  The linear noise approximation models the random fluctuations from the mean
field model of a chemical reaction that unfolds near the thermodynamic limit.
Specifically, the fluctuations obey a linear Langevin equation up to order
$\Omega^{-1/2}$, where $\Omega$ is the size of the chemical system (usually the
volume). Under the presence of disparate timescales, the linear noise
approximation admits a quasi-steady-state reduction referred to as the slow
scale linear noise approximation. However, the slow scale linear approximation
has only been derived for fast/slow systems that are in Tikhonov standard form.
In this work, we derive the slow scale linear noise approximation directly from
Fenichel theory, without the need for a priori scaling and dimensional
analysis. In so doing, we can apply for the first time the slow scale linear
noise approximation to fast/slow systems that are not of standard form. This is
important, because often times algorithms are only computationally expensive in
parameter ranges where the system is singularly perturbed, but not in standard
form. We also comment on the breakdown of the slow scale linear noise
approximation near dynamic bifurcation points -- a topic that has remained
absent in the chemical kinetics literature, despite the presence of
bifurcations in simple biochemical reactions, such the Michaelis--Menten
reaction mechanism.
