{
  "abstract": "(CRP) have led to frequent counterfeiting. Rapid and non-destructive identification of counterfeit CRP is therefore essential. This study proposes a novel detection model that integrates visible/near-infrared (Vis/NIR) spectroscopy with deep learning algorithms. Authentic CRP and four types of counterfeit CRP samples were collected, and their multispectral Vis/NIR data were acquired. Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) data augmentation enhanced model generalization, further improving classification robustness. After spectral preprocessing, deep learning models were applied to classify and detect different CRP types, aiming to identify the optimal network for counterfeit discrimination. The experimental results demonstrated that the Inception-ResNet model achieved the test set accuracy of 96.56% in identifying authentic CRP from four counterfeit categories, outperforming conventional machine learning approaches. This study provides a precise and efficient model for non-destructive authentication and adulteration analysis of CRP.",
  "abstract_shown": true,
  "affiliations": [
    "College of Information Engineering Henan University of Science and Technology Luoyang China",
    "College of Horticulture and Plant Protection Henan University of Science and Technology Luoyang China",
    "College of Food and Bioengineering Henan University of Science and Technology Luoyang China",
    "College of Computing and Data Science Nanyang Technological University Singapore Singapore",
    "Complex Systems Intelligent Control Laboratory Shanghai Jiao Tong University Shanghai China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Chao Ma",
      "orcid": "0000-0002-4257-1149"
    },
    {
      "affs": [
        0
      ],
      "name": "Mingkun Zhang",
      "orcid": "0000-0003-2797-8781"
    },
    {
      "affs": [
        0
      ],
      "name": "Jianwei Ma"
    },
    {
      "affs": [
        1
      ],
      "name": "Sudan Chen",
      "orcid": "0000-0002-2871-6540"
    },
    {
      "affs": [
        2
      ],
      "name": "Yunxia Yuan"
    },
    {
      "affs": [
        3
      ],
      "name": "Mingtong Du"
    },
    {
      "affs": [
        4
      ],
      "name": "Zhiyong Dai",
      "orcid": "0000-0003-1101-8647"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/48mmdnz7hzr1a.bib",
  "cited_by": 8,
  "doi": "10.1002/fsn3.71975",
  "fulltext_url": null,
  "id": "48mmdnz7hzr1a",
  "issns": [
    "2048-7177"
  ],
  "landing_url": "https://doi.org/10.1002/fsn3.71975",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1602|Spectroscopy and Chemometric Analyses"
    ]
  ],
  "openalex": "W7163424874",
  "pdf_url": null,
  "pmcid": "PMC13239362",
  "pmid": "42255696",
  "published": "2026-06-01",
  "publisher": "Wiley",
  "ris": "https://research.draconitic.com/paper/48mmdnz7hzr1a.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:26:44Z",
      "native_id": "W7163424874",
      "source": "openalex",
      "url": "https://doi.org/10.1002/fsn3.71975"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:1602|Spectroscopy and Chemometric Analyses",
      "subfield": "analytical"
    }
  ],
  "title": "Counterfeit Citri Reticulatae Pericarpium Identification: Multi‐Class Detection Using Vis/ NIR Spectroscopy and Residual Neural Networks",
  "title_text": "Counterfeit Citri Reticulatae Pericarpium Identification: Multi‐Class Detection Using Vis/ NIR Spectroscopy and Residual Neural Networks",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T13:21:03Z",
  "url": "https://research.draconitic.com/paper/48mmdnz7hzr1a",
  "venue": "Food Science & Nutrition",
  "versions": []
}