Current spectral models for detection of aflatoxin (AF) in maize kernels are limited by very low representation of kernels with AF levels above the models’ specified contamination thresholds. With the aim of improving prediction model sensitivity to AF-contaminated kernels, two methods were evaluated for artificially enriching the representation of kernels containing ≥150 ppb AF in a large spectral dataset: humid incubation and synthetic oversampling. The addition of synthetic training samples improved prediction accuracy of kernels containing ≥150 ppb AF from 51% to 80%. Humid incubation contributed additional kernels with intermediate levels of AF contamination (primarily 5 - 75 ppb), but generally did not change the overall distribution of AF contamination. Feature importance distributions overlapped among models at 329-345 nm, 380-385.5 nm, 415-425 nm, 639-668 nm, and 1,013.5-1,060 nm. These spectral ranges could be applicable to the development of limited wavelength grain sorting devices for low-resource settings.