@article {bnh-3093,
	title = {Application of statistical techniques to pyrolysis-GC-MS data from soil to identify the impact of fire},
	number = {226},
	year = {2016},
	month = {09/2016},
	institution = {Bushfire and Natural Hazards CRC},
	address = {Melbourne},
	abstract = {<p>Soil organic matter has strong effects on many soil properties such as water holding capacity, soil structure and stability, nutrient availability and cation exchange capacity.\&nbsp; Therefore, characterising soil organic matter is necessary to improve soil management.\&nbsp; Pyrolysis coupled to gas chromatography-mass spectrometry (pyr-GC-MS) is one of many techniques that have been successfully used in this characterisation.\&nbsp; However, a major limitation of pyr-GC-MS is that generates large amounts of mass-spectrometry data preventing fast, high throughput data analysis.\&nbsp; This hinders our ability to identify compounds in complex matrices such as SOM that could be useful for predicting their characteristics.\&nbsp; In this study, we aimed to investigate whether it was possible to rapidly identify significant differences among pyr-GC-MS data from soil from burnt and unburnt areas using an unsupervised statistical approach and identify the specific features that cause them.\&nbsp; Of nearly 400 useful compounds extracted from the pyr-GC-MS data, only 15 were found to be necessary to classify between burnt and unburnt soil.\&nbsp; We discuss how these features could be useful in the classification of soil disturbance such as fire or, potentially, as a quantitative measure of fire impact (intensity or severity).</p>},
	author = {Malcolm Possell and Gharun, Mana and Bell, Tina}
}
