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ARS Home » Plains Area » Manhattan, Kansas » Center for Grain and Animal Health Research » Stored Product Insect and Engineering Research » Research » Publications at this Location » Publication #310425

Research Project: PRESERVATION, ENHANCEMENT, AND MEASUREMENT OF GRAIN QUALITY AND MARKETABILITY

Location: Stored Product Insect and Engineering Research

Title: Detecting and Segregating Black Tip-Damaged Wheat Kernels Using Visible and Near Infrared Spectroscopy

Author
item Armstrong, Paul
item Maghirang, Elizabeth
item Pearson, Tom - Amgen, Inc

Submitted to: Cereal Chemistry
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/11/2015
Publication Date: 7/24/2015
Citation: Armstrong, P.R., Maghirang, E.B., Pearson, T.C. 2015. Detecting and Segregating Black Tip-Damaged Wheat Kernels Using Visible and Near Infrared Spectroscopy . Cereal Chemistry. 92(4): 258-263. DOI: http://dx.doi.org/10.1094/CCHEM-09-14-0201-R.

Interpretive Summary: Black tip wheat damage is a distinct discoloration of the wheat germ end and surrounding area caused by fungi and bacteria. Black tip infections occur in the field under conditions of high relative humidity or rainfall. There are ongoing breeding efforts to develop black tip resistance but considering that it is highly weather-dependent, it has been difficult to prevent this problem. Black tip poses no toxicological danger can cause flour discoloration and is thus considered a serious quality factor. Measuring black tip damage is done by human inspection and an instrumented approach is highly desirable. Two instruments developed by USDA-ARS were evaluated for black tip measurement on single wheat kernels. Both take spectral measurements with one using near-infrared light and the other, visible light. The near-infrared instrument can process about three kernels per second while the visible instrument processes about 30 kernels per second. Near-infrared measurements could distinguish good and lightly damaged kernels from more heavily damaged kernels with good accuracy, greater than 80%. Visible measurements were less accurate but still could provide useful measurements for some applications using multiple passes of a sample through the instrument. These instruments can serve as important tools for plant breeders and grading facilities of the wheat industry that require timely and objective measurements to quantify this defect.

Technical Abstract: Detection of individual wheat kernels with black tip symptom (BTS) and black tip damage (BTD) was demonstrated using near infrared reflectance spectroscopy (NIRS) and silicon light-emitting-diode (LED) based instruments. The two instruments tested, a single kernel near-infrared spectroscopy instrument (SKNIRS) and silicon LED-based single kernel high-speed sorter (SiLED-SKS), were both developed by the Stored Product Insect and Engineering Research Unit, CGAHR, ARS-USDA, Manhattan, KS. Black tip damage was classified into 4 levels for the study ranging from sound, symptomatic (BTS) at two levels, and damaged (BTD). Discriminant analysis models for the SKNIRS instrument could distinguish sound undamaged kernels well, correctly classifying kernels 80% of the time. Damaged kernels were classified with 67% accuracy and symptomatic levels at about 44%. Combining sound with lightly symptomatic kernels and heavily symptomatic with damaged kernels gave higher classification accuracy for these two groups (81% to 87%). A linear regression model was developed from the SiLED-SKS sorted fractions to predict the percentage of combined BTS and BTD kernels in a sample. The model had an R2 = 0.64 and a standard error of prediction of 7.4% showing it has some measurement ability for BTS and BTD. The SiLED-SKS correctly classified and sorted out 90% of BTD and 66% of BTS for all 28 samples after three passes through the sorter. These instruments can serve as important tools for plant breeders and grading facilities of the wheat industry that require timely and objective determination and/or sorting of different levels of black tip present in wheat samples.