Development of innovative techniques for food authentication - the last barrier to prevent food fraud
Bibliographic record
Abstract
Food fraud was estimated to cost the global food industry $10-15 billion per year. Various traceability and risk assessment systems have been developed to deter food fraud. With rapid globalization and complex supply chain, effective product tracing and tracking and accurate vulnerability assessment have been inevitably hindered. Serving as the last barrier to ensure food authenticity, reliable techniques to identify fraudulent foods are indispensable. Traditional techniques (e.g. liquid chromatography-based assays) are usually time-consuming, labor- intensive, lack the sensitivity and/or specificity, and/or complicated. Therefore, the overall objective of my Ph.D. thesis project was to validate the feasibility of spectroscopic techniques [i.e. Raman, mid-infrared, and nuclear magnetic resonance (NMR) spectroscopies] and an advanced DNA amplification method [i.e. loop-mediated isothermal amplification (LAMP)] to detect food fraud. During my studies, an optimized protocol was developed to authenticate ground beef meat and identify and quantify the offal adulterants using mid-infrared spectroscopy coupled with chemometric models, providing a limit of detection (LOD) <10% w/w of offal in ground beef meat. With simple/no sample pretreatment, solution NMR spectroscopy and solid-state NMR spectroscopy were confirmed to detect 6.7 and 128.6 mg/kg Sudan I in paprika powder in <30 min, respectively. A confocal micro-Raman spectrometer and a portable Raman spectrometer were applied to identify 11 species of raw finfish purchased from seafood markets in Vancouver. The method developed using the portable Raman spectrometer exhibited promising results with a 100% accuracy in differentiating Salmonidae and non-Salmonidae, 88% accuracy in identifying four species of salmon, and 91% accuracy in separating the seven species of non-Salmonidae fish. A LAMP-based assay was developed to authenticate pure pomegranate juice from juice adulterated by apple and/or grape juice. With a novel paper-based DNA extraction device and a simple LAMP result visualization method, the overall sample-to-result analysis was completed in ~1 h with a LOD of 10~100 ng of DNA. Methods and devices developed in my studies may be used to authenticate many other food commodities and have the potential to be adopted by governmental laboratories, food industries and even consumers to rapidly authenticate food products, and thus better ensure the integrity of foods.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".