New technology to reduce mycotoxins in grain
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1 New technology to reduce mycotoxins in grain Tom A. Scott, Ph. D. Research Chair in Feed Processing Technology Mark Wickstrom, Ph. D., Western College of Veterinary Medicine Michael Kautzman, M. Sc. Candidate University of Saskatchewan
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3 Fusarium Damaged Kernels (FDK) FDK is associated with: Lower yield Lower quality as food or malt Tolerance for No 2 wheat dropped from 2% FDK to 1% Prystupa and Paliwal (2011) estimate Fusarium Head Blight (FHB) costs $80M/yr to Canadian farmers due to lower grade and yield Lower grade / price Potential for mycotoxins / food, malt & feed safety Clear & Nowicki (2011) estimated that FHB created losses >$500M in Quebec & Manitoba alone (1998) Effectively eliminated Durum planting in Manitoba
4 Methods of reducing toxicity Genetic selection for resistance Agronomic practices to reduce infection (i.e., fungicides, tillage practices, crop rotations) (McMullet et al., 2012) Dilution of contaminated grains to permissible levels < 4% for export feed Some EU countries have 0 tolerance for mycotoxins Removal of outer coat of contaminated grain by abrasion Chemical and physical detoxification Alkaline treatments; processing temperatures
5 Methods of reducing toxicity Use of additives Binders (reduce bioavailability) Detoxifiers (reduce bioactivity and/or negative health effects) Enzymes Biocontrol: use of other microflora to competitively exclude or destroy toxins (Palumbo et al., 2008) Employing a postgraduate student to manually remove contaminated seeds (you can t afford anyone else)
6 Detection of FDK and mycotoxins Visual grading is employed and % of FDK estimated Some cultivars have grain coats that tend to get called FDK No indication of level or source of mycotoxins Near Infrared Reflectance Spectroscopy Both for FDK and DON Semi-rapid toxin analysis (Elisa kits) Detailed toxin analysis > 400 mycotoxins FAO estimates 25% of all food is contaminated AllAboutFeed (2012) indicated that 97% of 2012 UK crop is infested with Fusarium 45% is F. graminearum associated with DON
7 Focus on Near Infrared Reflectance (NIR) Spectroscopy Oatway et al. (2011) Field Crop Development Centre, Lacombe, Alberta Using purified DON was able to establish NIR calibrations Able to quantify levels of DON < 2 ppm, with low accuracy Using levels of DON contaminated breeding stock they were only able to predict high concentrations (75 ppm) using purified DON with low precision. Physical Fusarium presence is easy but it hasn t found its way into grading
8 A. SKNIR system B. Singulator wheel C. Kernel feeder D. Kernel pick up E. Positioned grain on trough F. Diverters to separate fractions G. As for F H. Histogram SINGLE KERNEL NEAR INFRARED REFLECTANCE (SKNIR)
9 Visual differences in FDK 1. Scabby seeds or tombstones 2. Intermediate FDK seeds 3. Intermediate seeds in sound fraction 4. Sound seeds Peiris et al. (2010)
10 Controlled Fusarium inoculations of heads of susceptible and less susceptible wheat MORE SOURCES OF VARIABILITY! Inoculated central floret s of individual heads of wheat There were differences in progression of FDK and mycotoxins Spreading down in one strain Spreading in both directions in another strain NOT all kernels that had measureable levels of mycotoxins had visual signs of FDK, including detection of mycelia Peiris et al. (2011)
11 Controlled Fusarium inoculations of heads of susceptible and less susceptible wheat MORE SOURCES OF VARIABILITY! FHB damaged kernels with visible mycelia: Lower kernel weight Higher mycotoxin levels Were more prevalent in susceptible strain Resistant strain may limit spread upwards in spike and/or detoxify mycotoxins Peiris et al. (2011)
12 Kernel Wt (mg) DON (ppm) DON (ppm) Kernel Wt (mg) Peiris et al. (2011)
13 SKNIR vs Visual Grading FDK Visual grading subject to error and low repeatability (Wegulo & Dowell, 2008) 21 sources of wheat x 4 x 100 kernel samples (Nebraska, 2007, year of high FDK) Visually scored Based on chalky-white or pinkish appearance and/or shriveled By two graders SKNIR Each sample sorted twice Correlation (r) between graders & SKNIR was: r = 0.91 for Grader 1 r = 0.89 for Grader 2
14 SKNIR vs Grader (Wegulo and Dowell, 2008) Rating Min Max SKNIR 1 71% Grader % Grader % Standard deviations for 4 replicate samples of 100 grains illustrates that the SKNIR was better than the graders at sorting FDK SKNIR has a wider range of FDK detection SKNIR can sort 100 kernels in ~7 min. Based on 40g/1000 kernels that is 34 g of seed / hr Almost as fast as a postgraduate student So probably not commercial useful!! Graders sorted 1 sample in 8-10 min
15 SKNIR to predict FDK and DON NIR spectra ( nm) used to differentiate seeds based on FDK and DON (lab controlled inoculations) Differences may be due to chemical changes or associated with physical changes measured by NIR (Peiris et al., 2010) FDK specific spectra & nm DON specific spectra 1410 nm
16 Peiris et al. (2010) - cont. 108 samples sorted with a total of 25,665 kernels Overall 12.2% FDK Visual verification of sorted fractions indicated that the 3 sorted fractions: Sound = 98.8% Good Intermediate seeds had 43% severe FDK (tombstone) FDK had 55% tombstone
17 Peiris et al. (2010) - cont. - DON Single seed levels of DON measured with GC-MS SKNIR was used to sort kernels into three fractions based on DON 108 lines over 2 growing seasons DON levels in the grains varied from 3 to 268 ppm SKNIR can sort individual kernels < 60 and > 60 ppm with > 95% accuracy
18 SKNIR Indicates that NIR is capable of identifying FDK and DON to a moderate degree Does not necessarily measure the fungi or the toxin directly, but estimates it based on other changes to the kernel Capacity, 1 seed / sec. May have use in: Selection for resistant strains of grain Grading grains, as an assessment of both damage and toxin
19 BoMill IQ NIR Individual Kernel Laboratory model 1000 kernels / min Individual singulators for wheat, barley or durum, can be modified for other grain Sort into 6 fractions Sorting Criteria Protein, Hardness, Vitreousness, Pearling yield, Viability and Disease damage
20 TECHNOLOGY! WHERE WOULD WE BE WITHOUT IT? NOW HOW DO WE SORT 20 MMT OF WHEAT?
21 Gravitational beds Increase fan speed of combines and blow FDK & DON out the back Gravitational sorting Lighter material sorted out and estimate this would decrease FDK and DON
22 Color sorters Delwiche et al. (2005) indicated that color sorters have been used to remove FDK Results in ~50% decrease in FDK and DON Capacity up to 10 MT/h Have seen operation that used gravitational sorted material and sorted this with color sorter to remove ergot
23 Spectrum Agricultural Inc FDK seeds are structurally modified by the infection K-Max system utilizes the scatter of light off the kernel as a means of identifying FDK (David Prystupa, Manitoba)
24 DON Concentration Before and After Separation of FDK
25 Spectrum Agricultural Inc Modular design 5 modules process 1 MT/hr (42 mg/k) Design life 80,000 h Truck / Trailer mobile (18 MT/h) 93% of FDK removed DON reduced by 84% Kmax Economics ~ Capital cost = $350K Operating Costs (/MT) Amortization $ Labor $1.25 Electricity $0.65 Maintenance $1.30 Total $ (David Prystupa, Manitoba)
26 Front Cover!
27 BoMill TriQ 30 NIR Transmittance 18 measurements / seed 30,000 seeds / sec Initially: Establishes variability in seeds Produces 10 fractions of equal area under the distribution curve These are measured and used to set limits for production of 3 fractions
28 BoMill TriQ 30 Measures crude protein, highly associated with other traits and functionality Fungal protease decreases protein (gluten) levels in kernel Unsorted Low CP Medium CP High CP Protein Wet Gluten DON (ppm)
29 BoMill Applications Protein distribution in the sample and visual observations of the kernels Infected, fusariated and diseased kernel will come in the low protein fraction The main fraction typically consist of sound and healthy kernels Immature kernel will come into the high protein fraction
30 BoMill TriQ - Sorting of Fusarium infected wheat Wheat Unsorted DON ppb / (% of sample) Low CP 9600 (3) 2710 (2) Mid CP 720 (64) 335 (48) High CP 290 (33) 120 (50) 1 st fraction < 5% of total seeds DON varied from ppb Remaining 95% of samples were less than 1 ppm (5) 510 (22) <100 (74)
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32 U of Sask Data 1 Source of wheat ~2% FDK graded Two fractions Lowest 20% CP Highest 80% CP Outliers High levels of outliers can be produced due to malpositioned seeds
33 Fractions tested for mycotoxins North Dakota State University Veterinary Toxicology Measure 16 mycotoxins routinely found in grain Detection limit 0.5 ppm 1. Vomitoxin 2. T-2 Tetraol 3. Fusarenone-X 4. 3-Acetyl D)N Acetyl DON 6. DAB 7. T-2 Triol 8. T-2 Toxin 9. Iso T-2 Toxin 10. Scirpentriol 11. Nivalenol Acet-Scirp 13. Neosolaniol 14. HT-2 Toxin 15. Zearalenol 16. Zearalenone
34 U of Sask Fusarium Wheat (1.2% FDK)
35 Crude Protein 4 FHB
36 DON (ppb) of 4 wheat samples sorted
37 Recent Data Dec 6, 2012 % Fusarium Damaged Kernels ,7 Durum Unsorted Low CP <20% High CP 0,0 Soft White 0,3 Red Spring 0,0 Red Spring Canadian Grain Commission Grades for unsorted and sorted wheat fractions Grain Unsorted Low CP High CP Durum Salvage Salvage 3CW Soft W CW Feed Fusarium* 2 CW Red S1 3CW CW Feed 1CW Red S2 CW Feed Fusarium* 1CW Fusarium* = Sample Canada AC Fusarium Damaged 2011 Wheat (No %) = $345 (Vancouver) 2011 Wheat (No 2) = $307 (Vancouver) 2011 Wheat (Feed) = $235 (Vancouver) CGC October 25, 2012
38 What are we going to do with high Fusarium sorted grains? Burn it? If 5% of seeds are removed, this is 50,000 MT/ 1MMT sorted $250 = $12.5M/MMT) Ethanol? Mycotoxins are concentrated in the DDGS (3 fold) Some indications that some bacteria are able to detoxify some mycotoxins Why ruminants tend to be less impacted?
39 BoMill Tri Q 30 seed sorter 30,000 seeds / second = 3 MT/hr Facility operating at economies of scale (10 units) = 250,000 MT/yr Would take 32 facilities to sort 20% of wheat graded as feed (8 MMT) Uses NIR detector to sort into 3 categories (adaptable) 88 NIR light sources Limited spectrum focused on sorting by crude protein Objectives are to determine value of other detectors E.g. detect sprouting
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41 References Clear, R. and T. Nowicki, The impact of FHB on grain grading and handling in Canada. 6 th Canadian Workshop on Fusarium Head Blight pp Delwiche, S.R., T.C. Pearson and D.L. Brabec, High-speed optical sorting of soft wheat for reduction of deoxynivalenol. Plant Disease 89: McMullen, M., G. Bergstrom, E. DeWolf, R. Dill-Macky, D. Hershman, G. Shaner and D. Van Sanford, A unified effort to fight an enemy of wheat and barley: Fusarium head blight. Plant Diseases posted 07/11/2012 Oatway, L., J. Helm and Z. Hartman, A novel approach to the development of near infrared reflectance spectroscoy (NIRS) to screen for deoxynivalenol in cereal grains. 7 th Canadian Workshop on Fusarium Head Blight pp 38. Palumbo, J.D., T.L. O Keefe and H.K. Abbas, Microbial interactions with mycotoxigenic fungi and mycotoxins. Toxin Reviews 27: Prystupa, D., Removal of Fusarium damaged seeds with an optical sorter. 7 th Canadian Workshop on Fusarium Head Blight.
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