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in this video were going to look at the relationship between line shapes and peak models in particular wed like to understand how a probe line shape can influence the stability of a peak model we have two examples of line shapes that are approximations to piece that involve Laurentians and gaussians the LA line shape is a void function that is formed by a numerical integration of a Gaussian with a Laurentian whereas the GL line shape is an approximation to a void function that is formed by the product of a Laurentian with a Gaussian on the face of it looking at the results of these two peak fits applied to the same data the differences are minimal and so one might conclude that there is no real advantage to choosing one over the other when fitting these types of data however the difference between you choosing a void function and a gel line shape only becomes obvious when you start to look at data with poorest signal-to-noise and attempt to fit that data using a peak model of this f