Combined wavelet transform–artificial neural network use in tablet active content determination by near-infrared spectroscopy
| Autor: | Serge Walter, Michel Ulmschneider, Pascal Chalus |
|---|---|
| Rok vydání: | 2007 |
| Předmět: |
Spectroscopy
Near-Infrared Artificial neural network Correlation coefficient business.industry Chemistry Process analytical technology Near-infrared spectroscopy Analytical chemistry Wavelet transform Pattern recognition Biochemistry Analytical Chemistry Wavelet Pharmaceutical Preparations Partial least squares regression Calibration Environmental Chemistry Neural Networks Computer Artificial intelligence Least-Squares Analysis business Spectroscopy Tablets |
| Zdroj: | Analytica Chimica Acta. 591:219-224 |
| ISSN: | 0003-2670 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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