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
Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: June 7, 2007
Publication Date: September 27, 2007
Citation: Throne, J.E., Pearson, T.C. 2007. Detection of insects in grain [abstract]. International Workshop on Management of Stored Rice Pests, Alcacer do Sal, Portugal, September 17-19, 2007.
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, more than 95% of insects 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 kilogram is considered infested by the Grain Inspection, Packers, and Stockyards Administration of the United States Department of Agriculture. A kilogram of rice may contain 50,000 kernels; thus, we are trying to detect up to 2 infested kernels in 50,000 kernels of rice. We have shown that one lesser grain borer adult in a kernel of wheat will result in 14 insect fragments in a sample of flour milled from that wheat, while 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 in grain, including staining kernels to detect weevil egg plugs, density separation based on infested kernels being lighter weight and floating in a liquid, crushing kernels between ninhydrin-impregnated papers, 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 from 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), conductive mill (determining conductivity of a kernel as it’s milled), and infrared thermal imaging. A new NIRS method has also been developed for detecting insect fragments in flour. Problems encountered with detection methods is that the most accurate methods, such as x-rays, 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.