Development of safe storage guidelines for prairie-grown flaxseed (Linum usitatissimum) and characterization of stored seeds using near infrared spectroscopy
Bibliographic record
Abstract
Canada is the largest producer of flaxseed in the world with a production of 483.2 thousand tonnes in 2019. Maintaining the quality of huge amounts of seeds especially under constantly varying environmental conditions is an ongoing challenge. Therefore, the current research focuses on developing safe storage guidelines for flaxseed, which can help farmers plan proper post-harvest operations to avoid losses. Locally sourced flaxseed was conditioned to moisture contents of 7, 8, 9, and 13% and stored in relative humidity (RH) conditions of 54, 65, 75, and 94% at temperatures of 10, 20, and 30℃ for 16 weeks. The indices of spoilage were seed germination, free fatty acid value (FAV), visible mold, and protein content. The influence of temperature, RH, and storage period on germination rate and FAV was statistically significant (P = 0.05). The protein content did not change significantly with relative humidity, but a significant change was observed due to temperature. The seeds must be dried within 3 weeks of storage if it is to be stored at a moisture content of 13% or above and at temperatures over 20 °C. Samples at 10 and 20°C can be safely stored for at least 16 weeks if MC is maintained between 7 and 9%. Another goal of this study is to use non-destructive imaging techniques of visible near-infrared (Vis-NIR; 450-1100 nm) and shortwave infrared (SWIR; 1000-2500 nm) hyperspectral imaging for stored flaxseed quality assessment. Principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and partial least squares regression (PLSR) models were developed to model flax quality. Satisfactory groupings were demonstrated through PCA models in the Vis-NIR range based on storage period and temperature. PCA in the SWIR range distinguished flaxseed based on initial MC. The PLS-DA model achieved classification accuracies of 81.5, 72.8, and 87.5 % in calibration, cross-validation, and external prediction for flaxseed based on initial MC, respectively. The PLSR prediction model for MC and FAV yielded reliable results but not for predicting protein content and germination rate. As a result, it was established that NIRS may be utilized by food processors and farmers to analyze flaxseed quality non-destructively.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".