The intermediate infrared region (IMIR, 4 – 8 µm) provides significant advantages over the visible-shortwave infrared and mid-infrared for quantitative determination of mafic mineral composition. In particular, olivine’s sharp spectral features in IMIR spectra exhibit systematic shifts in wavelength position with iron-magnesium content. Previous IMIR studies have used laboratory data, with signal-to-noise ratios (SNRs) and spectral resolutions greater than those expected of imaging spectrometers. Here we employ a feature fitting algorithm to quantitatively assess the influence of SNR and sampling rate on olivine detection and compositional interpretation from IMIR data. We demonstrate that olivine is easily distinguished from pyroxene and other lunar-relevant minerals across IMIR wavelengths, with the feature-fitting algorithm effectively determining olivine composition for various synthetic, terrestrial, Martian, and lunar samples with an average error of only 6.4 mol%. We then apply the feature-fitting routine to degraded spectra with reduced SNRs and sampling rates, establishing data-quality thresholds for accurate determination of olivine composition. Spectra for the sample most relevant to lunar exploration, an Apollo 74002 drive tube consisting of microcrystalline olivine and glass-rich pyroclastics, required SNRs ≥ 200 for sampling rates ≤ 25 nm to predict composition within ±11 Mg# (molar Mg/[Mg+Fe] * 100) of the sample’s true composition. Derived limits on SNRs and sampling rates will serve as valuable inputs for the development of IMIR imaging spectrometers, enabling comprehensive knowledge of olivine composition across the lunar surface and providing valuable insight into the Moon’s crustal history and thermal evolution.