SYNTHETIC OVERSAMPLING IMPROVES SPECTRAL DETECTION OF AFLATOXIN IN SINGLE MAIZE KERNELS

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.

Name thesis:degree_name

M.S., Plant Pathology and Plant-Microbe Biology

Level thesis:degree_level

Master of Science

Discipline thesis:degree_discipline

Plant Pathology and Plant-Microbe Biology

Grantor

Cornell University

Year dc:date.issued

2022

Author dc:creator

• Siegel, Chloe Siobhan

Committee member dc:contributor.committeemember

• Bergstrom, Gary Carlton

Language dc:language.iso

en

DOI

https://doi.org/10.7298/69xa-vr67

Dc Identifier Other

ProQuest Submission ID: 11459
ProQuest Publication ID: 29168023

OAI identifier oai:identifier

oai:ecommons.cornell.edu:1813/111653