Establishment of novel standardised operating procedures for LF‐NMR: used in rapid detection of typical fruit and vegetable
Bibliographic record
Abstract
Summary Although LF‐NMR has been widely used in many fields of food science, the lack of NMR expertise of many food researchers needs to be bridged. Using samples of carrots, bananas, and king oyster mushroom, this work systematically studied the influence of LF‐NMR parameters (repetition time (TR), echo time (TE)) on experimental results. The result showed that the TR of carrot, mushroom and banana were 2684, 4572 and 4317 ms, respectively. The rate of change of signal amplitude can be used as an index to optimise TR. The change of TE has different effects on the short relaxation components of the T 2 distributions and long relaxation components in different TR samples. TE and Nech should be set according to T 2 to record completely the decay process. What’s more, the method of normalised CPMG decay was suggested to eliminate the effects of parameters such as number of scan (NS), pre‐amp regulate gain (PRG) and mass of sample. At last, the standardised operating procedures were proposed to provide a strategy of improving the properties and applications of LF‐NMR.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".