Analysis of Silver Nanoparticles in Ground Beef by Single Particle Inductively Coupled Plasma Mass Spectrometry (SP-ICP-MS)
Bibliographic record
Abstract
The regulation and characterization of nanomaterials in foods are of great interest due to the potential risks associated with their exposure and the increasing number of applications where they are used within the food industry. One factor limiting the scientifically rigorous regulation of nanoparticles in foods is the lack of standardized procedures for the extraction of nanoparticles (NPs) from complex matrices without alteration of their physico-chemical properties. To this end, we tested and optimized two sample preparation approaches (enzymatic- and alkaline-based hydrolyses) in order to extract 40 nm of Ag NP, following their equilibration with a fatty ground beef matrix. NPs were characterized using single particle inductively coupled plasma mass spectrometry (SP-ICP-MS). Fast sample processing times (<20 min) were achieved using ultrasonication to accelerate the matrix degradation. NP losses during the sample preparation were minimized by optimizing the choice of enzymes/chemicals, the use of surfactants, and the product concentration and sonication. The alkaline approach using TMAH (tetramethylammonium hydroxide) was found to have the highest recoveries (over 90%); however, processed samples were found to be less stable than the samples processed using an enzymatic digestion based upon pork pancreatin and lipase (≈60 % recovery). Low method detection limits (MDLs) of 4.8 × 106 particles g−1 with a size detection limit (SDL) of 10.9 nm were achieved for the enzymatic extraction whereas an MDL of 5.7 × 107 particles g−1 and an SDL of 10.5 nm were obtained for the alkaline hydrolysis.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".