Elucidating the Variability of Magnesium Stearate and the Correlations With Its Spectroscopic Features

The objective of this study is to investigate the variability in physiochemical and spectral properties of commercially available vegetable-grade magnesium stearate (MgSt) samples and to assess the correlation between physiochemical properties and near-infrared and Raman spectroscopic features to determine if fast spectral measurements could be used for physical and chemical evaluation. Thirteen MgSt samples of 9 manufacturer grades were obtained from 3 suppliers. The chemical composition was examined using gas chromatography and loss on drying. The physical characteristics were examined on 3 levels: solid state, particle, and bulk level. Comparing the largest to the smallest test values of 13 samples, the variation of the properties ranged from 7% to 335%, with majority of them varying by more than 100% of the smallest value. The samples could be categorized into 4 groups based on solid state properties (1) monohydrate, (2) dihydrate, (3) mixture of monohydrate and dihydrate, and (4) anhydrous form. Scanning electron microscopy images revealed 2 morphological types: thin, flat, and plate-like crystal habit versus irregular crystal habit. The overall variability was mapped using Principal Component Analysis. The greatest variation was due to different manufacturers and perhaps manufacturing methods and starting materials. Based on correlations to physiochemical properties of MgSt, near-infrared and Raman spectra showed potential as a rapid technique for evaluating the differences in excipient properties.

Application of Multivariate Strategies to the Classification of Pharmaceutical Excipient Manufacturers Based on Near-Infrared (NIR) Spectra

Using partial least square discriminate analysis (PLSDA), we studied the spectroscopic differences between the commonly used filler-binder microcrystalline cellulose (MCC) from five manufactures. These samples had subtle differences in the chemical and physical properties, which are often the cause of differences in excipient performance. Studying these differences allowed us to build and validate a model to classify five manufacturers of MCC using near-infrared (NIR) spectra. The sample training set includes 39 MCC samples collected from five manufactures with regions spanning the United States of America, Japan, Taiwan, Germany, and Brazil. The samples from individual manufacturers include diverse grades that differ in moisture content, particle size, and bulk density. Optimized pretreatment methods were identified as standard normal variate normalization, followed by Savitzky-Golay second derivative, mean centering, and orthogonal signal correction. The model was optimized with cross-validation and validated with an independent sample set comprising nine samples collected from those five manufacturers. The results showed that none of the samples in the independent validation set was misclassified. The score and loading plots revealed that the differences in content of oxidized cellulose group, water content and states, hydrogen bonding, and degree of polymerization of the MCC samples are responsible for the class differentiation. Permutation test demonstrated that the outcome of the PLSDA model was significantly different from that of the randomly generated model. The advantages and limitations of the method in this type of application were discussed.

Characterization of near-infrared spectral variance in the authentication of skim and nonfat dry milk powder collection using ANOVA-PCA, pooled-ANOVA, and partial least-squares regression

Forty-one samples of skim milk powder (SMP) and nonfat dry milk (NFDM) from 8 suppliers, 13 production sites, and 3 processing temperatures were analyzed by NIR diffuse reflectance spectrometry over a period of 3 days. NIR reflectance spectra (1700-2500 nm) were converted to pseudoabsorbance and examined using (a) analysis of variance-principal component analysis (ANOVA-PCA), (b) pooled-ANOVA based on data submatrices, and (c) partial least-squares regression (PLSR) coupled with pooled-ANOVA. ANOVA-PCA score plots showed clear separation of the samples with respect to milk class (SMP or NFDM), day of analysis, production site, processing temperature, and individual samples. Pooled-ANOVA provided statistical levels of significance for the separation of the averages, some of which were many orders of magnitude below 10⁻³. PLSR showed that the correlation with Certificate of Analysis (COA) concentrations varied from a weak coefficient of determination (R²) of 0.32 for moisture to moderate R² values of 0.61 for fat and 0.78 for protein for this multinational study. In this study, pooled-ANOVA was applied for the first time to PLS modeling and demonstrated that even though the calibration models may not be precise, the contribution of the protein peaks in the NIR spectra accounted for the largest proportion of the variation despite the inherent imprecision of the COA values.

Exploring authentic skim and nonfat dry milk powder variance for the development of nontargeted adulterant detection methods using near-infrared spectroscopy and chemometrics

A multinational collaborative team led by the U.S. Pharmacopeial Convention is currently investigating the potential of near-infrared (NIR) spectroscopy for nontargeted detection of adulterants in skim and nonfat dry milk powder. The development of a compendial method is challenged by the range of authentic or nonadulterated milk powders available worldwide. This paper investigates the sources of variance in 41 authentic bovine skim and nonfat milk powders as detected by NIR diffuse reflectance spectroscopy and chemometrics. Exploratory analysis by principal component analysis and varimax factor rotation revealed significant variance in authentic samples and highlighted outliers from a single manufacturer. Spectral preprocessing and outlier removal methods reduced ambient and measurement sources of variance, most likely linked to changes in moisture together with sampling, preparation, and presentation factors. Results indicate that significant chemical variance exists in different skim and nonfat milk powders that will likely affect the performance of adulterant detection methods by NIR spectroscopy.