A balancing act: Synthetic oversampling corrects skewed distribution of aflatoxin in maize and improves prediction of contaminated kernels

Abstract

Current spectral models for detecting 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 parts per billion (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 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 1013.5–1060 nm. These spectral ranges could be applicable to the development of limited wavelength grain sorting devices for low-resource settings.