Literature DB >> 22179490

Non-destructive dating of fiber-based gelatin silver prints using near-infrared spectroscopy and multivariate analysis.

Ana Martins1, Lee Ann Daffner, Ann Fenech, Christopher McGlinchey, Matija Strlič.   

Abstract

An innovative approach to date fiber-based gelatin silver prints using near-infrared spectroscopy (NIR) and multivariate analysis is presented. NIR spectra were acquired for 152 film stills printed in the USA between 1914 and 1986, and partial least square (PLS) analysis was used to correlate the spectra with the year the photographs were printed. Principal component analysis and spectral interpretation helped clarify the underlying correlation between the print date and the composition and ageing of the photographic papers. The method was successfully validated with an independent set of 66 film stills printed in the USA, and a prediction error (root mean square error of prediction) of 6 years was achieved. The method was also tested on films stills printed in Germany and Russia, as well as amateur prints and photographs in the collection of the Museum of Modern Art. The prediction error was significantly larger, with the exception of the amateur prints, due to differences in the composition and/or properties of the papers depending on their geographical origin and purpose as confirmed by discriminant analysis.

Entities:  

Year:  2011        PMID: 22179490     DOI: 10.1007/s00216-011-5566-2

Source DB:  PubMed          Journal:  Anal Bioanal Chem        ISSN: 1618-2642            Impact factor:   4.142


  3 in total

Review 1.  Recent developments in using the molecular decay dating method: a review.

Authors:  Johannes Tintner
Journal:  Ann N Y Acad Sci       Date:  2021-01-14       Impact factor: 5.691

Review 2.  Recent Advances in Counterfeit Art, Document, Photo, Hologram, and Currency Detection Using Hyperspectral Imaging.

Authors:  Shuan-Yu Huang; Arvind Mukundan; Yu-Ming Tsao; Youngjo Kim; Fen-Chi Lin; Hsiang-Chen Wang
Journal:  Sensors (Basel)       Date:  2022-09-26       Impact factor: 3.847

3.  Machine learning-assisted non-destructive plasticizer identification and quantification in historical PVC objects based on IR spectroscopy.

Authors:  Tjaša Rijavec; David Ribar; Jernej Markelj; Matija Strlič; Irena Kralj Cigić
Journal:  Sci Rep       Date:  2022-03-23       Impact factor: 4.379

  3 in total

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