This study evaluated the performance of low-cost, real-time, and field-deployable spectroscopic instruments operating at near-infrared (NIR) and mid-infrared (MIR) wavelengths for measuring quality traits (β-glucan, starch, protein, and lipid) of oats to support breeding selection. Samples were kindly provided by PepsiCo R&D (n = 150) as oat groats. A handheld FT-NIR sensor (1350–2560 nm) measured spectra of ground and intact oat samples, while a portable FT-IR spectrometer (4000–650 cm −1) measured ground samples only. Several laboratory reference methods were used to measure β-glucan, starch, protein, and lipid composition to develop spectroscopic analysis models based on Partial Least Squares Regression (PLSR). Best model performance was obtained from NIR spectra of ground groats, with standard error of prediction (SEP) for β-glucan, starch, protein, and lipid of 0.2%, 1.0%, 0.6%, and 0.3%, respectively. PLSR models for the MIR spectra exhibited similar predictive accuracy. The performance of these PLSR models either matched or outperformed NIR techniques reported in the literature using portable and benchtop systems. Therefore, novel miniaturized NIR sensors can provide breeders with a rapid method (15 s) to screen for unique traits in the field with equivalent reliability and sensitivity as benchtop systems.