Precision Nutrition

See What's
Really In Your Food

Advanced computer vision and scientific datasets estimate microplastic contamination in your meals. Empowering consumers through molecular transparency.

verified_userDOI-Verified SourcessciencePeer Reviewed

See It In Action

Upload a meal or select a sample case study.

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Or try a pre-analyzed example
Meal Total
~0particles
range: 32–198 particles
0%
of daily average
check_circleLow Risk

≈ 2 glasses of tap water

Chicken Nuggets

verifiedHigh ConfidenceTier A · ≥45μm
Highly ProcessedSource: Milne et al. 2024
~0
PARTICLES
MIN (8)MAX (120)
tips_and_updates
  • Swapping to grilled breast meat reduces detected particles by ~80%
  • Air frying home-cut potatoes further minimizes microplastic exposure
310 cal15g protein18g carbs20g fat

French Fries

verifiedMedium ConfidenceTier A · ≥45μm
Minimally ProcessedSource: Milne et al. 2024 (extrapolated)
infoEstimated from processed potato products data
~0
PARTICLES
MIN (14)MAX (104)
tips_and_updates
  • Cut and fry potatoes at home to avoid factory processing contamination
  • Use glass or ceramic containers instead of paper-lined cardboard
365 cal4g protein48g carbs17g fat
Recommended Actions
  • ecoSwap plastic containers for glass or ceramic
  • ecoAvoid reheating food in plastic containers
  • ecoIncrease share of whole fruits over packaged snacks

These estimates are based on peer-reviewed research and statistical modeling. Actual microplastic content may vary based on brand, origin, preparation method, and local environmental conditions. This is not medical advice.

Scientific Pipeline

photo_camera

1. Optical Recognition

System identifies specific food matrix and packaging materials from pixels.

database

2. Dataset Matching

Cross-referencing global research on microplastic concentrations in identified matrices.

insights

3. Statistical Modeling

Monte Carlo simulations account for variance in processing and environmental factors.

What makes PlastiScan different?

High-Resolution Accuracy — combining visual detection with peer-reviewed inference to quantify what others can only estimate.

verifiedReal-time DOI updates
verifiedAcademic oversight
verifiedTransparency first
verifiedGlobal food mapping
MOLECULAR SCAN

Transparent Methodology

Quantifying the invisible with rigorous cross-study synthesis.

ATier A: EmpiricalverifiedHigh Confidence
> 45 μm·Visual + Raman/FTIR Spectroscopy
BTier B: ExtendedverifiedMedium Confidence
~10–100 μm·μ-FTIR
CTier C: Micro-scaleverifiedMedium Confidence
1–5 μm·SEM-EDX
DTier D: Nano-scaleverifiedLow Confidence
< 1 μm·SRS / SEM Microscopy
MTier M: Mass-basedverifiedMedium Confidence
N/A (mass)·Pyrolysis-GC/MS
report

Why Scaling Matters

Research shows particle counts scale exponentially as detection limits decrease. A study detecting at 1.5µm finds orders of magnitude more than one at 45µm — even in the same sample. PlastiScan accounts for these variations to provide balanced “Composite Estimates.”

bookKey References

Milne, R.I. et al. (2024) — Environmental Science & Technologyarrow_forward
Oliveri Conti, G. et al. (2020) — Environmental Researcharrow_forward
Dessì, C. et al. (2021) — Food Additives & Contaminants: Part Aarrow_forward
Kim, J.S. et al. (2018) — Environmental Science & Technologyarrow_forward
Qian, N. et al. (2024) — Science Advancesarrow_forward
Cox, K.D. et al. (2019) — Environmental Science & Technologyarrow_forward
Hernandez, L.M. et al. (2019) — Environmental Science & Technologyarrow_forward
Hussain, K.A. et al. (2023) — Environmental Toxicology and Pharmacologyarrow_forward
Luo, Z. et al.; Yadav, S. et al. (2023) — Environmental Science & Technology; Science of the Total Environmentarrow_forward