Classification of scab‐ and other mold‐damaged wheat kernels by near‐infrared reflectance spectroscopy

ABSTRACT. Scab (Fusarium head blight) is a disease that causes wheat kernels to be shriveled, underweight, and difficult to mill. Scab is also a health concern because of the possible concomitant production of the mycotoxin deoxynivalenol. Current official inspection procedures entail manual human inspection. A study was undertaken to explore the possibility of detecting scab–damaged wheat kernels by a near–infrared (NIR) diode array spectrometer. Wheat kernels from three categories (sound, scab–damaged, and mold–damaged) were visually inspected by and furnished by the USDA Grain Inspection, Packers, and Stockyards Administration (GIPSA). The reflectance spectrum of each intact kernel was collected at 6–nm increments over a 940 to 1700 nm region. Exhaustive searches of the best combination of individual wavelengths, best difference of wavelengths, best ratio, and combinations thereof were performed on a set of 100 kernels from each of the three categories. The best modeling classification occurred for precisely aligned kernels using a combination of kernel mass and the difference in log(1/R) at two wavelengths, 1182 and 1242 nm. When applied to a test set, the classification model correctly identified the intended category with 95 % accuracy. The most difficult to classify conditions were scab–damaged vs. mold–damaged and mold–damaged vs. sound. When scab–damaged and mold–damaged kernels were combined into one category, the overall accuracy for a two–category (sound vs. damaged) classification model was between 95 % and 98%, depending on the kernel orientation scheme and inclusion of kernel mass. The achieved accuracy levels demonstrate the feasibility of using NIR reflectance spectroscopy with as few as two wavelengths to assist in wheat grading and commercial sorting