Development of a method based on <scp>ATR‐FTIR</scp> spectroscopy to detect maple syrup adulterated with added syrups
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Food adulteration is a global concern, whether it takes place intentionally or incidentally. In Canada, maple syrup is susceptible to being adulterated with cheaper syrups such as corn, beet, cane syrups, and many more due to its high price and economic importance. RESULTS: in the absorbance unit. These spectra were used to build six libraries and three models. A method that is capable of performing a qualitative library search using a similarity search, which is based on the first derivative correlation search algorithm, was developed. This method was further evaluated and proved to be able to capture adulterated and reject non-adulterated maple syrups, belonging to the color grades golden and amber maple syrups, with an accuracy of 93.9% and 92.3%, respectively. However, for the maple syrup belonging to the dark color grade, this method demonstrated low specificity of 33.3%, and for this reason it was only able to adequately detect adulterated samples from the non-adulterated ones with an accuracy of 81.4%. CONCLUSION: This simple and rapid method has strong potential for implementation in different stages of the maple syrup supply chain for early adulteration detection, particularly for golden and amber samples. Further evaluation and improvements are required for the dark color grade. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it