  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). In the presence of disparate timescales, the linear noise
approximation admits a quasi-steady-state reduction referred to as the
\textit{slow scale} linear noise approximation (ssLNA). Curiously, the ssLNAs
reported in the literature are slightly different. The differences in the
reported ssLNAs lie at the mathematical heart of the derivation. In this work,
we derive the ssLNA directly from geometric singular perturbation theory and
explain the origin of the different ssLNAs in the literature. Moreover, we
discuss the loss of normal hyperbolicity and we extend the ssLNA derived from
geometric singular perturbation theory to a non-classical singularly perturbed
problem. In so doing, we disprove a commonly-accepted qualifier for the
validity of stochastic quasi-steady-state approximation of the
Michaelis--Menten reaction mechanism.
