Automated Analysis of Fire Debris Samples using MIDI Sherlock GC-MS

Martin Perkins

19th July 2016

Arson, Fire Debris, Gary Jackoway, GC-MS, GC/MS, MIDI Inc, MIDI Sherlock GC-MS,

The Field

Arson accounts for significant financial losses each year, as well as substantial loss of life. According to The Arson Prevention Forum, property damage in England due to arson in 2008 amounted to £543 million.

When a fire occurs in a structure, fire debris samples are collected and analysed for the presence of ignitable liquids that may indicate an intentional act. These samples are extracted using passive headspace and then run on a Gas Chromatograph-Mass Spectrometer (GC-MS) system to separate and detect the hydrocarbons present.

The Challenge

The traditional approach to analysing hydrocarbons from a fire debris sample requires a highly-skilled analyst to manually identify each peak, using both the retention time information from the GC and the spectral information from the MS. Due to time limitations, an analyst will typically evaluate between ten and twenty different compounds in one chromatogram, though dozens of less abundant compounds may be present. Once the peaks of interest are named, the overall profile is used to determine whether an ignitable liquid is present and, if so, which one.

Because fire investigations often lead to court cases, an objective, automatic technique is extremely desirable. MIDI, Inc.’s Sherlock™ software automates complete compound naming of fire debris samples, saving the analyst time, and assuring accuracy and objectivity.

Compound Naming

A chromatogram from a fire debris sample contains a series of peaks, each specific to a particular compound. Each peak elutes at a different retention time and is measured in intensity by the area of the peak. Using a GC-MS, each peak is also searched against a spectral library to determine likely compound names. It is this combination of retention time and spectral information that an analyst utilizes to name each compound.

The Sherlock system uses the same information – retention time and spectral library search – to automate the naming of compounds in the fire debris sample.

Sherlock applies the retention time information in a unique manner: based on a multiple-compound calibration standard run with each batch of samples, Sherlock determines the Equivalent Carbon Length (ECL) for each peak. For straight-chain alkanes, the ECL is the number of carbons. For instance, decane, the saturated 10-carbon alkane, has ECL 10.000, while dodecane, the saturated 12-carbon alkane, has ECL 12.000. Compounds that fall in between the saturated fatty acids are given interpolated values. The polycyclic aromatic hydrocarbon naphthalene, for example, elutes between decane and dodecane and is assigned an ECL of 11.398. Because Sherlock uses information from the calibration standard run with each batch, ECL computation is a precise measure for each compound that accommodates instrument variation. When used in combination with information from spectral library matches, a broad variety of hydrocarbons can be automatically identified and distinguished from the potentially conflating background peaks of flooring, carpet or other matrices.

Sherlock generates a report giving detailed information about each named compound, and annotates the compound naming directly on the chromatogram.


Advanced Calculations

Given the named compounds, a technician applies a number of techniques to determine what ignitable liquid is present in a sample.  One approach is to directly compare the chromatogram to that of a known ignitable liquid.  Done manually, there are issues of alignment and also of incorporating this result into a useful report.  With Sherlock, alignment to a standard can be done directly and included in the final sample report.  The image below shows a sample (in blue) compared to a gasoline standard (upside-down in red).


This visual approach yields an immediate sense of how similar the sample is to a known standard.

According to ASTM E1618, Standard Test Method for Ignitable Liquids, it is essential to consider not just the individual compounds in a sample but also the relative abundance of the various types of compounds present in the sample. Gasoline, for example, will have a predominance of aromatics, while diesel fuel will have more alkanes. The Sherlock Categorization software automatically combines compounds by type and presents a report giving summations per type. When categorized, the sample shown above yields the following result:


Index Response Percent Name
1 1813479586 76.07 Aromatics
2 58309915 2.45 Cycloalkanes
3 184842692 7.75 Condensed Rings
4 83526215 3.50 Alkanes-straight
5 242008855 10.15 Alkanes-branched
8 1799684 0.08 Alkenes-straight

As can be seen, the high percentage of aromatics is another indicator that gasoline is present.


The traditional approach to fire debris analysis has been criticized for being tedious, time consuming, requiring an experienced technician, and prone to bias. Sherlock improves this forensic analysis by automating compound naming and categorization of hydrocarbons, providing a more complete and objective analysis to aid the investigator. Sherlock automation saves the technician time, reducing backlog and errors, and provides the data in an easy-to-read format for inclusion in case files.

An efficient and accurate analysis of fire debris samples – as provided by MIDI’s Sherlock software – is a critical aspect in continuous improvement of the technology and perception of forensic science in the criminal justice system.

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