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United States Department of Agriculture

Agricultural Research Service

Research Project: ECOLOGY, SAMPLING, AND MODELING OF INSECT PESTS OF STORED GRAIN, PROCESSING FACILITIES, AND WAREHOUSES

Location: Stored Product Insect Research Unit

Title: Detection of insects in grain

Authors
item THRONE, JAMES
item Pearson, Thomas

Submitted to: Meeting Proceedings
Publication Type: Proceedings
Publication Acceptance Date: September 20, 2008
Publication Date: June 1, 2008
Citation: Throne, J.E., Pearson, T.C. 2008. Detection of insects in grain. In: Mancini, R., M.O.Carvalho, B. Timlick, and C. Adler (eds.), Contribution for Integrated Management of Stored Rice Pests. Handbook. Instituto de investigacao Cientifica Tropical, Lisbon, Portugal, p. 123-136.

Technical Abstract: Detecting insects hidden inside kernels of grain is important to grain buyers because internal infestations can result in insect fragments in products made from the grain, or, if the grain is stored before use, the insect population can increase and damage the grain further. In a study in the United States, 98% of Rhyzopertha dominica in railcars of wheat at a mill were hidden within grain kernels and would not be detected by sieving samples of the grain, a standard detection method used at mills. An inherent problem in insect detection is that we are trying to detect insects at very low levels – rice that contains two live injurious insects per 500 grams is considered infested by the United States Department of Agriculture Grain Inspection, Packers, and Stockyards Administration. A 500-gram sample of rice may contain 25,000 kernels; thus, we are trying to detect up to 2 infested kernels in 25,000 kernels of rice. We have shown that a 100-gram sample of wheat containing one R. dominica adult inside a kernel will result in an average of 14 insect fragments in flour milled from that wheat, while a sample containing one larva results in 0.6 fragments and one pupa results in 1.6 fragments. A number of methods have been developed to detect insects hidden inside grain kernels, including staining kernels to detect weevil egg plugs, density separation based on infested kernels being lighter weight and floating in a liquid, detection of carbon dioxide or uric acid produced by the internally feeding insects, detection by use of nuclear magnetic resonance (NMR), detection by standard film or digital X-ray images, use of image analysis for automated detection by X-rays, acoustical sensors to hear insects feeding inside kernels, and enzyme-linked immunosorbent assays (ELISA) to detect myosin in the muscles of insects. Some of the recent methods developed to detect insects hidden inside kernels are near-infrared spectroscopy (NIRS), adapting the single-kernel characterization system (SKCS), computed tomography (CT), acoustic impact emissions (dropping kernels and recording the sounds made when they hit a steel plate), and use of a conductive mill (determining conductivity of a kernel as it’s milled). Problems encountered with these detection methods are that the most accurate methods, such as X-ray, are laborious and expensive, while rapid, automated methods tend to not be able to detect eggs and young larvae. We will present an overview of these detection methods, including advantages and disadvantages, and will focus on some of the newer technologies being developed for detection.

Last Modified: 9/10/2014