Submitted to: Journal of Cereal Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/9/2019
Publication Date: 2/27/2019
Citation: Armstrong, P.R., Maghirang, E.B., Ozulu, M. 2019. Determining damage levels in wheat caused by Sunn pest (Eurygaster integriceps) using visible and near-Infrared spectroscopy. Journal of Cereal Science. 86:102-107. https://doi.org/10.1016/j.jcs.2019.02.003.
Interpretive Summary: Sunn pest is an insect that causes damage when feeding on wheat berries by injecting a salivary enzyme which damages the gluten and results in significant decrease in wheat quality and price. Being able to detect and quantify the amount of damage in wheat samples is an important tool for wheat breeders, grain buyers, and grain processors. The prevalent detection technique for determining the number of bug-infested grains is still visual inspection, but it is time consuming and prone to errors. A commercial near infrared (NIR) instrument for bulk samples was used to measure Sunn pest damage wheat, but it was demonstrated that calibrations were not good at measuring low concentrations of SP-damage, which is where accuracy most critical for end users, but performed much better with higher levels of damage. A single kernel visible and near-infrared instrument was better able to correctly classify kernels as being sound or Sunn pest damaged. However, there were instances when results were poor, which makes the capability of the method inconclusive. The single seed instrument could correctly identify, on average, sound from damaged seeds 75% of the time. More tests will need to be done with more wheat varieties and conditions to confirm if this method can provide a practical solution.
Technical Abstract: A commercial near-infrared (NIR) instrument for bulk samples and a modified Single Kernel Near-Infrared (SKNIR) instrument equipped with a visible or NIR spectrometer were studied as a way to measure damage levels caused by Sunn pest (SP) in wheat. Sunn pest causes damage by feeding on wheat berries and injecting a salivary enzyme damaging the gluten. For measurement of SP damage in bulk wheat, NIR calibration models developed for mixtures containing 0-10% and 0-100% sound and SP damaged wheat resulted in R^2 of 0.25 and 0.89 and SECVs of 2.75 and 10.9, respectively. The 0-100% model was considered a qualitative measure of damage but predictions were poor over 0-10%, which is a critical range for commercial applications. Discrimination between single kernels of Sound and SP-damaged was typically good, with classification accuracy averaging ~75% for both visible and NIR although some were poor, which greatly affected the average. Average classification accuracy was ~85% for spectral data that contained kernels from all samples. While the potential for using visible or NIR spectroscopy was shown, results highlighted the need to develop a more robust SP classification model to further evaluate the single kernel model.