For manufacturing of medicaments, all ingredients must be reliably identified. Wet chemistry methods for identification of cellulose ethers, used by the Pharmacopoea Europea, is time consuming and expensive. To distinguish microcristalline and powdered cellulose, only unspecific sedimentation properties are used. However, applications as well as technological and pharmacokinetic properties of cellulose and various cellulose ethers are different.
NIR reflectance spectroscopy speeds up the identification of excipients. So this technique causes fewer delay in manufacturing processes. The discrimination of powdered and microcristalline celluloses as well as cellulose and cellulose ethers is made possible by factor analysis and soft independent modelling of class analogies (SIMCA). The classification was improved by spectral pretreatment multiplicative scatter correction (MSC), derivation and wavelength selection. The discrimination of powdered and microcristalline celluloses is statistically highly significant, so the identification can be done reliably. Cellulose ethers can be quickly identified by NIR spectroscopy, although a large number of samples of different manufacturers and physical properties, for example viscosity, were used. The only exception is the discrimination of methylcellulose and cellulose ethers containing methyl and hydroxyalkyl substituents, which show identical spectra. But even for those excipients, the wet chemistry expenses can be reduced to one test. The developed strategy for data evaluation is quite general in nature, hence it can be applied to other pharmaceutical powders, excipients and active components as well. 相似文献
Several varieties of blue ballpoint pen inks were analyzed by high performance liquid chromatography (HPLC) and infrared spectroscopy (IR). The chromatographic data extracted at four wavelengths (254, 279, 370 and 400 nm) was analyzed individually and at a combination of these wavelengths by the soft independent modeling of class analogies (SIMCA) technique using principal components analysis (PCA) to estimate the separation between the pen samples. Linear discriminant analysis (LDA) measured the probability with which an observation could be assigned to a pen class. The best resolution was obtained by HPLC using data from all four wavelengths together, differentiating 96.4% pen pairs successfully using PCA and 97.9% pen samples by LDA. PCA separated 60.7% of the pen pairs and LDA provided a correct classification of 62.5% of the pens analyzed by IR. The results of this study indicate that HPLC coupled with chemometrics provided a better discrimination of ballpoint pen inks compared to IR. The need to develop a suitable IR method for analysing blue ballpoint pen inks has been emphasized and it is hoped that the development of such a method would indeed provide a valuable tool for the non-destructive analysis of blue ballpoint pen ink samples for forensic purposes. 相似文献
Chemometric techniques have been used to compare two methods for fat extraction, namely focused microwave-assisted Soxhlet extraction (FMASE) and dynamic ultrasound-assisted extraction (DUAE), with the conventional Folch method, frequently used as reference. The data generated by a mid infrared spectrometer, after appropriate treatment, provide a simple and effective way for the detection of potential alterations of the fat obtained with the assistance of auxiliary energies, in this case, microwaves and ultrasounds. The results thus obtained are as compared with those from the Folch method, a mild extraction method with a view to finding faster alternatives for routine analysis. Moreover, classification of the samples between cookies and snacks based on extraction kinetics studies was possible, thus demonstrating the importance of these studies for the development of analytical methods. Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were used for both purposes, namely detection of alterations and classification of the samples as a function of their extraction kinetics, while K Nearest Neighbours (KNN) and Soft Independent Modelling of Class Analogy (SIMCA), based on PCA, models were generated in order both to predict the extraction kinetics of unknown samples, thus adjusting the extraction time as a function of the matrix, and find out explanation to the different extraction kinetics. 相似文献