Blend uniformity end-point determination using near-infrared spectroscopy and multivariate calibration.

A multivariate calibration approach using near-infrared (NIR) spectroscopy for determining blend uniformity end-point of a pharmaceutical solid dosage form containing 29.4% (w/w) drug load with three major excipients (crospovidone, lactose, and microcrystalline cellulose) is presented. A set of 21 off-line, static calibration samples were used to develop a multivariate partial least-squares (PLS) calibration model for on-line predictions of the API content during the blending process. The concentrations of the API and the three major excipients were varied randomly to minimize correlations between the components. A micro-electrical-mechanical-system (MEMS) based NIR spectrometer was used for this study. To minimize spectral differences between the static and dynamic measurement modes, the acquired NIR spectra were preprocessed using standard normal variate (SNV) followed by second derivative Savitsky-Golay using 21 points. The performance of the off-line PLS calibration model were evaluated in real-time on 67 production scale (750L bin size) blend experiments conducted over 3 years. The real-time API-NIR (%) predictions of all batches ranged from 93.7% to 104.8% with standard deviation ranging from 0.5% to 1.8%. These results showed the attainment of blend homogeneity and were confirmed with content uniformity by HPLC of respective manufactured tablets values ranging from 95.4% to 101.3% with standard deviation ranging from 0.5% to 2.1%. Furthermore, the performance of the PLS calibration model was evaluated against off-target batches manufactured with high and low amounts of water during the granulation phase of production. This approach affects the particle size and hence blending. All the off-target batches exhibited API-NIR (%) predictions of 94.6% to 103.5% with standard deviation ranging from 0.7% to 1.9%. Using off-target data, a systematic approach was developed to determine blend uniformity end-point. This was confirmed with 3 production scale batches whereby the blend uniformity end-point was determined using the PLS calibration model. Subsequently, the uniformity was also ascertained with conventional thief sampling followed by HPLC analysis and content uniformity by HPLC of the manufactured tablets.

Spectral simulation protocol for extending the lifetime of near-infrared multivariate calibrations.

Multivariate calibration models based on synthetic single-beam near-infrared spectra are used to demonstrate the ability to maintain viable calibrations over extended time periods. Glucose is studied over the physiological concentration range of 1-30 mM in a buffered aqueous matrix containing varying levels of alanine, ascorbate, lactate, urea, and triacetin. By employing a set of 25 test samples measured 23 times over a period of 325 days, partial least-squares (PLS) calibrations based on synthetic spectra are observed to outperform conventional calibrations that use a set of 64 measured calibration samples. The key to the success of this approach is the use of a set of spectra of phosphate buffer collected on each prediction day to construct synthetic calibration spectra that are specific to that day. This allows the incorporation into the calibration model of nonanalyte spectral variance that is unique to a particular day. In this way, the adverse effects of instrumental drift or other sources of spectral variance on prediction performance can be minimized. Through the application of this methodology, values of the standard error of prediction (SEP) for glucose concentration are maintained to a range of 0.50-0.95 mM and an average of 0.68 mM over the 325 days of the experiment. These results are significantly better than those obtained with conventional models based on measured calibration samples. Over the same time period, a PLS model based on measured calibration spectra in absorbance units produced values of SEP that ranged from 0.41 to 2.02 mM and an average of 1.23 mM.

Real-time on-line blend uniformity monitoring using near-infrared reflectance spectrometry: a noninvasive off-line calibration approach.

A robust, noninvasive, real-time, on-line near-infrared (NIR) quantitative method is described for blend uniformity monitoring of a pharmaceutical solid dosage form containing 29.4% (w/w) drug load with three major excipients (crospovidone, lactose, and microcrystalline cellulose). A set of 21 off-line, static calibration samples were used to develop a multivariate partial least-squares (PLS) calibration model for on-line prediction of the API content during the blending process. The concentrations of the API and the three major excipients were varied randomly to minimize correlations between the components. A micro electrical-mechanical system (MEMS) based portable, battery operated NIR spectrometer was used for this study. To minimize spectral differences between the static and dynamic measurement modes, the acquired NIR spectra were preprocessed using standard normal variate (SNV) followed by second derivative Savitzky-Golay using 21 points. The performance of the off-line PLS calibration model were evaluated in real-time on 16 laboratory scale (30 L bin size) blend experiments conducted over 3 months. To challenge the robustness of the off-line calibration model, several blend experiments were conducted using a different bin size, faster revolution speed and variations in the potency of the API. Employing the PLS calibration model developed using the off-line calibration approach, the real-time API NIR (%) predictions for all experiments were all within 90-110%. These results were confirmed using the conventional thief sampling of the final blend followed by high performance liquid chromatography (HPLC) analysis. Further confirmation was established through content uniformity by HPLC of manufactured tablets. Finally, the optimized off-line PLS method was successfully transferred to a production site which involved using a secondary NIR instrument with a 15-fold scale-up in bin size from development.

Simulated radiance profiles for automating the interpretation of airborne passive multi-spectral infrared images.

Methodology is developed for simulating the radiance profiles acquired from airborne passive multispectral infrared imaging measurements of ground sources of volatile organic compounds (VOCs). The simulation model allows the superposition of pure-component laboratory spectra of VOCs onto spectral backgrounds that simulate those acquired during field measurements conducted with a downward-looking infrared line scanner mounted on an aircraft flying at an altitude of 2000-3000 ft (approximately 600-900 m). Wavelength selectivity in the line scanner is accomplished through the use of a multichannel Hg:Cd:Te detector with up to 16 integrated optical filters. These filters allow the detection of absorption and emission signatures of VOCs superimposed on the upwelling infrared background radiance within the instrumental field of view (FOV). By combining simulated radiance profiles containing analyte signatures with field-collected background signatures, supervised pattern recognition methods can be employed to train automated classifiers for use in detecting the signatures of VOCs during field measurements. The targeted application for this methodology is the use of the imaging system to detect releases of VOCs during emergency response scenarios. In the work described here, the simulation model is combined with piecewise linear discriminant analysis to build automated classifiers for detecting ethanol and methanol. Field data collected during controlled releases of ethanol, as well as during a methanol release from an industrial facility, are used to evaluate the methodology.

Content uniformity determination of pharmaceutical tablets using five near-infrared reflectance spectrometers: a process analytical technology (PAT) approach using robust multivariate calibration transfer algorithms.

Near-infrared calibration models were developed for the determination of content uniformity of pharmaceutical tablets containing 29.4% drug load for two dosage strengths (X and Y). Both dosage strengths have a circular geometry and the only difference is the size and weight. Strength X samples weigh approximately 425 mg with a diameter of 12 mm while strength Y samples, weigh approximately 1700 mg with a diameter of 20mm. Data used in this study were acquired from five NIR instruments manufactured by two different vendors. One of these spectrometers is a dispersive-based NIR system while the other four were Fourier transform (FT) based. The transferability of the optimized partial least-squares (PLS) calibration models developed on the primary instrument (A) located in a research facility was evaluated using spectral data acquired from secondary instruments B, C, D and E. Instruments B and E were located in the same research facility as spectrometer A while instruments C and D were located in a production facility 35 miles away. The same set of tablet samples were used to acquire spectral data from all instruments. This scenario mimics the conventional pharmaceutical technology transfer from research and development to production. Direct cross-instrument prediction without standardization was performed between the primary and each secondary instrument to evaluate the robustness of the primary instrument calibration model. For the strength Y samples, this approach was successful for data acquired on instruments B, C, and D producing root mean square error of prediction (RMSEP) of 1.05, 1.05, and 1.22%, respectively. However for instrument E data, this approach was not successful producing an RMSEP value of 3.40%. A similar deterioration was observed for the strength X samples, with RMSEP values of 2.78, 5.54, 3.40, and 5.78% corresponding to spectral data acquired on instruments B, C, D, and E, respectively. To minimize the effect of instrument variability, calibration transfer techniques such as piecewise direct standardization (PDS) and wavelet hybrid direct standardization (WHDS) were used. The PDS approach, the RMSEP values for strength X samples were lowered to 1.22, 1.12, 1.19, and 1.08% for instruments B, C, D, and E, respectively. Similar improvements were obtained using the WHDS approach with RMSEP values of 1.36, 1.42, 1.36, and 0.98% corresponding to instruments B, C, D, and E, respectively.

Quantitative determination of ethanol in heated plumes by passive Fourier transform infrared remote sensing measurements.

Quantitative calibration models are developed for passive Fourier transform infrared (FT-IR) remote sensing measurements of open-air-generated vapors of ethanol. These experiments serve as a feasibility study for the use of passive FT-IR measurements in quantitative determinations of industrial stack emissions. A controlled-temperature plume generator is used to produce plumes of known concentrations of pure ethanol and mixtures of ethanol and methanol. Analyte plumes are generated over the path-averaged concentration range of 20-300 ppm-m and stack temperatures of 125, 150, 175, and 200 degrees C. A novel experimental setup is employed in which an ambient temperature polyvinyl chloride backdrop is placed behind the emission stack and used as a target for the passive IR measurements. An emission FT-IR spectrometer with telescope entrance optics is then employed to view the generated plumes against the backdrop. Signal processing techniques based on signal averaging and bandpass digital filtering are applied to both interferogram and single-beam spectral data obtained from these measurements, and the resulting filtered signals are used as inputs into the generation of multivariate partial least-squares (PLS) calibration models. Successful calibration models are obtained with both interferogram and spectral data, and neither analysis requires the collection of separate IR background data. For a set of validation data collected on a different day from the calibration measurements, standard errors of prediction of 30.6 and 32.2 ppm-m ethanol are obtained for the PLS models based on interferogram and spectral data, respectively.

Remote detection of volatile organic compounds by passive multispectral infrared imaging measurements.

Automated pattern recognition methodology is described for the detection of signatures of volatile organic compounds from passive multispectral infrared imaging data collected from an aircraft platform. Data are acquired in an across-track scanning mode with a downward-looking line scanner based on 8 to 16 spectral channels in the 8-14 and 3-5 microm spectral ranges. Two controlled release experiments are performed in which plumes of ethanol are generated and detected from aircraft overflights at altitudes of 2200 to 2800 ft (671 to 853 m). In addition, a methanol release from a chemical manufacturing facility is monitored. Automated classifiers are developed by application of piecewise linear discriminant analysis to the calibrated, registered, and preprocessed radiance data acquired by the line scanner. Preprocessing steps evaluated include contrast enhancement, temperature-emissivity separation, feature selection, and feature extraction/noise reduction by the minimum noise fraction (MNF) transform. Successful classifiers are developed for both compounds and are tested with data not used in the classifier development. Separation of temperature and emissivity by use of the alpha residual calculation is found to reduce false positive detections to a negligible level, and the MNF transform is shown to enhance detection sensitivity.

Spectral simulation methodology for calibration transfer of near-infrared spectra.

A spectrum simulation method is described for use in the development and transfer of multivariate calibration models from near-infrared spectra. By use of previously measured molar absorptivities and solvent displacement factors, synthetic calibration spectra are computed using only background spectra collected with the spectrometer for which a calibration model is desired. The resulting synthetic calibration set is used with partial least squares regression to form the calibration model. This methodology is demonstrated for use in the analysis of physiological levels of glucose (1-30 mM) in an aqueous matrix containing variable levels of alanine, ascorbate, lactate, urea, and triacetin. Experimentally measured data from two different Fourier transform spectrometers with different noise levels and stabilities are used to evaluate the simulation method. With the more stable instrument (A), well-performing calibration models are obtained, producing a standard error of prediction (SEP) of 0.70 mM. With the less stable instrument (B), the calibration based solely on synthetic spectra is less successful, producing an SEP value of 1.58 mM. For cases in which the synthetic spectra do not describe enough spectral variance, an augmentation protocol is evaluated in which the synthetic calibration spectra are augmented with the spectra of a small number of experimentally measured calibration samples. For instruments A and B, respectively, augmentation with measured spectra of nine samples lowers the SEP values to 0.64 and 0.85 mM.