1. Combined target factor analysis and Bayesian soft-classification of interference- contaminated samples: Forensic Fire Debris Analysis 
Mary R. Williams, Michael E. Sigman, Jennifer Lewis, Kelly McHugh Pitan 
Forensic Science International 
Volume 222, Issue 1, Pages (October 2012)
DOI: /j.forsciint
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2. Fig. 1
Total ion spectra of ignitable liquids used in simulation studies: (a) Gas, (b) ISO, (c) MPD. See Table 1 for sample details.
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3. Fig. 2
Total ion spectra for pyrolysis products from substrates 1–5, labeled as (a)–(e) respectively. See Table 1 for sample detail.
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4. Fig. 3
Total ion spectra for pyrolysis products from substrates 6–10, labeled as (a)–(e) respectively. See Table 1 for sample detail.
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5. Fig. 4
Five representative spectra corresponding to S1–S5 with 1–5% A1 contribution shown as (a)–(e) and (f) shows the screen plot for the abstract factor analysis.
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6. Fig. 5
Eigenvectors (i.e., abstract factors) 1–6 from the factor analysis of six factor data set composed of 1–15% A1 and 90–94% S1–S5 (see Table 1).
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7. Fig. 6
(a) Regression (r=0.621, n=171) of a 90% evaporated Gas test spectrum against the predicted spectrum when retaining six eigenvectors, (b) regression (r=0.930, n=171) of a fresh Gas test spectrum against the predicted spectrum when retaining six eigenvectors.
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8. Fig. 7
(a) Kernel probability distribution functions calculated by Eqs. (6)–(8) (hGas=0.035, hISO=0.013 and hMPD=0.007) from the distributions of r for Gas (solid line), MPD (long dash) and ISO (short dash), (b) posterior probability calculated by Eq. (9) as a function of rLL.
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9. Fig. 8
Total ion chromatograms are shown from: (a) gasoline, (b) asphalt roofing shingle pyrolysis, and (c) 95% shingle pyrolysis and 5% gasoline. The asterisk (*) symbols in panel (c) indicate positions where there is a noticeable difference between the two TIC shown panels (b) and (c).
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10. Fig. 9
(a) Kernel probability distribution functions calculated by Eqs. (6)–(8) (hGas=0.037, hISO=0.009 and hMPD=0.007) from the distributions of r for Gas (solid line), MPD (long dash) and ISO (short dash), (b) posterior probability calculated by Eq. (9) as a function of rLL.
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11. Fig. 10
Kernel probability density functions for Gas (solid line), MPD (long dash) and ISO (short dash) obtained from the analysis of a dataset containing A1 in (a) 5%, (b) 30%, (c) 50% and (d) 70% contributions. Each of the 10 spectra also contained one of the substrates S1–S10 and 5% noise.
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12. Fig. 11
Optimization of correct classification percentage as a function of rLL for 20 laboratory test burns, see Table 2.
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13. Fig. 12
(a) An example container prior to burning, (b) a representative amount of damage sustained by most of the containers, and (c) sample collection markers shown in a container sustaining a high degree of damage from the burn.
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14. Fig. 13
(a) Schematic showing the layout of large-scale burn containers with the positions of thermocouples indicated as T1–T4. (b) Temperature recorded by thermocouples T1–T4 during a 6-min burn.
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15. Fig. 14
Class-conditional kernel probability distributions obtained from large scale burn of Container 1. The ordering of curves in each panel is solid, short dash and long dash: (a) Gas, HAR and HPD; (b) ISO, LAR and LPD; (c) MAR, MPD and NA; (d) NP and OXY.
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