Recovering independent components from shifted data using fast independent component analysis and swarm intelligence.
Frequency displacement, or spectral shift, is commonly observed in industrial spectral measurements. It can be caused by many factors such as sensor de-calibration or by external influences, which include changes in temperature. The presence of frequency displacement in spectral measurements can cause difficulties when statistical techniques, such as independent component analysis (ICA), are used to analyze it. Using simulated spectral measurements, this paper initially highlights the effect that frequency displacement has on ICA. A post-processing technique, employing particle swarm optimization (PSO), is then proposed that enables ICA to become robust to frequency displacement in spectral measurements. The capabilities of the proposed approach are illustrated using several simulated examples and using tablet data from a pharmaceutical application.
Control Systems Centre, School of Electrical and Electronic Engineering, University of Manchester, P.O. Box 88, Sackvile Street Building, Manchester, M60 1QD UK. Caleb.Rascon@postgrad.manchester.ac.uk
Caleb
C
Rascon
Barry
B
Lennox
Ognjen
O
Marjanovic
D000465:Algorithms; D015283:Citalopram / Q000737:chemistry; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D008962:Models, Theoretical; D004364:Pharmaceutical Preparations / Q000737:chemistry; D019265:Spectroscopy, Near-Infrared
D004364:Pharmaceutical Preparations; D015283:Citalopram