title: Combined wavelet transform-artificial neural network use in tablet active content determination by near-infrared spectroscopy.
abstract: The pharmaceutical industry faces increasing regulatory pressure to optimize quality control. Content uniformity is a basic release test for solid dosage forms. To accelerate test throughput and comply with the Food and Drug Administration's process analytical technology initiative, attention is increasingly turning to nondestructive spectroscopic techniques, notably near-infrared (NIR) spectroscopy (NIRS). However, validation of NIRS using requisite linearity and standard error of prediction (SEP) criteria remains a challenge. This study applied wavelet transformation of the NIR spectra of a commercial tablet to build a model using conventional partial least squares (PLS) regression and an artificial neural network (ANN). Wavelet coefficients in the PLS and ANN models reduced SEP by up to 60% compared to PLS models using mathematical spectra pretreatment. ANN modeling yielded high-linearity calibration and a correlation coefficient exceeding 0.996.
authors.affiliation: F. Hoffmann-La Roche AG, 65/516, 4070 Basel, Switzerland. pascal.chalus@roche.com
authors.forename: Pascal
authors.initials: P
authors.lastname: Chalus
authors.forename: Serge
authors.initials: S
authors.lastname: Walter
authors.forename: Michel
authors.initials: M
authors.lastname: Ulmschneider
mesh_terms: D016018:Least-Squares Analysis; D016571:Neural Networks, Computer; D004364:Pharmaceutical Preparations / Q000032:analysis*; D019265:Spectroscopy, Near-Infrared / Q000379:methods*; D013607:Tablets / Q000032:analysis*
chemical_list: D004364:Pharmaceutical Preparations; D013607:Tablets