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Pharmaceutical applications of vibrational chemical imaging and chemometrics: a review
Authors:Gendrin C  Roggo Y  Collet C
Institution:F. Hoffmann-La Roche A.G., Basel, Switzerland. christelle.gendrin@roche.com
Abstract:The emergence of chemical imaging (CI) has gifted spectroscopy an additional dimension. Chemical imaging systems complement chemical identification by acquiring spatially located spectra that enable visualization of chemical compound distributions. Such techniques are highly relevant to pharmaceutics in that the distribution of excipients and active pharmaceutical ingredient informs not only a product's behavior during manufacture but also its physical attributes (dissolution properties, stability, etc.). The rapid image acquisition made possible by the emergence of focal plane array detectors, combined with publication of the Food and Drug Administration guidelines for process analytical technology in 2001, has heightened interest in the pharmaceutical applications of CI, notably as a tool for enhancing drug quality and understanding process. Papers on the pharmaceutical applications of CI have been appearing in steadily increasing numbers since 2000. The aim of the present paper is to give an overview of infrared, near-infrared and Raman imaging in pharmaceutics. Sections 2 and 3 deal with the theory, device set-ups, mode of acquisition and processing techniques used to extract information of interest. Section 4 addresses the pharmaceutical applications.
Keywords:AH  agglomerative hierarchical  ANN  artificial neural network  API  active pharmaceutical ingredient  ASA  acetylsalicylic acid  ATM  atomic force microscopy  ATR  attenuated total reflection  BSS  blind source separation  BTEM  band target entropy minimization  CCA  cosine correlation analysis  CCD  charge coupled detectors  CI  chemical imaging  CLS  classical least squares  csiFCM  cluster size insensitive fuzzy-C mean  2D/3D  two-/three-dimensional  DA  discriminant analysis  DAC  directed agglomeration clustering  DCLS  direct classical least squares  DESI-MS  desorption electrospray ionization linear ion trap mass spectrometry  D2O  deuterium oxide  DR  diffuse reflection  EMSC  extended multiplicative signal correction  EVA  ethylene vinyl acetate  FCM  fuzzy-C mean  FNNLS  fast non-negative least squares  FPA  focal plane array  FFT  fast Fourier transform  FIR  far-infrared  FOV  field of view  FT  Fourier transform  FTIR  Fourier transform infrared  HPLC  high performance liquid chromatography  HPMC  hydroxypropylmethylcellulose  ICA  independent component analysis  IR  infrared  ITTFA  iterative target transformation factor analysis  KM  K-means  KSFA  key-set factor analysis  LCTF  liquid crystal tunable filter  LDA  linear discriminant analysis  LLS  laser light scattering  LUT  look-up table  MBCD  methyl-β-cyclodextrin  MCR-ALS  multivariate curve resolution-alternating least squares  MCT  mercury cadmium telluride  MIA  multivariate image analysis  MIR  mid-infrared  MLF-ANN  multilayer feed-forward-artificial neural network  MLP-ANN  multilayer perception-artificial neural network  MNF  maximum noise fraction  MSC  multiplicative scatter correction  NA  numerical aperture  NIR  near-infrared  NMF  non-negative matrix factorization  OLS  ordinary least squares  OPA  orthogonal projection analysis  PARAFAC:  parallel factor  PAT  process analytical technology  PBS  phosphate buffered saline  PCA  principal component analysis  PDMS  polydimethylsiloxane  PEG  polyethylene glycol  PEO  polyethylene oxide  PLGA  poly(lactic-co-glycolic acid)  PLS  partial least squares  PMF  positive matrix factorization  RGB  red–green–blue  ROI  regions of interest  SA  salicylic acid  S  D    standard deviation  SIM  spectral identity mapping  SIMCA  soft independent modeling of class analogies  SIMPLISMA  simple-to-use interactive self-modeling mixture analysis  SMCR  self-modeling curve resolution  SNR  signal to noise ratio  SNV  standard normal variate  SVM  support vector machine  UV  ultraviolet  WEFA  window evolving factor analysis
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