A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies |
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| Authors: | Roggo Yves Chalus Pascal Maurer Lene Lema-Martinez Carmen Edmond Aurélie Jent Nadine |
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| Affiliation: | F. Hoffmann-La Roche Ltd., Basel, Switzerland. yves.roggo@roche.com |
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| Abstract: | Near-infrared spectroscopy (NIRS) is a fast and non-destructive analytical method. Associated with chemometrics, it becomes a powerful tool for the pharmaceutical industry. Indeed, NIRS is suitable for analysis of solid, liquid and biotechnological pharmaceutical forms. Moreover, NIRS can be implemented during pharmaceutical development, in production for process monitoring or in quality control laboratories.This review focuses on chemometric techniques and pharmaceutical NIRS applications. The following topics are covered: qualitative analyses, quantitative methods and on-line applications. Theoretical and practical aspects are described with pharmaceutical examples of NIRS applications. |
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| Keywords: | ANN, artificial neural networks API, active pharmaceutical ingredient ATR, attenuated total reflectance CC, correlation coefficient CFR, code of federal regulations DBSCAN, density based spatial clustering of applications with noise DS, direct standardization DSC, differential scanning calorimetry EMEA, European agency for the evaluation of medicinal products FDA, food and drug administration FT-IR, Fourier transform infrared spectroscopy FT-NIR, Fourier transform near infrared spectroscopy GC, gas chromatography GMP, good manufacturing practice HPLC, high performance liquid chromatography ICH, international conference on harmonization KF, Karl Fischer KNN, K nearest neighbours LDA, linear discriminant analysis LOD, loss on drying LVQ, learning vector quantization MLR, multi-linear regression MPE, mean percent error MSC, multiplicative scatter correction NIR, near infrared NIRS, near infrared spectroscopy NMR, nuclear magnetic resonance OSC, orthogonal signal correction PASG, pharmaceutical analytical sciences group PC, principal component PCA, principal component analysis PCR, principal component regression PDS, piecewise direct standardization PLS, partial least squares regression PLS-DA, partial least squares discriminant analysis PNN, probabilistic neural network QDA, quadratic discriminant analysis RMSEC, root mean square error of calibration RMSECV, root mean square error of cross-validation RMSEP, root mean square error of prediction RNA, ribonucleic acid SEC, standard error of calibration SECV, standard error of cross-validation SEP, standard error of prediction SIMCA, soft independent modelling of class analogy SNV, standard normal variate SVM, support vector machines SVR, support vector regression SW, Shenk and Westerhaus method TG, thermogravimetry XRD, X-ray powder diffractometry |
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