{
  "abstract": "The rapid, non-destructive screening of leather authenticity requires analytical measurements that capture complementary volatile-response and microstructural information. This study developed RLFormer, a multimodal workflow combining cross-responsive metal-oxide-semiconductor electronic-nose (E-nose) fingerprints with microscopic surface images. Four classes—synthetic, pig, sheep, and cattle leather—were represented by 100 specimens each and measured during a standardized 10-day campaign. The relative standard deviations of the category-level daily mean E-nose responses ranged from 1.4 to 13.3% (global mean, 6.18%), and a variance-component analysis revealed a progressive between-day response attenuation common to all categories. A ResNet50 visual encoder and a two-layer long short-term memory (LSTM) encoder generated modality-specific representations that were integrated by a transformer encoder. With days 1–7 used for training and days 8–10 for testing, RLFormer achieved an accuracy of 97.67±2.16% and a Cohen’s kappa of 0.9689±0.0288 over five random seeds, compared with an 87.50±2.50% accuracy for direct ResNet50–LSTM fusion. The results demonstrate the value of combining non-targeted volatile-response patterns with microscopic texture for rapid leather classification. The workflow is intended for screening rather than compound-specific identification and requires independent validation before deployment.",
  "abstract_shown": true,
  "affiliations": [
    "College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China",
    "School of Biomedical Engineering, Capital Medical University, Beijing 100069, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Xiaoran Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Juan Chen"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Yu Gu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0yggf52m0cwky.bib",
  "cited_by": 0,
  "doi": "10.3390/molecules31213879",
  "fulltext_url": null,
  "id": "0yggf52m0cwky",
  "issns": [
    "1420-3049"
  ],
  "landing_url": "https://www.mdpi.com/1420-3049/31/21/3879",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2204|Advanced Chemical Sensor Technologies"
    ]
  ],
  "openalex": "W7223783377",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-27",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/0yggf52m0cwky.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:42Z",
      "native_id": "10.3390/molecules31213879",
      "source": "crossref",
      "url": "https://www.mdpi.com/1420-3049/31/21/3879"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "issn:1420-3049",
      "subfield": "general"
    }
  ],
  "title": "Non-Targeted Electronic-Nose Fingerprinting and Microscopic Imaging for Leather Classification via Transformer-Based Multimodal Fusion",
  "title_text": "Non-Targeted Electronic-Nose Fingerprinting and Microscopic Imaging for Leather Classification via Transformer-Based Multimodal Fusion",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:42Z",
  "url": "https://research.draconitic.com/paper/0yggf52m0cwky",
  "venue": "Molecules",
  "versions": []
}