Standardisation of near Infrared Spectrometers Using Artificial Neural Networks

Abstract

The use of chemometrics procedures with near infrared spectroscopic data to produce calibration equations for analytical chemistry has been very successful. A large increase in prediction error is observed when the calibration equation developed on one instrument is used directly on another. Since many spectral differences can exist between two spectrometers, a standardisation procedure is a requirement for the long-term use of quantitative or qualitative models. In this work, an original neural network approach is proposed in order to correct for spectral differences. Spectral response of a given instrument is modelled from another before the use of the calibration equations. In this way, the time-consuming step of recalibration for the second spectrometer is avoided and the initial error prediction level is retrieved.

Keywords

standardisation calibration transfer neural networks experimental design

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