{
  "abstract": "Rapid assessment of chilled-meat freshness is constrained by conventional laboratory-based measurements. Although food-detection studies emphasize predictive performance, computational cost is reported less consistently. Here, we developed a 2 × 2 colorimetric sensor array based on a porous konjac glucomannan (KGM)/gelatin/starch/SiO₂ aerogel substrate loaded with four pH-sensitive dyes. Sensor images were acquired with a smartphone under standardized conditions, and four dye-responsive regions of interest (ROIs) were manually extracted and assembled into 2 × 2 dye-grid images. Five lightweight models-Spot-CNN, ShuffleNetV2-0.5x, EfficientNet-Lite0, MobileNetV3-Small and Tiny-MLP-were evaluated for classifying beef and pork freshness as Fresh, Sub-fresh or Spoiled. During chilled storage, the sensor color responses showed broadly consistent temporal trends with total volatile basic nitrogen (TVB-N). Under the fixed group-level split, Spot-CNN achieved image-unit-level accuracies of 0.933 and 0.938 and macro-AUC values of 0.995 and 0.982 on the beef and pork test sets, respectively. Tiny-MLP contained 0.013 M parameters and required less than 0.001 G floating-point operations, demonstrating lower computational cost. These results establish a controlled-laboratory prototype integrating an aerogel colorimetric sensor array, standardized smartphone image processing and lightweight-model comparison for multi-meat freshness screening. Further validation across meat batches, smartphone devices, lighting conditions and deployment environments is required.",
  "abstract_shown": true,
  "affiliations": [
    "College of Engineering, Huazhong Agricultural University, Wuhan 430070, China.",
    "Ningbo Academy of Agricultural Sciences, Ningbo 315040, China.",
    "School of Life and Health Sciences, Hubei University of Technology, Wuhan 430068, China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Hanzhi Chen"
    },
    {
      "affs": [
        1
      ],
      "name": "Xiao Zhou"
    },
    {
      "affs": [
        2
      ],
      "name": "Caoyuan Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "Zengrui Wu"
    },
    {
      "affs": [
        2
      ],
      "name": "Bo Peng"
    },
    {
      "affs": [
        0
      ],
      "name": "Yingli Wang",
      "orcid": "0000-0001-8747-6068"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/af1ssbdnrejq1.bib",
  "cited_by": 0,
  "doi": "10.3390/gels12100910",
  "fulltext_url": "https://europepmc.org/articles/PMC13704372",
  "id": "af1ssbdnrejq1",
  "issns": [
    "2310-2861"
  ],
  "landing_url": "https://europepmc.org/article/MED/42929944",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1103|Meat and Animal Product Quality"
    ]
  ],
  "openalex": "W7221354374",
  "pdf_url": "https://europepmc.org/articles/PMC13704372?pdf=render",
  "pmcid": "PMC13704372",
  "pmid": "42929944",
  "published": "2026-10-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/af1ssbdnrejq1.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:01Z",
      "native_id": "MED:42929944",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42929944"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1103|Meat and Animal Product Quality",
      "subfield": "zoology-behaviour"
    }
  ],
  "title": "Smartphone-Assisted Aerogel Colorimetric Sensor Array Coupled with Lightweight Deep Learning for Multi-Meat Freshness Monitoring.",
  "title_text": "Smartphone-Assisted Aerogel Colorimetric Sensor Array Coupled with Lightweight Deep Learning for Multi-Meat Freshness Monitoring.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:22:01Z",
  "url": "https://research.draconitic.com/paper/af1ssbdnrejq1",
  "venue": "Gels (Basel, Switzerland)",
  "versions": []
}