1. New perspectives in the use of ink evidence in forensic science
Cedric Neumann, Pierre Margot 
Forensic Science International 
Volume 185, Issue 1, Pages (March 2009)
DOI: /j.forsciint
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2. 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).
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3. 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.
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4. Fig. 3
Schematic description of a possible strategy. All components from each sample are identified and the respective lists of components are compared.
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5. 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.
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6. Fig. 5
Schematic description of the third strategy: the two-dimentional matrices for both inks are compared mathematically.
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7. Fig. 6
Euclidean distance (algorithm A) on dataset 5 (samples from same ink on the same.
plate)
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8. Fig. 7
Euclidean distance (algorithm A) on dataset 4 (samples from same ink on different.
plates)
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9. Fig. 8 Euclidean distance (algorithm A) on the entire dataset.
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10. Fig. 9 Pearson correlation (algorithm B) on the entire dataset.
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11. Fig. 10
ANN (algorithm D—compilation along the migration distances) on the entire dataset.
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12. Fig. 11
ANN (algorithm F—compilation along the wavelengths) on the entire dataset.
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13. 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.
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14. 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.
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15. 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.
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16. Fig. 15
DET curves for all different conservation conditions for algorithms A (Euclidean distance).
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17. Fig. 16
DET curves for all different conservation conditions for algorithms B Pearson correlation).
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18. Fig. 17
DET curves for all different conservation conditions for algorithms C (ANN trained on fresh samples).
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19. Fig. 18
DET curves for all different conservation conditions for algorithms D (ANN trained on fresh and degraded samples).
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20. Fig. 19
DET curves for all different conservation conditions for algorithms E (ANN trained on fresh samples).
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21. Fig. 20
DET curves for all different conservation conditions for algorithms F (ANN trained on fresh and degraded samples).
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