Quality Assurance of Foods and Functional Ingredients Using Quantitative NMR Methods and Chemometrics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.000 | 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