Scatter correction of transmission near-infrared spectra by photon migration data: quantitative analysis of solids

The scope of this work is a new methodology to correct conventional near-infrared (NIR) data for scattering effects. The technique aims at measuring the absorption coefficient of the samples rather than the total attenuation measured in conventional NIR spectroscopy. The main advantage of this is that the absorption coefficient is independent of the path length of the light inside the sample and therefore independent of the scattering effects. The method is based on time-resolved spectroscopy and modeling of light transport by diffusion theory. This provides an independent measure of the scattering properties of the samples and therefore of the path length of light. This yields a clear advantage over other preprocessing techniques, where scattering effects are estimated and corrected for by using the shape of the measured spectrum only. Partial least squares (PLS) calibration models show that, by using the proposed evaluation scheme, the predictive ability is improved by 50% as compared to a model based on conventional NIR data alone. The method also makes it possible to predict the concentration of active substance in samples with other physical properties than the samples included in the calibration model.     

Development and validation of NIR-chemometric methods for chemical and pharmaceutical characterization of meloxicam tablets

Context: Near-Infrared (NIR) spectroscopy is an important component of a Process Analytical Technology (PAT) toolbox and is a key technology for enabling the rapid analysis of pharmaceutical tablets.

Objective: The aim of this research work was to develop and validate NIR-chemometric methods not only for the determination of active pharmaceutical ingredients content but also pharmaceutical properties (crushing strength, disintegration time) of meloxicam tablets.

Materials and methods: The development of the method for active content assay was performed on samples corresponding to 80%, 90%, 100%, 110% and 120% of meloxicam content and the development of the methods for pharmaceutical characterization was performed on samples prepared at seven different compression forces (ranging from 7 to 45?kN) using NIR transmission spectra of intact tablets and PLS as a regression method.

Results: The results show that the developed methods have good trueness, precision and accuracy and are appropriate for direct active content assay in tablets (ranging from 12 to 18 mg/tablet) and also for predicting crushing strength and disintegration time of intact meloxicam tablets.

Discussion: The comparative data show that the proposed methods are in good agreement with the reference methods currently used for the characterization of meloxicam tablets (HPLC-UV methods for the assay and European Pharmacopeia methods for determining the crushing strength and disintegration time).

Conclusion: The results show the possibility to predict both chemical properties (active content) and physical/pharmaceutical properties (crushing strength and disintegration time) directly, without any sample preparation, from the same NIR transmission spectrum of meloxicam tablets.     

Quantitative transmission Raman spectroscopy of pharmaceutical tablets and capsules

Quantitative analysis of pharmaceutical formulations using the new approach of transmission Raman spectroscopy has been investigated. For comparison, measurements were also made in conventional backscatter mode. The experimental setup consisted of a Raman probe-based spectrometer with 785 nm excitation for measurements in backscatter mode. In transmission mode the same system was used to detect the Raman scattered light, while an external diode laser of the same type was used as excitation source. Quantitative partial least squares models were developed for both measurement modes. The results for tablets show that the prediction error for an independent test set was lower for the transmission measurements with a relative root mean square error of about 2.2% as compared with 2.9% for the backscatter mode. Furthermore, the models were simpler in the transmission case, for which only a single partial least squares (PLS) component was required to explain the variation. The main reason for the improvement using the transmission mode is a more representative sampling of the tablets compared with the backscatter mode. Capsules containing mixtures of pharmaceutical powders were also assessed by transmission only. The quantitative results for the capsules' contents were good, with a prediction error of 3.6% w/w for an independent test set. The advantage of transmission Raman over backscatter Raman spectroscopy has been demonstrated for quantitative analysis of pharmaceutical formulations, and the prospects for reliable, lean calibrations for pharmaceutical analysis is discussed.     

Pharmaceutical quantification with univariate analysis using transmission Raman spectroscopy

The purpose of this study was to investigate the quantification performance of transmission Raman spectroscopy with univariate analysis. Model dosage forms containing acetaminophen and an excipient, lactose monohydrate, were prepared. The Raman spectra of the tablets were obtained using the modes of transmission, backscattering micro-spectroscopy, and wide area illumination. Calibration curves for quantification of acetaminophen in the tablets were created using peak heights of the Raman spectra. Of the three modes of measurement, the quantitative results by transmission had the highest correlation with those by conventional UV–vis methods. In the validation of quantification by the transmission mode with univariate analysis, a certain degree of daily variation was confirmed. Additionally, quantitative results using peak heights were compared with those of partial least squares (PLSs) multivariate analysis. The root mean square error of prediction (RMSEP) suggested that quantification using PLS provided better precision than the peak height method as expected. However, content uniformity test using large sample sizes by the Raman spectra is not required to be very highly predictive because they usually employ non-parametric criteria and include wide specification ranges. Therefore, univariate analysis using transmission Raman spectroscopy was a suitable quantitative method for conducting content uniformity tests of large sample sizes.     

Prediction of Drug Content and Hardness of Intact Tablets Using Artificial Neural Network and Near-Infrared Spectroscopy

The purpose of this study was to predict drug content and hardness of intact tablets using artificial neural networks (ANN) and near-infrared spectroscopy (NIRS). Tablets for the drug content study were compressed from mixtures of Avicel® PH-101, 0.5% magnesium stearate, and varying concentrations (0%, 1%, 2%, 5%, 10%, 20%, and 40% w/w) of theophylline. Tablets for the hardness study were compressed from mixtures of Avicel PH-101 and 0.5% magnesium stearate at varying compression forces ranging from 0.4 to 1 ton. An Intact Analyzer™ was used to obtain near infrared spectra from the tablets with varying drug contents, whereas a Rapid Content Analyzer™ (RCA) was used to obtain spectral data from the tablets with varying hardness. Two sets of tablets from each batch (i.e., tablets with varying drug content and hardness) were randomly selected. One set of tablets was used to generate appropriate calibration models, while the other set was used as the unknown (test) set. A total of 10 ANN calibration models (5 each with 10 and 160 inputs at appropriate wavelengths) and five separate 4-factor partial least squares (PLS) calibration models were generated to predict drug contents of the test tablets from the spectral data. For the prediction of tablet hardness, two ANN calibration models (one each with 10 and 160 inputs) and two 4-factor PLS calibration models were generated and used to predict the hardness of test tablets. The PLS calibration models were generated using Vision® software. Prediction of drug contents of test tablets using the ANN calibration models generated with 10 inputs was significantly better than the prediction obtained with the ANN calibration models with 160 inputs. For tablets with low drug concentrations (less than or equal to 2%w/w), prediction of drug content was better with either of the two ANN calibration models than with the PLS calibration models. However, prediction of drug contents of tablets with greater than or equal to 5% w/w drug was better with the PLS calibration models than with the ANN calibration models. Prediction of tablet hardness was better with the ANN calibration models generated with either 10 or 160 inputs than with the PLS calibration models. This work demonstrated that a well-trained ANN model is a powerful alternative technique for analysis of NIRS data. Moreover, the technique could be used in instances when the conventional modeling of data does not work adequately.     

Comparison of the determination of a low-concentration active ingredient in pharmaceutical tablets by backscatter and transmission Raman spectrometry

A total of 383 tablets of a pharmaceutical product were analyzed by backscatter and transmission Raman spectrometry to determine the concentration of an active pharmaceutical ingredient (API), chlorpheniramine maleate, at the 2% m/m (4 mg) level. As the exact composition of the tablets was unknown, external calibration samples were prepared from chlorpheniramine maleate and microcrystalline cellulose (Avicel) of different particle size. The API peak at 1594 cm(-1) in the second derivative Raman spectra was used to generate linear calibration models. The API concentration predicted using backscatter Raman measurements was relatively insensitive to the particle size of Avicel. With transmission, however, particle size effects were greater and accurate prediction of the API content was only possible when the photon propagation properties of the calibration and sample tablets were matched. Good agreement was obtained with HPLC analysis when matched calibration tablets were used for both modes. When the calibration and sample tablets are not chemically matched, spectral normalization based on calculation of relative intensities cannot be used to reduce the effects of differences in physical properties. The main conclusion is that although better for whole tablet analysis, transmission Raman is more sensitive to differences in the photon propagation properties of the calibration and sample tablets.     

Rapid prediction of past climate condition from lake sediments by near-infrared (NIR) spectroscopy

This study explored the feasibility of rapid, nondestructive near-infrared (NIR) reflection spectroscopy for the prediction of conventional physical properties, carbon-nitrogen-sulfur (CNS) analysis, and concentration of inorganic components in sediment cores from a brackish lake. A long core sample, which consisted of well-preserved annually formed lamina from Lake Ogawara along the Pacific coast in Aomori Prefecture, northeastern Japan, was used to investigate the past environmental record. The core was previously analyzed for physical properties, CNS, and inorganic components. Calibration models were developed from NIR reflection spectra of 149 core samples. Partial least squares (PLS) analysis provided good regression models between measured and predicted values for water content, total nitrogen (TN), total organic carbon (TOC), total sulfur (TS), Al(2)O(3), S/Al(2)O(3), Fe(2)O(3)/Al(2)O(3), Sc/Al(2)O(3), Cu/Al(2)O(3), and Zn/Al(2)O(3) with coefficients of determination (r(2)) for cross-validation of 0.73, 0.89, 0.88, 0.73, 0.92, 0.81, 0.82, 0.75, 0.82, and 0.82, respectively. The variation of predicted component values as a function of depth showed the same trend as that of conventionally measured values. This study also showed the possibility of NIR spectroscopy as an on-site, rapid analytical tool for the identification of tephra (fragmental material produced by a volcanic eruption regardless of composition, fragment size, or emplacement mechanism), which is important for dating.     

Monitoring of the manufacturing process for ambroxol hydrochloride tablet using NIR-chemometric methods: compression effect on content uniformity model and relevant process parameters testing

This study aimed at using near-infrared (NIR) spectroscopy to monitor compaction pressure for simultaneously determining the tensile strength and content uniformity, as well as moisture and mean particle size of ambroxol hydrochloride tablets. The content uniformity, compression force and tensile strength of the laboratory samples were obtained by pressing a mixture of active principle and excipient components into tablets. To reduce the spectral baseline shift of the laboratory samples, the compaction pressure applied to the mixture was assessed by a variable pressure test. Production samples were added to the test and subjected to principal component analysis. The expanded partial least-squares (PLS) calibration model used to quantify the active content was more accurate than the model constructed from laboratory samples using the production tablets included in the calibration set. The model showed good predictability, with correlation coefficient (R) 0.9977. The validation and reliability of the content model were evaluated to determine trueness and reliability for the measurement of individual production tablets and the laboratory tablets with drug content ranging from 24 to 36?mg. The PLS calibration models for compression force and tensile strength were constructed using the same spectral set assuming both were highly related. These models yielded high R values (0.9955 and 0.9910). The R values of the moisture and mean particle size were 0.9994 and 0.9919, respectively. This study demonstrated that NIR spectroscopy combined with chemometric techniques can be successfully used to quantitatively monitor the tablet manufacturing process in the pharmaceutical industry.     

Quantitative nondestructive methods for the determination of ticlopidine in tablets using reflectance near-infrared and Fourier transform Raman spectroscopy

Two different nondestructive spectroscopy methods based on near-infrared (NIR) and Fourier transform (FT) Raman spectroscopy were developed for the determination of ticlopidine-hydrochloride (TCL) in pharmaceutical formulations and the results were compared to those obtained by high-performance liquid chromatography (HPLC). An NIR assay was performed by reflectance over the 850-1700 nm region using a partial least squares (PLS) prediction model, while the absolute FT-Raman intensity of TCL's most intense vibration was used for constructing the calibration curve. For both methodologies the spectra were obtained from the as-received film-coated tablets of TCL. The two quantitative techniques were built using five "manual compressed" tablets containing different concentrations and validated by evaluating the calibration model as well as the accuracy and precision. The models were applied to commercial preparations (Ticlid). The results were compared to those obtained from the application of HPLC using the methodology described by "Sanofi Research Department" and were found to be in excellent agreement, proving that NIR, using fiber-optic probes, and FT-Raman spectroscopy can be used for the fast and reliable determination of the major component in pharmaceutical analysis.     

Advanced calibration strategy for in situ quantitative monitoring of phase transition processes in suspensions using FT-Raman spectroscopy

There is an increasing interest in using Raman spectroscopy to identify polymorphic forms and monitor phase changes in pharmaceutical products for quality control. Compared with other analytical techniques for the identification of polymorphs such as X-ray powder diffractometry and infrared spectroscopy, FT-Raman spectroscopy has the advantages of enabling fast, in situ, and nondestructive measurements of complex systems such as suspension samples. However, for suspension samples, Raman intensities depend on the analyte concentrations as well as the particle size, overall solid content, and homogeneity of the solid phase in the mixtures, which makes quantitative Raman analysis rather difficult. In this contribution, an advanced model has been derived to explicitly account for the confounding effects of a sample's physical properties on Raman intensities. On the basis of this model, a unique calibration strategy called multiplicative effects correction (MEC) was proposed to separate the Raman contributions due to changes in analyte concentration from those caused by the multiplicative confounding effects of the sample's physical properties. MEC has been applied to predict the anhydrate concentrations from in situ FT-Raman measurements made during the crystallization and phase transition processes of citric acid in water. The experimental results show that MEC can effectively correct for the confounding effects of the particle size and overall solid content of the solid phase on Raman intensities and, therefore, provide much more accurate in situ quantitative predictions of anhydrate concentration during crystallization and phase transition processes than traditional PLS calibration methods.     

Chemometric determination of blood parameters using visible-near-infrared spectra

Visible and near-infrared (NIR) integrating sphere spectroscopy and chemometric multivariate linear regression were applied to determine hematocrit (HCT) and oxygen saturation (SatO2) of circulating human blood. Diffuse transmission, total transmission, and diffuse reflectance were measured and the partial least squares method (PLS) was used for calibration considering different wavelength ranges and selected optical measurement parameters. HCT and SatO2 were changed independently. Each parameter was adjusted to different levels and four designs with blood from different donors were carried out for the calibration with PLS. The calibration included the changes in hemolysis as well as inter-individual differences in cell dimensions and hemoglobin content. At a sample thickness of 0.1 mm the HCT and SatO2 were predicted with a root mean square error (PRMSE) of 1.4% and 2.5%, respectively, using transmission and reflectance spectra and the full Vis-NIR range. Using only diffuse NIR reflectance spectroscopy and a sample thickness of 1 mm, HCT and SatO2 could be predicted with a PRMSE of 1.9% and 2.8%, respectively. Prediction of hemolysis was also possible for one blood sample with a PRMSE of 0.8% and keeping HCT and SatO2 stable with a PRMSE of 0.03%.     

Evaluation of multiblock NIR/MIR PLS predictive models to detect adulteration of diesel/biodiesel blends by vegetal oil

This work investigates the potential of using simultaneously near infrared (NIR) and mid infrared (MIR) spectroscopy for the quantification of vegetable oil in diesel/biodiesel blends. The two spectral ranges were used separately with PLS regressions. To combine the two pieces of information, first a concatenated matrix was used and then H-PLS and S-PLS models were constructed. The models were compared in term of prediction errors (RMSEP). The results obtained in NIR spectroscopy were better than the ones obtained with MIR spectroscopy (considering the influence of the pretreatment and of the selected variables range). The multiblock methodology seems to be of great interest in quantitative analysis with the simultaneous use of information from the MIR and NIR spectroscopy.     

Visible and near-infrared calibrations for quality assessment of fresh phase I and II mushroom (Agaricus bisporus) compost

Previous studies have shown that visible and near-infrared spectra (Vis-NIR) of dry and milled compost can be used for generating partial least squares (PLS) calibrations of phase II compost parameters including ammonia, nitrogen dry matter (NDM), dry matter (DM), pH, conductivity, carbon, microbial population, and potential productivity. The objective of this study was to develop robust calibrations for some of the key parameters from the spectra of fresh phase I and II composts. Samples of substrates from six commercial production yards were obtained during winter and summer months of 2000-2004 to monitor changes in quality and were analyzed for the test factors. Vis-NIR reflectance measurements of fresh samples (740) were made over the range of 400-2500 nm. After mathematical pretreatments, PLS calibrations of the key parameters were developed using the NIR (1100-2500 nm) and visible and NIR (400-2500 nm) regions and subsequently validated using an independent sample set of 123 phase I and II samples obtained during 2004-2005. The phase I and II standard errors of laboratory measurements of ammonia, pH, conductivity, DM, NDM, and ash were lower than the standard error of predictions of the same parameters, respectively, by the best NIR or Vis-NIR models. The degree of precision for some of the calibrations, especially ammonia, NDM, and DM, is suitable for composters to monitor changes in quality parameters during production. The laboratory measurement errors for phase I samples were greater than those of the phase II samples, except for ash, due to a higher degree of heterogeneity in the substrate. The calibrations, especially for pH, conductivity, and ash, need to be improved with new sample sets. A major advantage of NIR spectroscopy is the ability to assess substrate quality for a range of target parameters simultaneously, within a few hours of receiving the samples. The main drawbacks are the expensive instrumentation, expertise, and training necessary for operating the spectrometer and a dedicated chemometrician required for maintaining the equations compared to the reference methods.     

Vibrational spectroscopic and ultrasound analysis for in-process characterization of high-density polyethylene/polypropylene blends during melt extrusion

Spectroscopic techniques such as Raman, mid-infrared (MIR), and near-infrared (NIR) have become indispensable analytical tools for rapid chemical quality control and process monitoring. This paper presents the application of in-line Fourier transform near-infrared (FT-NIR) spectroscopy, Raman spectroscopy, and ultrasound transit time measurements for in-line monitoring of the composition of a series of high-density polyethylene (HDPE)/polypropylene (PP) blends during single-screw extrusion. Melt composition was determined by employing univariate analysis of the ultrasound transit time data and partial least squares (PLS) multivariate analysis of the data from both spectroscopic techniques. Each analytical technique was determined to be highly sensitive to changes in melt composition, allowing accurate prediction of blend content to within +/- 1% w/w (1sigma) during monitoring under fixed extrusion conditions. FT-NIR was determined to be the most sensitive of the three techniques to changes in melt composition. A four-factor PLS model of the NIR blend spectra allowed determination of melt content with a standard prediction error of +/- 0.30% w/w (1sigma). However, the NIR transmission probes employed for analysis were invasive into the melt stream, whereas the single probes adopted for Raman and ultrasound analysis were noninvasive, making these two techniques more versatile. All three measurement techniques were robust to the high temperatures and pressures experienced during melt extrusion, demonstrating each system's suitability for process monitoring and control.     

A variable selection method based on uninformative variable elimination for multivariate calibration of near-infrared spectra

Variable (or wavelength) selection plays an important role in the quantitative analysis of near-infrared (NIR) spectra. A modified method of uninformative variable elimination (UVE) was proposed for variable selection in NIR spectral modeling based on the principle of Monte Carlo (MC) and UVE. The method builds a large number of models with randomly selected calibration samples at first, and then each variable is evaluated with a stability of the corresponding coefficients in these models. Variables with poor stability are known as uninformative variable and eliminated. The performance of the proposed method is compared with UVE-PLS and conventional PLS for modeling the NIR data sets of tobacco samples. Results show that the proposed method is able to select important wavelengths from the NIR spectra, and makes the prediction more robust and accurate in quantitative analysis. Furthermore, if wavelet compression is combined with the method, more parsimonious and efficient model can be obtained.     

Quantitative analysis of cotton-polyester textile blends from near-infrared spectra

Quantitative analysis of textile blends and textile fabrics is currently of particular interest in the industrial context. In this frame, this work investigates whether the use of Fourier transform (FT) near-infrared (NIR) spectroscopy and chemometrics is powerful for rapid and accurate quantitative analysis of cotton-polyester content in blend products. As samples of the same composition have many sources of variability that affect NIR spectra, indirect prediction is particularly challenging and a large sample population is required to design robust calibration models. Thus, a total of more than three-hundred cotton-polyester samples were selected covering the range from the 0% to 100% cotton and the corresponding NIR reflectance spectra were measured on raw fabrics. The data set obtained was used to develop multivariate models for quantitative prediction from reference measurements. A successful approach was found to rely on partial least squares (PLS) regression combined with genetic algorithms (GAs) for wavelength selection. It involved evaluating a set of calibration models considering different spectral regions. The results obtained considering 27.5% of the original variables yielded a prediction error (RMSEP) of 2.3 in percent cotton content. It demonstrates that FT-NIR spectroscopy has the potential to be used in the textile industry for the prediction of the composition of cotton-polyester blends. As a further consequence, it was observed that the spectral preprocessing and the complexity of the model are simplified compared to the full-spectrum approach. Also, the relevancy of the spectral intervals retained after variable selection can be discussed.     

Estimation of wood stiffness and strength properties of hybrid larch by near-infrared spectroscopy

This work was undertaken to investigate the feasibility of near-infrared (NIR) spectroscopy for estimating wood mechanical properties, i.e., modulus of elasticity (MOE) and modulus of rupture (MOR) in bending tests. Two sample sets having large and limited density variation were prepared to examine the effects of wood density on estimation of MOE and MOR by the NIR technique. Partial least squares (PLS) analysis was employed and it was found that the relationships between laboratory-measured and NIR-predicted values were good in the case of sample sets having large density variation. MOE could be estimated even when density variation in the sample set was limited. It was concluded that absorption bands due to the OH group in the semi-crystalline or crystalline regions of cellulose strongly influenced the calibrations for bending stiffness of hybrid larch. This was also suggested from the result that both alpha-cellulose content and cellulose crystallinity showed moderate positive correlation to wood stiffness.     

Comparison of near-infrared and laser-induced breakdown spectroscopy for determination of magnesium stearate in pharmaceutical powders and solid dosage forms

Near-infrared (NIR) spectroscopy has become well established in both the pharmaceutical arena and other areas as a useful technique for rapid quantitative analysis of solid materials. Though laser-induced breakdown spectroscopy (LIBS) has not been widely applied in the pharmaceutical industry, the technique has been used for rapid quantitative analysis of solids in many other applications. One analysis amenable to each technique is the determination of magnesium stearate in solids during the lubrication blending unit operation of pharmaceutical processing. A comparative study of the utility of these two techniques for this application will be presented. Necessary sample preparations and the extent and type of matrix effects will be discussed. Additionally, it will be shown that NIR provides better accuracy and precision than LIBS with the experimental parameters used; however, LIBS showed superior selectivity as it was demonstrated to be more robust to sample matrix perturbations. Examples of blending applications will also be presented.     

Investigation of electrolyte measurement in diluted whole blood using spectroscopic and chemometric methods

The feasibility of using near-infrared (NIR) spectroscopy in combination with partial least-squares (PLS) regression was explored to measure electrolyte concentration in whole blood samples. Spectra were collected from diluted blood samples containing randomized, clinically relevant concentrations of Na+, K+, and Ca2+. Sodium was also studied in lysed blood. Reference measurements were made from the same samples using a standard clinical chemistry instrument. Partial least squares (PLS) was used to develop calibration models for each ion with acceptable results (Na+, R2 = 0.86, CVSEP = 9.5 mmol/L; K+, R2 = 0.54, CVSEP = 1.4 mmol/L; Ca2+, R2 = 0.56, CVSEP = 0.18 mmol/L). Slightly improved results were obtained using a narrower wavelength region (470-925 nm) where hemoglobin, but not water, absorbed indicating that ionic interaction with hemoglobin is as effective as water in causing measurable spectral variation. Good models were also achieved for sodium in lysed blood, illustrating that cell swelling, which is correlated with sodium concentration, is not required for calibration model development.