{
  "abstract": "To achieve accurate and high-efficient quantitative detection of three serum biochemical indicators (SBIs), namely glucose (GLU), triglycerides (TG), and total cholesterol (TC), this study proposes a bimodal spectroscopy method integrating multi-wavelength time-resolved photoacoustic spectroscopy (TR-PAS) with near-infrared spectroscopy (NIRS), combined with a deep learning (DL) approach. A total of 35 characteristic wavelengths (12 for GLU, 13 for TG, and 10 for TC) are determined from the energy-corrected photoacoustic (PA) peak-to-peak value spectra of biochemical standard solutions by integrating three wavelength selection methods. NIR spectra and time-resolved PA signals at the selected wavelengths are experimentally collected from 1084 serum samples. Following data augmentation for the bimodal data and data preprocessing for NIR spectra, a dual-branch DL network, namely DB-CNN-LSTM-MAM model, was established to quantitatively predict GLU, TG, and TC, respectively. Through optimization of the model structure and parameters, the root mean square error of prediction (RMSEP) and coefficient of determination (R p ²) for the testing set were as follows: GLU: 0.9653 mmol/L, 0.9296; TG: 0.3956 mmol/L, 0.9575; TC: 0.4443 mmol/L, 0.9290. The generalization of the established DL model is verified by using 10-fold cross validation, and the external independent samples. The quantitiative performances of three SBIs are compared with other DL models. To validate the effectiveness of the proposed method, the comparisons are also performed between the bimodal spectroscopy and unimodal spectroscopy, as well as between the multi-wavelength TR-PAS–NIRS and the single-wavelength TR-PAS–NIRS. The study results indicate that the proposed method provides an accurate and highly efficient candidate solution for the quantitative detection of SBIs of ex vivo serum.",
  "abstract_shown": true,
  "affiliations": [
    "Key Laboratory of Advanced Electronic Materials and Devices of Jiangxi Province, Jiangxi Science and Technology Normal University, Nanchang, Jiangxi 330038, China",
    "Jiangxi Province Key Laboratory of Immunology and Inflammation, Jiangxi Provincial Clinical Research Center for Laboratory Medicine, Department of Clinical Laboratory, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330038, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "name": "Zhong Ren"
    },
    {
      "affs": [
        0
      ],
      "name": "Chaojun Chen"
    },
    {
      "affs": [
        0
      ],
      "name": "Gaoqiang Liang"
    },
    {
      "affs": [
        1
      ],
      "name": "Haibin Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "Weinan Shi"
    },
    {
      "affs": [
        0
      ],
      "name": "Jia Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "Guohui Xiao"
    },
    {
      "affs": [
        0
      ],
      "name": "Xiaoyu Zhu"
    },
    {
      "affs": [
        0
      ],
      "name": "Wenyan Nie"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/b6q141ezea4ss.bib",
  "cited_by": 0,
  "doi": "10.1016/j.pacs.2026.100858",
  "fulltext_url": "http://www.sciencedirect.com/science/article/pii/S2213597926000649",
  "id": "b6q141ezea4ss",
  "issns": [
    "2213-5979"
  ],
  "landing_url": "https://doi.org/10.1016/j.pacs.2026.100858",
  "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": [
    [
      "lcc",
      "QC1-999"
    ],
    [
      "lcc",
      "QC221-246"
    ],
    [
      "lcc",
      "QC350-467"
    ],
    [
      "openalex",
      "1304|Spectroscopy Techniques in Biomedical and Chemical Research"
    ]
  ],
  "openalex": "W7167916638",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-29",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/b6q141ezea4ss.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T14:22:26Z",
      "native_id": "07e0c0ad2bf848edb2ef27ed979e276d",
      "source": "doaj",
      "url": "https://doi.org/10.1016/j.pacs.2026.100858"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1304|Spectroscopy Techniques in Biomedical and Chemical Research",
      "subfield": "biophysics"
    }
  ],
  "title": "Bimodal spectroscopy integrating multi-wavelength time-resolved photoacoustic spectroscopy and near-infrared spectroscopy with deep learning for quantitative detection of serum biochemical indicators",
  "title_text": "Bimodal spectroscopy integrating multi-wavelength time-resolved photoacoustic spectroscopy and near-infrared spectroscopy with deep learning for quantitative detection of serum biochemical indicators",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T14:22:26Z",
  "url": "https://research.draconitic.com/paper/b6q141ezea4ss",
  "venue": "Photoacoustics",
  "versions": []
}