Pith Number

pith:B74HQEQC

pith:2026:B74HQEQC5MJMLIJCZUNQTOLGUZ

not attested not anchored not stored refs pending

Explainable deep-learning detection of microplastic fibers via polarization-resolved holographic microscopy

Giulia Dalla Fontana, Jan Appel, Jarom\'ir B\v{e}hal, Lisa Miccio, Marika Valentino, Miroslav Je\v{z}ek, Pietro Ferraro, Raffaella Mossotti, Vittorio Bianco

Polarization eigen-parameters from holographic microscopy let a neural network classify microplastic fibers at 96.7 percent accuracy.

arxiv:2601.15769 v3 · 2026-01-22 · physics.optics · physics.data-an

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Claims

C1strongest claim

The designed fully connected deep neural network achieved an accuracy of 96.7 % on the validation data, surpassing that of common machine-learning classifiers. An additional reduced-feature model with the preserved architecture exploiting only these most significant eigenvalue-based characteristics retained high accuracy (93.3 %).

C2weakest assumption

That the 296 laboratory-prepared fibers and their extracted polarization descriptors are representative of the diversity, degradation states, and confounding factors (size, orientation, surface contamination) encountered in real environmental samples.

C3one line summary

A deep neural network classifies six types of microplastic and natural fibers using 72-dimensional polarization feature vectors from holographic microscopy at 96.7% accuracy, with SHAP analysis showing eigenvalue ratios as the dominant predictors.

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-06-01T01:02:29.752422Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

0ff8781202eb12c5a122cd1b09b966a661709234b2c46326f6ca8bd53dca877e

Aliases

arxiv: 2601.15769 · arxiv_version: 2601.15769v3 · doi: 10.48550/arxiv.2601.15769 · pith_short_12: B74HQEQC5MJM · pith_short_16: B74HQEQC5MJMLIJC · pith_short_8: B74HQEQC

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/B74HQEQC5MJMLIJCZUNQTOLGUZ \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 0ff8781202eb12c5a122cd1b09b966a661709234b2c46326f6ca8bd53dca877e

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "1dce4e206ffdd62af86700f404a7b45b32d78b5fe151002db0318ae868ef93e7",
    "cross_cats_sorted": [
      "physics.data-an"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "physics.optics",
    "submitted_at": "2026-01-22T08:59:55Z",
    "title_canon_sha256": "e0b60406c32622496a568cf209c7eed3fb55d28b3c6590cbb2fa47c6385361ea"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2601.15769",
    "kind": "arxiv",
    "version": 3
  }
}