Standardisation of near-IR spectrometers using artificial neural networks

Autor:	Cyril Ruckebusch, Ludovic Duponchel, Pierre Legrand, J.P. Huvenne
Rok vydání:	1999
Předmět:	Inorganic Chemistry Chemometrics Artificial neural network Spectrometer Chemistry Calibration (statistics) Organic Chemistry Analytical chemistry Spectral response Algorithm Spectroscopy Analytical Chemistry
Zdroj:	Journal of Molecular Structure. :551-556
ISSN:	0022-2860
DOI:	10.1016/s0022-2860(98)00781-9
Popis:	In analytical chemistry, the use of chemometrics on near-infrared data undergoes a major problem. Large increase of error prediction is observed when calibration equation developed on a first instrument is directly used on another one. Since many spectral differences between two spectrometers can occur, standardisation procedure has rapidly became a necessary step for a long-dated use of quantitative or qualitative models. An original neural network approach is proposed to correct spectral differences by modelling spectral response of an instrument from second one before using calibration equations. In this way, the consuming time recalibration step of the second spectrometer was avoided and initial error prediction level was retrieved.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::1b63a071fc68b21994779b6f1055555e https://doi.org/10.1016/s0022-2860(98)00781-9 Zobrazit plný text záznamu Full Text from ScienceDirect