that the NIR models can be used for the prediction of paper properties.

Infrared spectroscopic methods for the discrimination of cows' milk according to the feeding system, cow breed and altitude of the dairy farm

Bulk milk samples were collected from four French regions to study the potential capability of mid-infrared (MIR) and near-infrared (NIR) spectroscopy data to differentiate milk according to the feeding system, breed of cow and altitude of the farm. The MIR method demonstrated an excellent capability to distinguish milk from hay- and pasture-based systems and those from maize silage- and pasture-based systems. The MIR method did not exhibit the same capability concerning the discrimination of milk from hay- and maize silage-based systems. A similar trend was observed with the NIR method but with lower efficiency. The two infrared methods did not satisfactorily discriminate milk from different cow breeds. Significant differences (P < 0.05) between methods in the proportion of correctly classified samples according to the feeding system and breed were reported, whereas no significant differences were found between the methods concerning the discrimination of lowland versus upland samples.

A Comprehensive near Infrared Spectroscopic Study of the Limits of Quantitative Analysis of Sulfathiazole Polymorphism

A comprehensive study on the quantification of sulfathiazole polymorphs by near infrared (NIR) spectroscopy combined with multivariate analysis is reported. Calibration models were developed for each of the sulfathiazole polymorphs that can be obtained as polymorphically pure samples, sulfathiazole forms I, III and V, in binary mixtures in the 0–15% concentration range. Partial least squares regression (PLS) was employed and different spectral pre-treatment algorithms including, multiplicative scatter correction (MSC), standard normal variate (SNV) transformation, 1st derivative calculations and SNV combined with 1st derivative were applied to the data. The NIR method was compared with X-ray powder diffraction (XRPD) and was found to give more accurate results in all cases except one where XPRD was more accurate. For the NIR method, limits of detection were &lt;0.5% except for form V in l/V mixtures where the limit of detection was 1.5%. Limits of quantification ranged from 1.1% to 4.6%. When quantities of forms II and IV were added to samples which were analysed using the model developed for form III in I, their presence was detected by very large prediction errors. A comparison of multivariate and univariate analysis of the NIR data demonstrated that, in general, multivariate methods were superior.

Non‐destructive measurement of vitamin C, total polyphenol and sugar content in apples using near‐infrared spectroscopy

The vitamin C and polyphenol content of apples constitute quality and nutritional parameters of great interest for consumers, especially in terms of health. They are commonly measured using laborious reference methods. The purpose of this study was to evaluate the potential of near-infrared (NIR) spectroscopy as a rapid and non-destructive method to determine the sugar, vitamin C and total polyphenol content in apples. The quality parameters of more than 150 apple genotypes were analyzed using NIR and reference methods. The results obtained using the least squares support vector machine regression method showed good to very good prediction performance. Low standard error of prediction values, in addition to relatively high ratio to prediction (RPD) values, demonstrated the precision of the models, especially for polyphenol and sugar content. High RPD values (5.1 and 4.3 for polyphenol and sugar, respectively) indicated that an accurate classification of apples based on their content could be achieved. NIR spectroscopy coupled with the multivariate calibration technique could be used to accurately measure the quality parameters of apples. In addition, in the case of breeding programs, NIR spectroscopy can be considered an interesting tool for sorting varieties according to a range of concentrations.

Non-contact and Non-destructive Analysis of the Quality of Phenolic Resin by Near-Infrared Spectroscopy

Abstract The traditional analytical methods used to determine phenolic resin are slow and produce environmentally unfriendly waste. In this work, near-infrared (NIR) spectroscopy has been applied for resin content of phenolic resins. The partial least-squares regression was used to develop the calibration model of the resin content. Eight samples were withdrawn at different time stages for analysis with the proposed quantitative models; the data thus obtained were compared with those provided by reference methods. The maximal predicted error and the standard deviation of the differences were 0.96% and 0.51% for resin content. The results of the paired t test revealed that there was no significant difference between the NIR method and the reference method. NIR spectroscopy is an effective choice for the accurate, expeditious analyzing quality of phenolic resin.

Determination of organic carbon and nitrogen in particulate organic matter and particle size fractions of Brookston clay loam soil using infrared spectroscopy

The objective of this study was to determine whether models developed from infrared spectroscopy could be used to estimate organic carbon (C) content, total nitrogen (N) content and the C:N ratio in the particulate organic matter (POM) and particle size fraction samples of Brookston clay loam. The POM model was developed with 165 samples, and the particle size fraction models were developed using 221 samples. Soil organic C and total N contents in the POM and particle size fractions (sand, 2000–53 µm; silt, 53–2 µm; clay, &lt;2 µm) were determined by using dry combustion techniques. The bulk soil samples were scanned from 4000 to 400 cm−1 for mid‐infrared (MIR) spectra and from 8000 to 4000 cm−1 for near‐infrared (NIR) spectra. Partial least squares regression (PLSR) analysis and the ‘leave‐one‐out' cross‐validation procedure were used for the model calibration and validation. Organic C and N content and C:N ratio in the POM were well predicted with both MIR‐ and NIR‐PLSR models ( = 0.84–0.92; = 0.78–0.87). The predictions of organic C content in soil particle size fractions were also very good for the model calibration ( = 0.84–0.94 for MIR and = 0.86–0.92 for NIR) and model validation ( = 0.79–0.94 for MIR and = 0.84–0.91 for NIR). The prediction of MIR‐ and NIR‐PLSR models for the N content and the C:N ratio in the sand and clay fractions was also satisfactory ( = 0.73–0.88; = 0.67–0.85). However, the predictions for the N content and C:N ratio in the silt fraction were poor ( = 0.23–0.55; = 0.20–0.40). The results indicate that both MIR and NIR methods can be used as alternative methods for estimating organic C and total N in the POM and particle size fractions of soil samples. However, the NIR model is better for estimating organic C and N in POM and sand fractions than the MIR model, whereas the MIR model is superior to the NIR model for estimating organic C in silt and clay fractions and N in clay fractions.

Simultaneous quantification of polymeric and surface active stabilizers of nanosuspensions by using near-infrared spectroscopy

Nanosuspension technology is an attractive approach for the formulation and solubility enhancement of poorly water-soluble drug compounds. The technology requires adequate excipients for stabilizing the suspensions during nanogrinding and storage. This study aimed at establishing a near-infrared (NIR) method for assaying simultaneously the two nanoparticle stabilizers, sodium dodecyl sulphate (SDS) and hydroxypropylcellulose (HPC), in miconazole nanosuspensions. Second derivative of NIR signals was used to establish calibration curves in concentration ranges of interest of SDS (0.03–0.3%) and HPC (0.75–7.5%). The suitability and applicability of the NIR method was verified by evaluating the linearity, accuracy, precision, and specificity of the obtained data. The method was then used to quantify indirectly the amount of SDS and HPC adsorbed onto miconazole nanoparticles. Within the concentration range of interest, SDS adsorption increased up to 122 µg/m2 (4.2 × 10−7 mol/m2) with increasing SDS concentration, and HPC adsorption was in the range of 800–1000 µg/m2 (21–27 × 10−7 mol/m2) for nanosuspensions containing nominally 5% HPC and 12.5% or 20% miconazole. Interestingly, some of the adsorbed HPC was displaced upon increase of SDS concentration and adsorption. The data were also confirmed by surface tension measurements of aqueous solutions of SDS and HPC and nanosuspension supernatants. The availability of a fast and nondestructive method for quantifying simultaneously the adsorption of two stabilizers onto nanoground particles may not only speed up nanosuspension development, but also provide insight into the mechanisms of nanoparticle stabilization regarding competitive adsorption and electrostatic versus steric stabilization.

Analysis of rapid method for the quality index of phenolic resins and benefit

The traditional analytical methods used to determine phenolic resin are slow and produce environmentally unfriendly waste. In this work, Near-infrared (NIR) spectroscopy has been applied for resin content of phenolic resins. The partial least square regression was used to develop the calibration model of the resin content. 8 samples were withdrawn at different time stages for analysis with the proposed quantitative models; the data thus obtained were compared with those provided by reference methods. The maximal predicted error and the standard deviation of the differences were 0.96 and 0.51% for resin content. The results of the paired t-test revealed that there was no significant difference between the NIR method and the reference method. NIR spectroscopy is an effective choice for the accurate, expeditious analyzing quality of phenolic resin.

Complementary Near‐Infrared and Raman Chemical Imaging of Pharmaceutical Thin Films

Complementary near-infrared (NIR) and Raman mapping techniques have been used to study the distribution of drug particles suspended in a polymeric film. The film configuration was used with the goal of maintaining a drug with a particle size of less than 10 µm in a nonagglomerated form and to satisfy two commonly encountered pharmaceutical needs: enhanced dissolution rate of poorly soluble drugs and the content uniformity of drugs administered in low doses. A total of four film batches were prepared for this study using hydroxypropyl methylcellulose (HPMC) with griseofulvin as the active pharmaceutical ingredient. The NIR method analyzed a film area of 3 × 2.6 mm, whereas Raman mapping analyzed an area of 10 × 10 µm, and every sample was analyzed by the two methods. The second derivative transform removed baseline variations in the NIR spectra and provided differentiation between the two components. NIR chemical imaging did not identify clusters larger than 0.05 mm. Raman analysis identified areas rich in griseofulvin or HPMC, which were used to develop a partial least squares discriminant analysis method to determine drug or polymer distribution throughout the film.

Comparison of near infrared and microwave resonance sensors for at-line moisture determination in powders and tablets

In this paper we demonstrate the feasibility of replacing KF for water content testing in bulk powders and tablets with at-line near infrared (NIR) or microwave resonance (MR) methods. Accurate NIR and MR prediction models were developed with a minimalistic approach to calibration. The NIR method can accurately predict water content in bulk powders in the range of 0.5–5% w/w. Results from this method were compared to a MR method. We demonstrated excellent agreement of both NIR and MR methods for powders vs. the reference KF method. These methods are applicable to in-process control or quality control environments. One of the aims of this study was to determine if a calibration developed for a particular product could be used to predict the water content of another product (with related composition) but containing a different active pharmaceutical ingredient (API). We demonstrated that, contrary to the NIR method, a general MR method can be used to predict water content in two different types of blends. Finally, we demonstrated that a MR method can be developed for at-line moisture determination in tablets.

Determination of binary polymorphic mixtures of fluconazole using near infrared spectroscopy and X-ray powder diffraction: A comparative study based on the pre-validation stage results

The aim of the present study was to develop near infrared (NIR) and X-ray powder diffraction methods (XRPD) able to determine pure crystalline form II of fluconazole in a binary polymorphic mixtures containing forms II and III. In order to give a first performance estimation of both methods, these latters were pre-validated using accuracy profiles, a statistical approach based on β-expectation tolerance intervals. Both methods showed a good trueness, precision and accuracy and their β-expectation tolerance intervals were fully included within the acceptance limits. The comparative study was carried out using statistical analysis based on the work of Bland and Altman. A good agreement between the two methods was demonstrated indicating the interchangeability of NIR method with XRPD method.