T. C. Pearson

Reduction of Aflatoxin and Fumonisin Contamination in Yellow Corn by High-Speed Dual-Wavelength Sorting

ABSTRACT A high-speed dual-wavelength sorter was tested for removing corn contaminated in the field with aflatoxin and fumonisin. To achieve accurate sorting, single kernel reflectance spectra (500–1,700 nm) were analyzed to select the optimal pair of optical filters to detect mycotoxin-contaminated corn during high-speed sorting. A routine, based on discriminant analysis, was developed to select the two absorbance bands in the spectra that would give the greatest classification accuracy. In a laboratory setting, and with the kernels stationary, absorbances at 750 and 1,200 nm could correctly identify >99% of the kernels as aflatoxin-contaminated (>100 ppb) or uncontaminated. A high-speed sorter was tested using the selected filter pair for corn samples inoculated with Aspergillus flavus; naturally infested corn grown in central Illinois; and naturally infested, commercially grown and harvested corn from eastern Kansas (2002 harvest). For the Kansas corn, the sorter was able to reduce aflatoxin levels by ...

High-speed optical sorting of soft wheat for reduction of deoxynivalenol

Fusarium head blight (FHB) is a fungal disease that affects small cereal grains, such as wheat and barley, and is becoming more prevalent throughout much of the worlds temperate climates. The disease poses a health risk to humans and livestock because of the associated production of the mycotoxin deoxynivalenol (DON or vomitoxin) by the causal organism, Fusarium graminearum. A study was undertaken to examine the efficiency of high-speed, optical sorting of intact wheat (Triticum aestivum) kernels for reduction of DON concentration. Soft red winter (n = 32) and soft white (n = 3) wheat samples, known to have elevated levels of FHB, were obtained from commercial mills throughout the eastern United States. An additional seven samples of wheat from the discard piles of in-mill cleaners were also studied. Fusarium-damaged wheat, cleaned of nonkernels and foreign material ( ~4.5 kg/sample, DON range = 0.6 to 20 mg/kg), was fed into a commercial high-speed bichromatic sorter operating at a throughput of 0.33 kg/(channel-min) and a kernel rejection rate of 10%. A wavelength filter pair combination of 675 and 1,480 nm was selected for sorting, based on prior research. Visual measurements of the proportion of Fusarium-damaged kernels were collected on incoming and sorted seed (separate analyses of accepted and rejected portions), as were measurements of DON concentration. Results indicated that the fraction of DON contaminant level in the sorted wheat to that in the unsorted wheat ranged from 18 to 112%, with an average of 51%. Nine of the 35 regular samples and all seven of the discard pile samples underwent a second sort, with five from this second set undergoing a third sort. Multiple sorting was effective in producing wheat whose DON concentration was between 16 and 69% of its original, unsorted value.

AUTOMATED DETECTION OF INTERNAL INSECT INFESTATIONS IN WHOLE WHEAT KERNELS USING A PERTEN SKCS 4100

The wheat industry is in need of an automated, economical, and rapid means of detecting whole wheat kernels nwith internal insect infestation. The feasibility of the Perten Single Kernel Characterization System (SKCS) to detect internal ninsect infestations was studied. The SKCS monitors compression force and electrical conductance as individual kernels are ncrushed. Samples of hard red winter (HRW) wheat and soft red winter (SRW) wheat infested with rice weevil [Sitophilus oryzae n(L.)] and lesser grain borer [Rhyzopertha dominica (F.)] were run through the SKCS and the conductance/force signals saved nfor post-run processing. Algorithms were developed to detect kernels with live internal insects, kernels with dead internal ninsects, and kernels from which insects have emerged. The conductance signal was used to detect live infestations and the nforce signal for dead and emerged infestations. Live insect detection rates were 24.5% for small-sized larvae, 62.2% for nmedium-sized larvae, 87.5% for large-sized larvae, and 88.4% for pupae. The predicted, and observed, false positive (sound nkernels classified as infested) rate was 0.01%. Dead insect detection rates were 60.7% for large-sized larvae, 65.1% for npupae, and 72.6% for kernels where the insect emerged. The false positive rate of the dead insect detection algorithm ranged nfrom 0.2% for SRW to 0.5% for HRW. In all cases, insect detection rates were higher for rice weevil than lesser grain borer. nThe classification algorithms were robust for a wide range of moisture contents.

DETECTION OF WHEAT KERNELS WITH HIDDEN INSECT INFESTATIONS WITH AN ELECTRICALLY CONDUCTIVE ROLLER MILL

A laboratory roller mill system was modified to measure and analyze the electrical conductance of wheat as it was crushed. The electrical conductance of normal wheat kernels is normally low and fairly constant. In contrast, the electrical conductance of wheat kernels infested with live insects is substantially higher, depending on the size of the larvae and the resulting contact of the crushed larvae between the rolls. This instrument was designed to detect internal insect infestations in wheat that has a moisture content of 13.5% or less. The laboratory mill can test a kilogram (kg) of wheat in less than 2 min and 100 g in less than 10 s. Hard red winter and soft red winter wheat containing larvae of rice weevils and lesser grain borers of a variety of sizes were tested. On average, the instrument detected 8.3 out of 10 infested kernels per 100 g of wheat containing the larger sized insects (fourth instar or pupae). Kernels infested with medium-sized larvae (second or third instar) were detected at an average rate of 7.4 out of 10 infested kernels per 100 g of wheat. Finally, kernels infested with the small-sized larvae (first or second instar) were detected at a rate of 5.9 out of 10 infested kernels per 100 g of wheat. Under reasonable grain moisture contents there were no false positive errors, or noninfested kernels classified as insect infested. The cost of the mill is low and can lead to rapid and automated detection of infested wheat.

CHARACTERISTICS AND SORTING OF WHITE FOOD CORN CONTAMINATED WITH MYCOTOXINS

White corn grown in southern Texas was collected for characterization and evaluation of the feasibility of sorting kernels containing mycotoxins. Kernels were grouped into one of six symptom categories depending on the degree of visible discoloration and bright green-yellow fluorescence (BGYF) or bright orange fluorescence (BOF). Kernels visibly discolored (= 25% of their surface) and having BGYF contained over 57% of the aflatoxin. However, kernels approximately 50% discolored without BGYF contained over 35% of the aflatoxin. Over 33% of the fumonisin was found in kernels that were visibly discolored and had BOF. The remaining fumonisin was in asymptomatic kernels at low levels. Sorting tests for removing mycotoxin-contaminated kernels were performed using a dual wavelength high-speed commercial sorter. In one pass through the sorter, aflatoxin was reduced by an average of 46%, and fumonisin was reduced by 57% while removing 4% to 9% of the corn. Re-sorting accepted kernels a second time resulted in an 88% reduction in aflatoxin while removing approximately 13% of the corn. Approximately half of the aflatoxin missed by the optical sorter was found in larger kernels showing BGYF but no other symptoms, with the remaining aflatoxin in smaller kernels where the germ was damaged by insect feeding.

Camera Attachment for Automatic Measurement of Single-Wheat Kernel Size on a Perten SKCS 4100

A simple camera was attached to a Perten Single-Kernel Characterization System (SKCS) 4100 to measure single kernel morphology as they are fed through the SKCS. The camera and lighting were positioned above the SKCS weigher bucket. Each image of a wheat kernel was captured and processed in 15 to 60 ms. Image measurements included kernel area, length, and width.

A New PCA/ICA Based Feature Selection Method

Dimensionality reduction algorithms help reduce the classification time and sometimes the classification error of a classifier (Yang, et al., 1997). For time critical applications, in order to have reduction in the feature acquisition phase, feature selection methods are more preferable to dimensionality reduction methods, which require measurement of all inputs. Traditional feature selection methods, such as forward or backward feature selection, are costly to implement. In this study, we introduce a new feature selection method that decides on which features to retain, based on how PCA (principal component analysis) or ICA (independent component analysis) (Hyvarinen and Oja, 1999) values those features. We compare the accuracy of our method to backward and forward feature selection with the same number of features selected and PCA and ICA using the same number of principal and independent components. For our experiments, we use spectral measurement data taken from corn kernels infested and not infested by fungi.

Detection of Fragments from Internal Insects in Wheat Samples Using a Laboratory Entoleter

ABSTRACT A simple, rapid method that uses a small mechanical rotary device (entoleter) was developed for estimating insect fragment counts in flour caused by hidden, internal-feeding insects in whole grains of hard red winter and soft red winter wheat. Known counts of preemergent adults, pupae, and larvae of lesser grain borers and rice weevils were blended with 500 g samples of uninfested wheat. The entoleter impeller speed was adjusted based on grain hardness and moisture content to obtain about ≈98% intact and ≈2–2.5% broken kernels in an uninfested sample. The entoleter flung the wheat kernels against a surrounding steel ring. Approximately 70–90% of the insect-infested kernels, being weaker, released internal insect pieces upon impact. The broken kernels were sieved with number 10 and number 20 sieves to obtain large-sieved and small-sieved fractions, respectively. Insect pieces in sieved fractions were counted. The insect piece counts were correlated with the estimated flour fragments (R2 = 0.94). T...

Impact of grain morphology and the genotype by environment interactions on test weight of spring and winter wheat ( Triticum aestivum L.)

Wheat (Triticum aestivum L.) market grades and prices are determined in part by test weight (TW). Millers value high TW because it is typically associated with higher flour extraction rates and better end-use quality. Test weight is expected to be influenced by other directly quantifiable grain attributes such as grain length (GL), grain width (GW), shape, single-grain-density (SGD), thousand-grain-weight (TGW), and packing efficiency (PE). The objectives of this study were to: (1) determine the primary morphological grain attributes that comprise TW measurements for winter and spring wheat classes; and (2) determine TW stability and genotype and genotypexa0×xa0environment interactions (GEIs) of the attributes that comprise TW. A market class representative group of 32 hard spring and 24 hard winter wheat cultivars was grown at several locations in South Dakota in 2011 and 2012. A regularized multiple regression algorithm was used to develop a TW model and determine what grain attribute reliably predicts TW. A GGE biplot was used for stability and GEI analyses whereas a linear mixed model was used for variance analyses. Data were collected on eight grain traits: TW, SGD, TGW, protein concentration, GW, GL, shape, size, and PE. Observations showed that in both spring and winter wheat, SGD accounted for over 90% of the phenotypic variation of TW. Cultivars with stable and high TW were identified in both wheat classes. Apart from TW; significant (pu2009<u20090.0001) genotype, environment, and GEI variances were observed for GW and SGD, a more direct measure of which could help improve genetic gain for TW.