Use of genetic algorithms with multivariate regression for determination of gelatine in historic papers based on FT-IR and NIR spectral data

TALANTA

卷:82

Article

Csefalvayova, L.¹  Pelikan, M.²  Cigic, I. Kralj³  Kolar, J.⁴  Strlic, M.¹ 

[1] UCL, Ctr Sustainable Heritage, London WC1E 6BT, England

[2] Univ Missouri, Missouri Estimat Distribut Algorithms Lab, St Louis, MO 63121 USA

[3] Univ Ljubljana, Fac Chem & Chem Technol, Ljubljana 61000, Slovenia

[4] Morana RTD Doo, Ivancna Gorica, Slovenia

关键词: Gelatine;    Genetic algorithm;    Near-infrared spectroscopy;    Historic paper;   

DOI  :  10.1016/j.talanta.2010.07.062

来源: Elsevier

【 摘 要 】

Quantitative non-destructive analysis of individual constituents of historic rag paper is crucial for its effective preservation. In this work, we examine the potentials of mid- and near-infrared spectroscopy, however, in order to fully utilise the selectivity inherent to spectroscopic multivariate measurements, genetic algorithms were used to select spectral data derived from information-rich FT-IR or UV-vis-NIR measurements to build multivariate calibration models based on partial least squares regression, relating spectra to gelatine content in paper. A selective but laborious chromatographic method for the quantification of hydroxyproline (HYP) has been developed to provide the reference data on gelatine content. We used 9-fluorenylmethyl chloroformate (FMOC) to derivatise HYP, which was subsequently determined using reverse-phase liquid chromatographic separation and fluorimetric detection. In this process, the sample is consumed, which is why the method can only be used as a reference method. The sampling flexibility afforded by small-size field-portable spectroscopic instrumentation combined with chemometric data analysis, represents an attractive addition to existing analytical techniques for cultural heritage materials. (C) 2010 Elsevier B.V. All rights reserved.

【 授权许可】

Free