Elucidation of changes in food grains due to spoilage in bulk storage using advanced imaging for post-harvest management
Bibliographic record
Abstract
Different varieties within a class of a grain type exhibit distinct behaviors that impact their end use quality during storage. Until now, high-resolution imaging data for both dry and spoiled cereal grains have not been available. Therefore, a pioneering effort was made to generate 3D data for a better understanding of seed structure and changes due to spoilage, focusing on the selected crops (wheat and barley). In the first preliminary experiment, durum wheat varieties (AAC Spitfire, CDC Defy, and AAC Stronghold) and spring wheat varieties (AAC Starbuck, AAC Brandon, Faller) were stored for 5 wk at 17% moisture content (wet basis (wb)). Both control (dry) and stored high-moisture seeds were analyzed for structural, and nutritional changes using synchrotron techniques, including phase-contrast micro-computed tomography (SR-μCT), bulk X-ray fluorescence imaging (SR-XRF), X-ray fluorescence imaging (SR-XFI), and mid-infrared spectroscopy (mid-IR) at the Canadian Light Source (CLS), Saskatoon, SK. Seeds were also stored in a freezer (-18°C) for further scanning to study any additional changes in the microstructure of seeds due to freezing. In this experiment, operational parameters were standardized for data acquisition for SR-μCT (20 keV, 3.6μ resolution, and 5 cm propagation distance), SR-XRF/XFI (15 keV, 5μ resolution, and 100 ms dwell time), and mid-IR (900–4000 cm-1 range at 4 cm-1 resolution). Image processing revealed changes in the microstructure of wheat with spoilage at the end of the 5-wk storage, and this remained unaltered after freezing in stored wheat. Bulk XRF demonstrated the impact of storage time on variations in available nutrients. The SR-XFI revealed significant changes (p<0.05) in nutritional distributions at the micron scale in thin section maps within stored durum wheat seeds. Changes in nutritional features (protein, lipids, and carbohydrates) due to spoilage during storage were determined using mid-IR spectroscopy. Durum varieties AAC Spitfire and CDC Defy exhibited maximum changes in microstructural and nutritional composition compared to AAC Stronghold, and all spring wheat varieties performed better than durum wheat varieties.In the second experiment, eight wheat varieties, each representing distinct wheat classes, were included: Canada Prairie Spring Red (CPSR) cultivar ‘AAC Penhold’, Canada Prairie Spring White (CPSW) cultivar ‘AC Vista’, Canada Western Extra Strong (CWES) cultivar ‘Burnside’, Canada Western Hard White Spring (CWHWS) cultivar ‘AC Snowstar’, Canada Western Red Winter (CWRW) cultivar ‘AAC Wildfire’, Canada Northern Hard Red (CNHR) cultivar ‘Prosper’, Canada Western Special Purpose (CWSP) cultivar ‘Aldoren’, and Canada Western Soft White Spring (CWSWS) cultivar ‘AC Andrew’. Additionally, four barley classes were considered: Tradition (Six-row), AB Cattlelac (Six-row hulless), Esma (Two-row), and AC Metacalf (Two-row hulless), each representing four different western malt barley classes. These grains were stored for 8 wk at 17% moisture content (wb). Data acquisition for both control (wheat and barley) and 8-wk stored grains (wheat and barley) was carried out using standardized operational parameters for SR-μCT, SR-XFI, and mid-IR. Characterization of wheat based on changes in the microstructure of selected wheat classes became possible. The wheat classes that performed better in storage were CNHR, CWRW, CWSP, and CPSW, compared to classes CWHWS, CPSR, CWSWS, and CWES. The existing condition of the kernel microstructure strongly influenced the performance of wheat in storage, representing a major outcome of this work. Mid-IR analysis of wheat data showed variations among nutritional components, while SR-XFI revealed nutrient distribution gradients before and after storage. Hulled barley varieties exhibited more deterioration in microstructure than hulless varieties of barley, establishing a direct correlation between microstructural changes and alterations in nutritional content. The initial condition of the grain structure of control samples (air spaces beneath the husk and cracks) strongly influenced the storage life of wheat and barley during the short-term storage of 8 wek The high-resolution information generated for selected barley varieties will be useful in the development of real-time decision support systems and planning post-harvest storage.
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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".