Title :
Calibration of a mini-spectrophotometer using a neural-network-based interpolator
Author :
Wisniewski, Marcin ; Morawski, Roman Z. ; Barwicz, Andrzej
Author_Institution :
Dept. of Electr. Eng., Quebec Univ., Trois-Rivieres, Que., Canada
Abstract :
The computer-based interpretation of spectrometric data, aimed at identification of the chemical composition of an analyzed substance, requires some information on the spectrophotometer, acquired during its calibration. This information is of particular importance if a low-cost, low-resolution mini-spectrophotometer is used for analysis since only a few data per spectral peak are then available for processing. Artificial neural networks seem to be a sufficiently flexible and numerically powerful tools for solving the related problem of data interpolation. In this paper, the applicability of two classes of neural networks for solving this problem, viz. RBF networks and Elman networks, is studied using synthetic spectrometric-type data
Keywords :
calibration; interpolation; mean square error methods; radial basis function networks; recurrent neural nets; spectrochemical analysis; spectrophotometers; spectroscopy computing; Elman networks; MSE; RBF networks; computer-based interpretation; data interpolation; feedback ANN; low-cost low-resolution spectrophotometer; mini-spectrophotometer calibration; neural-network-based interpolator; parameter estimation; spectrochemical data; Artificial neural networks; Biomedical engineering; Biomedical optical imaging; Calibration; Chemical analysis; Information analysis; Neural networks; Neurons; Spectroscopy; Transfer functions;
Conference_Titel :
Instrumentation and Measurement Technology Conference, 2001. IMTC 2001. Proceedings of the 18th IEEE
Conference_Location :
Budapest
Print_ISBN :
0-7803-6646-8
DOI :
10.1109/IMTC.2001.928797
