Quality Assurance of Foods and Functional Ingredients Using Quantitative NMR Methods and Chemometrics
Bibliographic record
Abstract
One of the greatest challenges facing the functional food and Natural Health Products (NHPs) industries is sourcing high quality functional ingredients for their finished products. Increasingly consumers are demanding full transparency for the products they consume regarding their quality, source and how they are made. Unfortunately, the lack of ingredient standards, modernized analytical methodologies and industry oversight creates the potential for low quality and in some cases deliberate adulteration of ingredients. DNA barcoding has emerged as one tool but its suitability for processed foods and functional ingredients has not been established. Due to its excellent quantitative properties, NMR spectroscopy is increasingly being used as an innovative solution to warrant the quality and safety of processed foods and manufactured functional ingredients. The NRC has been partnering with the industry to develop alternative analytical methods to capture the complex chemical composition of raw materials and extracts into a “chemical barcode”. Supported by statistical methodologies, a non-directed chemical approach to evaluate ingredients quality provide a key advantage in the ability to detect and quantitate in the same analysis, the presence of both the expected bioactives as well as any potential adulterants that are presumed to be absent. This presentation will introduce these concepts and show their application to a diverse range of extracts and foods (more than 200 ingredients) illustrating how quantitative NMR spectroscopy and chemometrics are being used to classify and improve the quality assurance of these products.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".