Enhanced Characterization of Naproxen Formulation by Near Infrared Spectroscopy

Near infrared spectroscopy in combination with appropriate chemometric methods is an effective technique for quantitative analysis of parameters of interest for the pharmaceutical industry. In this study, the artificial neural network (ANN) was applied to monitor critical parameters (compression force, tablet hardness, mean particle size, and active pharmaceutical ingredient concentration of tablets) in the process of naproxen pharmaceutical preparation. The performance of ANN was compared to linear methods (partial least squares regression (PLS) and synergy interval partial squares (siPLS)). The ANN models for compression force, tablet hardness, mean particle size, and active pharmaceutical ingredient concentration of tablets yielded the low root mean square error of prediction (RMSEP) values of 0.936 KN, 0.302 kg, 4.49 mg, and 2.14 µm, respectively. The predictive ability of the PLS model was improved by siPLS with selection of spectral regions and the best performance among all calibration methods was showed by the nonlinear method (ANN). Effective models were built by using these approaches using near infrared spectroscopy.     

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.     

Measurement of the percentage volume particle size distribution of powdered microcrystalline cellulose using reflectance near-infrared spectroscopy

This is the first reported method for determining the percentage volume particle size distribution of a powder (microcrystalline cellulose) by near-infrared (NIR) reflectance spectroscopy. A total of 113 samples of powdered microcrystalline cellulose were used from six different commercially available grades, with different moisture contents (range: 0.9-4.8% m/m). NIR reflectance measurements of these samples were made in narrow soda glass vials. Reference particle size data for the samples were acquired by laser diffraction. The NIR data were then calibrated to measure particle size by partial least squares regression. The effects of a range of different NIR data pre-treatments on calibration and prediction precision were investigated. Overall, simple absorbance data were found to produce regression models with the best predictive ability (root mean square error of prediction = 0.90%). The method was also found to be insensitive to moisture content.     

Formulation design of an oral, fast-disintegrating dosage form containing taste-masked particles of famotidine

A fast-disintegrating dosage form has been developed as a user-friendly formulation that disintegrates in the mouth immediately. Patients can take it without water like a liquid formulation. In this study famotidine taste-masking technology was applied to the new fast-disintegrating tablet in an attempt to produce a novel, taste-masked, fast-disintegrating tablet. Partial granulation was found to be an effective and practical way to address content uniformity, however, oral disintegration time tended to become longer as content uniformity improved. The disintegration time was improved considerably by controlling ambient humidity during the compression process (>50% RH). Furthermore, since the new fast-disintegrating technology made it possible to use low compression force, there was no change in the structure or dissolution rate of the taste-masked particles after compression. Therefore, this system can produce a taste-masked fast-disintegrating tablet with satisfactory attributes.     

A partial least squares based spectrum normalization method for uncertainty reduction for laser-induced breakdown spectroscopy measurements

A bottleneck of the wide commercial application of laser-induced breakdown spectroscopy (LIBS) technology is its relatively high measurement uncertainty. A partial least squares (PLS) based normalization method was proposed to improve pulse-to-pulse measurement precision for LIBS based on our previous spectrum standardization method. The proposed model utilized multi-line spectral information of the measured element and characterized the signal fluctuations due to the variation of plasma characteristic parameters (plasma temperature, electron number density, and total number density) for signal uncertainty reduction. The model was validated by the application of copper concentration prediction in 29 brass alloy samples. The results demonstrated an improvement on both measurement precision and accuracy over the generally applied normalization as well as our previously proposed simplified spectrum standardization method. The average relative standard deviation (RSD), average of the standard error (error bar), the coefficient of determination (R²), the root-mean-square error of prediction (RMSEP), and average value of the maximum relative error (MRE) were 1.80%, 0.23%, 0.992, 1.30%, and 5.23%, respectively, while those for the generally applied spectral area normalization were 3.72%, 0.71%, 0.973, 1.98%, and 14.92%, respectively.     

Effect of the moisture content in aerosol on the spray performance of Stmerin D HFA preparations

Stmerin D, a pressurized metered dose inhaler (MDI) for treatment of asthma, contains CFCs (chlorofluorocarbons) as a propellant. For the CFC replacement study, two formulations were prepared using hydrofluoroalkanes (HFA-134a and HFA-227) and the effect of storage on the spray performance was investigated under accelerated stress conditions. Drug stability, moisture content and spray performances such as the emitted dose uniformity and aerodynamic particle size distribution were evaluated. Drug content did not change after 3 months storage at 40 degrees C/75% RH. However, the emitted dose uniformity varied and the respirable fraction (RF) was reduced remarkably. While stored at 40 degrees C/ambient for 3 months, no change was observed in either drug content or spray performances. This study clarified that the moisture content in the canister played an important role on the spray performance, and it changed not only the emitted dose uniformity but also the particle size distribution. Consequently, in order to improve the stability of the spray performance of aerosol prepared with HFAs, moisture permeation into the canister must be controlled.     

超声衰减谱测量电池浆料的粒度分布

电池浆料中颗粒状活性物质的粒度大小和分散均匀性对电池的内阻、 电压、 局部表面电流和总极化程度等性能有直接影响, 实现对其的在线实时测量对电池的质量控制具有重要意义. 基于电池浆料的高固含量、 高黏度和低透光性的特点, 本文利用超声衰减谱的方式测量了其粒度分布(PSD). 应用于电池浆料的粒度分布测量的最大难点是其利用超声衰减谱法预测粒度分布的模型需要难以获得的分散相和连续相的物性参数. 本文采用主成分分析(PCA)结合误差反向传播(BP)神经网络建立预测模型解决了超声衰减谱法的难点, 并引入遗传算法(GA)优化BP神经网络的初始阈值和权值. 通过以LiCoO₂为活性物质的电池浆料进行了验证, 结果表明, PCA-GA-BP神经网络能够有效对不同固含量电池浆料的粒度分布进行预测, 预测值与真实值的峰形重合度高, 峰高偏差小, 两者的均方误差为0.1358, 拟合度(R²)为0.9816, 说明超声衰减谱法可作为测量电池浆料粒度分布的重要方式.     

Infrared spectroscopy and outer product analysis for quantification of fat, nitrogen, and moisture of cocoa powder

The combination of the near infrared (NIR) and Fourier-transform infrared (FTIR) absorbance spectra (1100-2500 nm and 4000-600 cm⁻¹) of 100 cocoa powder samples was used to build calibration models for the determination of the content of fat, nitrogen, and moisture. The samples that comprised the dataset had an average composition of 13.51% of fat, 3.77% nitrogen, and 3.98% moisture. The fat content ranged from 2.42 to 22.00%, the nitrogen from 0.88 to 4.48%, and moisture from 1.60 to 7.80%. For NIR, the relative root mean square error of cross-validation (RMSECV) was 7.0% (R² = 0.96) for fat, 1.7% (R² = 0.98) for nitrogen, and 5.2% (R² = 0.94) for moisture. For FTIR, the relative RMSECV was 10.4% (R² = 0.94) for fat and 3.9% (R² = 0.95) for nitrogen. However, for moisture, it was not possible to build a calibration model with suitable predictability. The combination of the NIR and FTIR domains (data fusion) by outer product analysis PLS1 allowed to predict these parameters and to characterise frequencies in one domain based on the information of the other domain. This work allows to conclude that the second derivative of NIR is the recommended procedure to quantify fat, nitrogen, and moisture content in cocoa powders by infrared spectroscopy.     

Compositional analysis of Miscanthus giganteus by near infrared spectroscopy

Fourier transform near infrared spectroscopy was applied to ball-milled and dried whole plant Miscanthus × giganteus samples in combination with partial least square regression analysis for prediction of main constituents of the biomass. The developed models with 172 calibration samples had an R² in the range of 0.96–0.99. For the first time, the acetyl content was modeled for Miscanthus. An independent calibration set of 58 samples revealed a low root mean square error of prediction of 0.414 % for extractives, 0.485 % for glucan, 0.249 % for xylan, 0.061 % for arabinan, 0.050 % for acetyl, 0.198 % for Klason lignin, 0.226 % for total ash and 0.133 % for ash after extraction, an indication of a high level of accuracy. The results showed major improvement over previously reported models, which was attributed to the smaller particle size used. The models are a valuable tool for the fast monitoring of the composition of M. × giganteus in e.g. plant breeding studies.     

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.     

The Effect of Polydispersity on the Size of Colloidal Particles Determined by the Dynamic Light Scattering

An attempt is made to allow for a sample polydispersity while measuring particle size distribution by the dynamic light scattering. The scattering ability R(r) is calculated for monodisperse samples and samples with rectangular, bell-shaped, and triangular (increasing and decreasing) particle size distributions. It is shown that, when the processing program for the data of light bearing spectroscopy assumes a linear scale of particle sizes, the scattering ability, irrespective of the distribution pattern, is proportional to the sixth power of the particle size for the Rayleigh region and to the second power, for larger particles. However, when the scale of the half-width of the Rayleigh spectrum is linear, the pattern of the curve describing the particle scattering capability substantially changes upon passing from monodisperse to polydisperse samples. This curve can be approximated by a function R r ⁷ in the Rayleigh region and by a function R r ³ in the range of large (as compared with the wavelength) particles.     

Application of polyglycolized glycerides in protection of amorphous form of etoricoxib during compression

Polymorphic transition and stability problems during amorphous drug formulation are the major limiting factors in pharmaceutical technology. The purpose of the study was to evaluate the ability of polyglycolized glycerides (Gelucire) in protection of amorphous form of drug during compression and shelf life with lower proportion. Amorphous etoricoxib (AET) was prepared by spray drying technique. Tablets of AET and melt granules of AET (MG-AET) with Gelucire 50/13 were prepared. Tablets parameters like hardness, disintegration and content uniformity were evaluated. Tablets were evaluated immediately after compression and on storage for 3 months at ambient conditions to determine degree of transformation using X-ray powder diffraction (XRPD), differential scanning calorimetry (DSC) and dissolution profiles. Spray drying yielded the amorphous etoricoxib. Content uniformity in the tablet was in between 95 to 105%. Other parameters like disintegration and hardness were well within the limits. The results showed significant difference in the degree of crystallinity between AET tablet and MG-AET tablet. MG-AET tablet showed absence of crystallinity after 3 months storage. The reason could be formation of hydrogen bonding between the Gelucire and AET. Also Gelucire can be tableted very easily under low pressure and showed elastic recovery. Gelucire yielded a soft embedding during tableting, which prevented the polymorphic transformation. Polyglycolized glycerides (Gelucire 50/13) are able to protect amorphous etoricoxib during compression. As excipient required is low, it became possible to prepare tablet formulation as compared to other excipient like polyvinylpyrrolidon (PVP).     

Optimization of Walnut Oil Nanoemulsions Prepared Using Ultrasonic Emulsification: A Response Surface Method

Response surface methodology-central composite rotatable design (RSM-CCRD) was applied to determine effects of ultrasonic time (UT, 5–15 minutes), walnut oil (WO, 4–10% w/w) content, and concentration ratio of Span 80 to Tween 80 (K ₀, 0.55–0.80), on the some physicochemical characteristics of WO/water nanoemulsion including average particle size, Span and loss of antioxidant activity (LAA). Analysis of variance (ANOVA) showed that second-order polynomial models with high R ² (0.944–0.983) were well adjusted to predict response variables. The linear effect of UT was found to be most significant in all response surface models. The optimal conditions were: UT of 5.0 minutes, WO content of 7.35% w/w, and K ₀ of 0.8. Under these conditions, the average particle size, Span, and LAA were 356.08 nm, 0.548%, and 10.96. The adequacy of the models was confirmed by production this nanoemulsion under optimum values given by the model.     

The potential of field spectroscopy for the assessment of sediment properties in river floodplains

Investigations have shown that visible-near-infrared (VNIR) spectroscopy can accurately determine soil properties under laboratory conditions. In situ assessment of soil properties is of great benefit for several applications, as spectra can be acquired fast and almost continuously. The present study used partial least squares (PLS) regression to establish a relationship between soil reflectance spectra measured under field conditions and the organic matter and clay content of the soil. Spectra were acquired with a fieldspectrometer in a recently reconstructed floodplain along the river Rhine in The Netherlands. Several spectral pre-processing methods were employed to improve the performance and robustness of the models. Results indicate that, under varying surface conditions, field spectroscopy in combination with multivariate calibration does result in a qualitative relation for organic matter (R²=0.45) and clay content (R²=0.43) while under laboratory conditions more accurate results are obtained (R²=0.69 and 0.92, respectively). Soil moisture and vegetation cover had a negative influence on the prediction capabilities for both soil properties. Although the performance of the spectra measured in situ is not as accurate as physical analysis, the accuracy obtained is useful for rapid soil characterisation and remote sensing applications.     

Colloidal dispersions of tantalum trisulfide: syntheses and characteristics

Stable colloidal solutions with the TaS₃ content ≈ 1 mmol L^–1 were obtained by the ultrasonic treatment of TaS₃ in MeCN and DMSO. The detailed analysis of the particle size distribution in colloidal dispersions and their stability was carried out using data of atomic force microscopy and photon correlation spectroscopy for TaS₃/MeCN. The data of powder diffractometry and Raman spectroscopy show that the films prepared from colloidal dispersions of TaS₃ retain the crystal structure of the initial tantalum trisulfide.     

Polyethylene oxides

The aim of this study was to study tablet formation of polyethylene oxides (PEOs) with different molecular masses by means of 3D modeling and comparing the results to those of other more traditional techniques, such as Heckel analysis, analysis by the pressure- time function and energy analysis. The molecular masses ranged between 400,000 and 7,000,000 Da. Material properties, such as water content, particle size and morphology, and glass transition temperature were also studied. To complete this study, elastic recovery dependent on maximum relative density and time were determined. Furthermore, the crushing force of the tablets and their morphology were analyzed. The PEOs consist of smooth edged particles of irregular shape; the particle size is similar to one type of MCC, namely Avicel PH 200. The PEOs are much more ductile during compression than MCC. Elastic recovery after tableting is higher than that for tablets made from MCC and continues for some time after tableting. The crushing force of the resulting tablets is low. In conclusion, with regards to direct compression the PEOs do not appear to be useful as sole tableting excipients.     

Predicting the cell-wall compositions of <Emphasis Type="Italic">Pinus radiata</Emphasis> (radiata pine) wood using ATR and transmission FTIR spectroscopies

Because plant cell walls vary in their polysaccharide compositions and lignin contents, their monosaccharide compositions and lignin contents are often determined, but these analyses are time consuming and laborious. We therefore investigated Fourier transform infrared (FTIR) spectroscopy coupled with partial least squares (PLS) regression analysis as a way of rapidly predicting the monosaccharide compositions and lignin contents of the cell walls of compression wood (CW) and opposite wood (OW) of the gymnosperm Pinus radiata. The effects were investigated of sample moisture content (ambient or dry) and sample particle size (large particles, < 0.422 mm or small particles, < 0.178 mm) of milled wood on attenuated total reflectance (ATR) and transmission FTIR spectra, as well as the PLS-1 models and subsequent predictions. PLS-1 models were built using mixtures of CW and OW as the training set, to provide a linear range of monosaccharide compositions and lignin contents. Models were externally validated by predicting another set of wood mixtures before predicting CW and OW of a separate test set. Most of the monosaccharide amounts in the separate test set were best predicted by ATR spectroscopy of ambient large particles, achieving the lowest standard error values for the monosaccharides arabinose (0.36%), xylose (1.05%), galactose (1.79%), glucose (6.32%), and 4-O-methylglucuronic acid (0.20%). The results show the feasibility of using ATR spectroscopy of ambient large particles for the rapid prediction of monosaccharide compositions and lignin contents of plant cell walls.     

A comparative study of diesel analysis by FTIR, FTNIR and FT-Raman spectroscopy using PLS and artificial neural network analysis

Diesel properties determined by ASTM reference methods as cetane index, density, viscosity, distillation temperatures at 50% (T50) and 85% (T85) recovery, and the total sulfur content (%, w/w) were modeled by FTIR-ATR, FTNIR, and FT-Raman spectroscopy using partial last square regression (PLS) and artificial neural network (ANN) spectral analysis. In the PLS models, 45 diesel samples were used in the training group and the other 45 samples were used in the validation. In the ANN analysis a modular feedforward network was used. Sixty diesel samples were used in the neural network training and other 30 samples were used in the validation. Two different ATR configurations were compared in the FTIR, a conventional (ATR1) and an immersion (ATR2) cell. The ATR1 cell presented the best results, with smaller prediction errors (root mean square error of prediction, RMSEP). The comparison of the three PLS models (FTIR-ATR1, FTNIR, and FT-Raman) shows that reasonable values of R² and RMSEP were obtained by the FTIR-ATR1 and FTNIR models in the evaluation of density, viscosity, and T50. The PLS/FT-Raman models presented reasonable results only for the T50 property. None of the techniques was able to generate suitable PLS calibration models for the determination of sulfur content. The ANN/FT-Raman models presented the best performances, with all models presenting R²-values above 85% some of them with RMSEP values significantly smaller than those obtained with FTIR-ATR and FTNIR. The ANN/FT-Raman and ANN/FTIR-ATR1 models were able to estimate the total sulfur content of diesel with 0.01% (w/w) accuracy.     

Potential of visible-near infrared spectroscopy combined with chemometrics for analysis of some constituents of coffee and banana residues

Banana (stalk, leaf, rhizome, rachis and stem) and coffee (leaf and husks) residues are promising feedstock for fuel and chemical production. In this work we show the potential of near-infrared spectroscopy (NIR) and multivariate analysis to replace reference methods in the characterization of some constituents of coffee and banana residues. The evaluated parameters were Klason lignin (KL), acid soluble lignin (ASL), total lignin (TL), extractives, moisture, ash and acid insoluble residue (AIR) contents of 104 banana residues (B) and102 coffee (C) residues from Brazil. PLS models were built for banana (B), coffee (C) and pooled samples (B + C). The precision of NIR methodology was better (p < 0.05) than the reference method for almost all the parameters, being worse for moisture. With the exception of ash (B and C) and ASL (C) content, which was predicted poorly (R² < 0.80), the models for all the analytes exhibited R² > 0.80. The range error ratios varied from 4.5 to 16.0. Based on the results of external validation, the statistical tests and figures of merit, NIR spectroscopy proved to be useful for chemical prediction of banana and coffee residues and can be used as a faster and more economical alternative to the standard methodologies.     

Influence of different laser irradiation methods on determination of composition content in polypropylene/low-density polyethylene blends using Raman spectroscopy

Sample movement makes a difference to raw Raman spectra and determination of composition content using Raman spectroscopy. Therefore, it is necessary to have further studies in this aspect. In this paper, different laser irradiation methods were investigated for determination of composition content in polypropylene (PP)/low-density polyethylene (LDPE) blends using Raman spectroscopy. Raw Raman spectra of PP sample were firstly collected using different laser irradiation methods. It was shown that the relative standard deviations (RSD) of PP sample under circle irradiation were ten times bigger than that under point irradiation at the little sacrifice of signal-to-noise ratio (SNR). In other words, rotating (or moving) PP sample during Raman spectra collection could signally improve sample representation. Owing to this, in combined with partial least squares (PLS), Raman quantitative analysis of PP concentration in PP/LDPE blends were performed by different laser irradiation methods. The results validated that blend samples with rotation during Raman measurement led to lower prediction errors in prediction of PP concentration. The best root-mean-square error of prediction (RMSEP) and coefficient of determination (R²) that were obtained for PP were respectively 2.10% and 0.9884.