Submitted to: Transactions of the ASAE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: April 1, 2003
Publication Date: May 1, 2003
Citation: MAGHIRANG, E.B., DOWELL, F.E., BAKER, J.E., THRONE, J.E. AUTOMATED DETECTION OF SINGLE WHEAT KERNELS CONTAINING LIVE OR DEAD INSECTS USING NEAR-INFRARED REFLECTANCE SPECTROSCOPY. TRANSACTIONS OF THE AMERICAN SOCIETY OF AGRICULTURAL ENGINEERS. 2003. v. 46(4). p. 1277-1282. Interpretive Summary: The presence of internal insects in wheat is a major problem for the wheat industry. Visual inspection, which is commonly used in grain trade, cannot detect internal insects. There is a need for a measurement technique that can rapidly detect the presence of internal insects. A commercially available automated NIR system was used over a two-month storage period to detect single wheat kernels that contained live or dead internal rice weevils at various stages of growth. Correct classification of sound kernels and kernels containing live(Day 1) and dead (Days 7, 14, 28, 42, and 56) rice weevils. The calibrations using live insects at pupal, large, medium-sized, and small larval stages averaged 94, 92, 84, and 62%, respectively. Pupae + large larvae calibration models were developed for each sample set containing live internal insects yielded an 86 to 96% correct classification of dead internal insects. Calibrations that used dead internal insects correctly detected the presence of live internal insects with an accuracy of 92 to 93%. These findings will impact how calibration sample sets can be handled. Immediate sample processing will no longer be necessary; internal insects can be killed and calibrations created at a later time without sacrificing accuracy. Additionally, laboratories can share these same calibration samples saving time and resources.
Technical Abstract: An automated NIR system was used over a two-month storage period to detect single wheat kernels that contained live or dead internal rice weevils at various stages of growth. Correct classification of sound kernels and kernels containing live pupae, large larvae, medium-sized larvae, and small larvae averaged 94, 92, 84, and 62%, respectively. Pupae + large larvae calibrations were developed using live or dead internal insects. Validation results showed correct classifications ranging from 86 to 96% over the two-month storage period. Thus, wheat kernels containing either live or dead insects can be used to develop calibrations for detecting both live and dead insects in wheat.