7.2
SciPi 770: Best Practices: Detecting and Quantifying Micro- Nanoplastics (MNP) in Biological Tissues
The MDA equation currently proposed would suggest that one can never be confident in characterizing a sample with fewer than 3 plastic particles. Would you agree with this assertion? (please explain)
Results
(9 Answers)
Experts largely agree that the MDA equation suggests a minimum of 3 plastic particles is needed for confident characterization, with 7 of 9 experts answering "Yes" to the assertion.
Areas of agreement:
- Statistical confidence requires at least 3 particles due to variability in composition, morphology, and potential contamination
- Fewer than 3 particles falls below a reliable detection limit
Key disagreements:
- Two experts noted the MDA equation is not applicable to all analytical methods, specifically mentioning it doesn't apply to mass spectrometry techniques like pyrolysis GC-MS
- One expert considered the term "never" too absolute, suggesting findings below 3 particles could still be reported with appropriate caveats
- Another expert questioned whether the specific cutoff of 3 particles has clear rationale
Summary Generated by AI
Answer Explanations
- YesExpert 9I think the MDA equation is statistically valid and leads to this conclusion that a minimum number of MPs are required to ensure confidence in their detection.
- YesExpert 1This statement is applicable for image based microplastic analysis. This is not applicable for mass spec based analysis involving pyrolysis GC-MS. I would again make a statement indicating that a particle number of 3 is the limit proposed for image based analysis. This is not applicable for GC-MS based methods.
- YesExpert 3These materials are highly variable in composition, morphology, additive composition and quantities, as well as (potential) sorbed chemicals; as such, 3 should be the absolute minimum.
- YesExpert 2Taking into consideration the MDA framework, and you are applying the MDA equation strictly, then according to it, fewer than approximately 3 particles fall below the defined detection limit, so you cannot make a statistically confident claim that the sample is distinguishable from zero meaning that the presence of fewer than three plastic particles in a sample does not permit the characterization with a statistical confidence. It is important, however, to make a distinction between the absence of quantifiable confidence and the absence of detection. While such findings may be reported as evidence of detection, they do not meet the threshold for quantitative reliability . Therefore, there are studies that report that the particles were detected but were not quantifiable according to MDA.
- YesExpert 7With fewer than 3 particles in the sample, statistical confidence would be low considering ubiquitous contamination and background noise.
- NoExpert 4I think that the inclusion of 'never' is a bit too harsh. When there is a case where no more than 3 plastic particles have been detected, the common practise could be to report this and then not to draw any strong conclusions related to this observation, but just report that less than 3 particles were observed in a specific sample.
In adition I would like to note that the number of 3 is not carved in stone and there is no clear rational for selecting this specific cut-off value. - YesExpert 5Particles of the same composition? (polymer, additives, entrained contaminants, weathering, etc.)
Particles of the same polymer with the same morphology?
There are so many microplastic particle variables that 3 particles 'identified' are only 'indicative' of possible micro and nano plastics present. Not a confident measurement of the number, identity, morphology, etc of the particles in the biological tissue/system sampled. - YesExpert 6I believe, if plastic particles are observed, they should follow a distribution pattern and has a population. With this concept, I will not be confident in charactering a sample with just one or two particles.
- NoExpert 8Pyrolysis GCMS doesnt count particles, so in that case the MDA isn't relevant.