New perspectives in the use of ink evidence in forensic science Cedric Neumann, Pierre Margot  Forensic Science International  Volume 185, Issue 1, Pages 38-50 (March 2009) DOI: 10.1016/j.forsciint.2008.12.008 Copyright © 2009 Elsevier Ireland Ltd Terms and Conditions

Fig. 1 representation of the scan of the elution track of an ink sample when the elution track is simultaneously scanned at 31 different wavelengths (x-axis: migration distance in mm; y-axis: wavelengths in nm; z-axis: absorption intensity in au). Forensic Science International 2009 185, 38-50DOI: (10.1016/j.forsciint.2008.12.008) Copyright © 2009 Elsevier Ireland Ltd Terms and Conditions

Fig. 2 Presentation of the concept of the calibration process. The new coordinate system (grid) is anchored on the three ladders on each plate. The new coordinates of the other elution tracks of the plate are computed from these three ladders, the base line and the solvent front (dots) by linear interpolation. Forensic Science International 2009 185, 38-50DOI: (10.1016/j.forsciint.2008.12.008) Copyright © 2009 Elsevier Ireland Ltd Terms and Conditions

Fig. 3 Schematic description of a possible strategy. All components from each sample are identified and the respective lists of components are compared. Forensic Science International 2009 185, 38-50DOI: (10.1016/j.forsciint.2008.12.008) Copyright © 2009 Elsevier Ireland Ltd Terms and Conditions

Fig. 4 Schematic description of a second possible strategy: The presence/absence of each component of the first sample in the second sample is recorded and reciprocally. Forensic Science International 2009 185, 38-50DOI: (10.1016/j.forsciint.2008.12.008) Copyright © 2009 Elsevier Ireland Ltd Terms and Conditions

Fig. 5 Schematic description of the third strategy: the two-dimentional matrices for both inks are compared mathematically. Forensic Science International 2009 185, 38-50DOI: (10.1016/j.forsciint.2008.12.008) Copyright © 2009 Elsevier Ireland Ltd Terms and Conditions

Fig. 6 Euclidean distance (algorithm A) on dataset 5 (samples from same ink on the same. plate) Forensic Science International 2009 185, 38-50DOI: (10.1016/j.forsciint.2008.12.008) Copyright © 2009 Elsevier Ireland Ltd Terms and Conditions

Fig. 7 Euclidean distance (algorithm A) on dataset 4 (samples from same ink on different. plates) Forensic Science International 2009 185, 38-50DOI: (10.1016/j.forsciint.2008.12.008) Copyright © 2009 Elsevier Ireland Ltd Terms and Conditions

Fig. 8 Euclidean distance (algorithm A) on the entire dataset. Forensic Science International 2009 185, 38-50DOI: (10.1016/j.forsciint.2008.12.008) Copyright © 2009 Elsevier Ireland Ltd Terms and Conditions

Fig. 9 Pearson correlation (algorithm B) on the entire dataset. Forensic Science International 2009 185, 38-50DOI: (10.1016/j.forsciint.2008.12.008) Copyright © 2009 Elsevier Ireland Ltd Terms and Conditions

Fig. 10 ANN (algorithm D—compilation along the migration distances) on the entire dataset. Forensic Science International 2009 185, 38-50DOI: (10.1016/j.forsciint.2008.12.008) Copyright © 2009 Elsevier Ireland Ltd Terms and Conditions

Fig. 11 ANN (algorithm F—compilation along the wavelengths) on the entire dataset. Forensic Science International 2009 185, 38-50DOI: (10.1016/j.forsciint.2008.12.008) Copyright © 2009 Elsevier Ireland Ltd Terms and Conditions

Fig. 12 Comparison between the performances of the six algorithms on all samples from all datasets together. The ANN are referenced by their letter in Table 2, by the number of neurones in each layer, as well as by whether they have been trained on fresh (F) or fresh and degraded (F&D) samples. Forensic Science International 2009 185, 38-50DOI: (10.1016/j.forsciint.2008.12.008) Copyright © 2009 Elsevier Ireland Ltd Terms and Conditions

Fig. 13 Comparison between the performances of the six algorithms on the samples from dataset 9 (exposure to sun). The ANN are referenced by their letter in Table 2, by the number of neurones in each layer, as well as by whether they have been trained on fresh (F) or fresh and degraded (F&D) samples. Forensic Science International 2009 185, 38-50DOI: (10.1016/j.forsciint.2008.12.008) Copyright © 2009 Elsevier Ireland Ltd Terms and Conditions

Fig. 14 Comparison between the performances of the six algorithms, when processing only fresh ink samples (datasets 1–5). The ANN are referenced by their letter in Table 2, by the number of neurones in each layer, as well as by whether they have been trained on fresh (F) or fresh and degraded (F&D) samples. Forensic Science International 2009 185, 38-50DOI: (10.1016/j.forsciint.2008.12.008) Copyright © 2009 Elsevier Ireland Ltd Terms and Conditions

Fig. 15 DET curves for all different conservation conditions for algorithms A (Euclidean distance). Forensic Science International 2009 185, 38-50DOI: (10.1016/j.forsciint.2008.12.008) Copyright © 2009 Elsevier Ireland Ltd Terms and Conditions

Fig. 16 DET curves for all different conservation conditions for algorithms B Pearson correlation). Forensic Science International 2009 185, 38-50DOI: (10.1016/j.forsciint.2008.12.008) Copyright © 2009 Elsevier Ireland Ltd Terms and Conditions

Fig. 17 DET curves for all different conservation conditions for algorithms C (ANN 16-8-2 trained on fresh samples). Forensic Science International 2009 185, 38-50DOI: (10.1016/j.forsciint.2008.12.008) Copyright © 2009 Elsevier Ireland Ltd Terms and Conditions

Fig. 18 DET curves for all different conservation conditions for algorithms D (ANN 16-8-2 trained on fresh and degraded samples). Forensic Science International 2009 185, 38-50DOI: (10.1016/j.forsciint.2008.12.008) Copyright © 2009 Elsevier Ireland Ltd Terms and Conditions

Fig. 19 DET curves for all different conservation conditions for algorithms E (ANN 32-16-8-2 trained on fresh samples). Forensic Science International 2009 185, 38-50DOI: (10.1016/j.forsciint.2008.12.008) Copyright © 2009 Elsevier Ireland Ltd Terms and Conditions

Fig. 20 DET curves for all different conservation conditions for algorithms F (ANN 32-16-8-2 trained on fresh and degraded samples). Forensic Science International 2009 185, 38-50DOI: (10.1016/j.forsciint.2008.12.008) Copyright © 2009 Elsevier Ireland Ltd Terms and Conditions