Detoxification of DON Contaminated Wheat by Fractional Pneumatic Separation, Near-Infrared Transmittance (BoMill), and Gaseous Ozone, and the Effects of Treated Grain on Broiler Chickens.
Bibliographic record
Abstract
The mycotoxin deoxynivalenol (DON) caused by Fusarium spp. in cereal grains is a concern to the livestock industry, Current practices involve blending with clean grain to reduce overall DON content, but issues arise when clean grain has limited availability. These experiments were designed to determine: (1) if low DON wheat could be recovered from high DON wheat using specialized instruments; (2) the improvement of low DON grain recovery using two instruments in sequence and performance effects of sorted grain fed to broiler chickens; (3) the efficacy of gaseous ozone to reduce DON content in contaminated grains. The BoMill IQ (BoMill; BoMill AB, Sweden), a near infrared transmittance spectroscopy (NIT) single kernel sorter, was used with the Fusarium calibration to sort DON contaminated grain, as well as a GCS-200 grain cleaner (Grain Cleaning, LLC, MN, USA). In the first study, the BoMill was used to sort DON contaminated wheat and determine optimal calibration (Protein or Fusarium) and settings (HVK; hard vitreous kernel) to sort into eight fractions total, six sortable fractions with two unsortable fractions, and determine grain recovery under 5mg/kg DON of different wheats with different initial DON concentration. Experimental design was a CRD with four replications, data analyzed using least squares regression. The Fusarium calibration HHVK setting had 50.3% (P = 0.015), 29.0% (P = 0.001), and 1.8mg/kg (P = 0.068) recovered low DON wheat, rejection rate, and DON content respectively, which were intermediate to other settings and optimal compared to Protein calibration HVK setting 46.2% (P = 0.005) recovery, 35.6% (P = 0.001) rejection rate, and 1.6mg/kg (P = 0.001) DON content. Grain recovery (P<0.001) and rejection rate (P = 0.001) are dependent on initial DON concentration when sorting different sources of DON contaminated wheat. Using the BoMill technology could be an effective solution for regions with high DON incidence. In the second study a GCS-200 grain cleaner was used to sort and clean DON contaminated wheat by grain density, the intermediate DON content fraction was then sorted using the BoMill with optimal settings from the first study. Sorted and unsorted grain was used to formulate diets with graded amounts of DON for broiler birds to evaluate performance and intestinal morphology. Experimental design for grain sorting was a CRD with four replications. Experimental design for the broiler study was an RCBD with a 2 x 4 +1 factorial with ten replications. Fractional pneumatic separation grain recovery was 52.8, 42.7, and 58.8% with an average DON of 1.7, 1.5, and 1.0 mg/kg for three different initial DON wheats. Sequential sorting increased grain recovery between 10.5 to 19%. Dietary DON did not exceed 3.05 mg/kg. Bird performance and intestinal morphology were not different overall between treatments. In the third study, DON contaminated wheat was treated with gaseous ozone at different moisture contents and temperatures. Grain moisture was adjusted to 10.8, 15 or 20% moisture and grain treated with gaseous ozone for 24 hours at room temperature, then 20% moisture content wheat was temperature adjusted to 2°C or 29°C and treated with gaseous ozone for 24 hours. Statistical analysis was performed with one-way ANOVA using JMP. Increasing grain moisture content improved ozone degradation of DON by 6.7, 20.2, and 38.2% (P = 0.011) for 10.8, 15, and 20% moisture grain respectively. Temperature did not affect DON degradation when grain moisture was 20% (P > 0.10). Ozone can reduce DON content of wheat by 20 to 38% of wheat when grain moisture is above 15%. The key findings of the experiments were that the BoMill NIT Fusarium calibration can effectively sort and remove Fusarium damaged grain creating a fraction with reduced DON content. Sorting DON contaminated grain sequentially using a GCS-200 then BoMill improves grain recovery of low DON wheat. Sorted wheat can be used in broiler feed. Treating wheat with ozone can reduce the DON concentration at higher moisture levels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".