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ARS Home » Southeast Area » Gainesville, Florida » Center for Medical, Agricultural and Veterinary Entomology » Insect Behavior and Biocontrol Research » Research » Publications at this Location » Publication #314003

Title: Detection and prediction of Sitophilus oryzae infestations in triticale via near infrared spectral signatures

item KHEDHER AGHA, M - University Of Baghdad
item LEE, W - University Of Florida
item WANG, C - University Of Florida
item Mankin, Richard
item BLOUNT, A - University Of Florida
item BUCKLIN, R - University Of Florida
item BLIZNYUK, N - University Of Florida

Submitted to: Journal of Stored Products Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/27/2017
Publication Date: 5/15/2017
Citation: Khedher Agha, M.K., Lee, W.S., Wang, C., Mankin, R.W., Blount, A.R., Bucklin, R.A., Bliznyuk, N. 2017. Detection and prediction of Sitophilus oryzae infestations in triticale via near infrared spectral signatures. Journal of Stored Products Research. 72:1-10.

Interpretive Summary: Scientists at the USDA-ARS Center for Medical, Agricultural, and Veterinary Entomology, Gainesville, FL, and the University of Florida, Department of Agricultural and Biological Engineering, Gainesville, FL, used Near Infrared Spectroscopy to predict levels of infestation of triticale grain by rice weevils. These are internal feeders that cannot be detected visually until they become adults and exit the seeds. The use of this method enables the identification of growth stage as well as the presence of the rice weevil pest. As the cost of this method decreases, it will become more useful for early detection of insect infestations in grain.

Technical Abstract: The goal of this research was to detect and predict degree of triticale seed infestation with rice weevils using near-infrared (NIR) spectroscopy. Groups of seeds at 11 different levels (degrees) of infestation were tested by combining mixtures of infested and uninfested seeds at different ratios. Separate tests were conducted on three replicates of six different growth stages from egg to adult inside the seed. The variable selection and analysis for the NIR data were done using several generalized linear regression and classification methods, such as a stepwise variable selection using (GLM SELECT), an exhaustive model search. The analyses indicated that the later growth stages could be detected more accurately than early infestation. The stepwise variable selection produced the lowest mean square differences and yielded a high R2 value (0.98) for the larvae 4th instar, a pupa and an adult inside the seed. Overall, this study showed a great potential of using near-infrared spectroscopy for detection of the infested seed in the different growth stages of the rice weevil.