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<p>Honey adulteration represents a significant global issue that compromises consumer trust, threatens food safety, and impacts trade integrity. Conventional physicochemical and chromatographic methods, although effective for identifying simple adulterants, often fail to detect complex or subtle adulteration. In the last five years, significant advancements have been made through the integration of spectroscopic techniques, including near-infrared (NIR), Raman, and Fourier-transform infrared (FTIR) spectroscopy, with machine learning (ML) and deep learning (DL) models. The techniques exhibit significant sensitivity, with recent studies demonstrating classification accuracies of 97–100% and the capacity to quantify adulterant levels to 3–5%. Portable NIR and fluorescence spectroscopy devices facilitate prompt, non-invasive analysis. Blockchain and Internet of Things (IoT) technologies provide concurrent traceability throughout the supply chain. This assessment integrates traditional and modern analytical techniques, highlighting their precision, practicality, and sustainability. AI-enhanced spectroscopy and hybrid multimodal systems represent significant future opportunities for achieving rapid, cost-effective, and scalable authentication of honey. Present challenges encompass model generalizability, global data harmonization, and validation in practical contexts. The development of spectroscopic–AI hybrids represents notable progress toward intelligent, traceable, and decentralized detection systems, enhancing authenticity and transparency within the global honey industry.</p>

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Honey adulteration detection: a comprehensive review of traditional and modern techniques

  • 期刊论文

  • 发表日期：

    2026年2月25日

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  • Aroni Preya Biswas,
  • Mahmuda Tasnim,
  • Özge Süfer,
  • Sagar Chandra Das,
  • Shaswaty Sarker,
  • Min Zhang,
  • Nahidul Islam

摘要

Honey adulteration represents a significant global issue that compromises consumer trust, threatens food safety, and impacts trade integrity. Conventional physicochemical and chromatographic methods, although effective for identifying simple adulterants, often fail to detect complex or subtle adulteration. In the last five years, significant advancements have been made through the integration of spectroscopic techniques, including near-infrared (NIR), Raman, and Fourier-transform infrared (FTIR) spectroscopy, with machine learning (ML) and deep learning (DL) models. The techniques exhibit significant sensitivity, with recent studies demonstrating classification accuracies of 97–100% and the capacity to quantify adulterant levels to 3–5%. Portable NIR and fluorescence spectroscopy devices facilitate prompt, non-invasive analysis. Blockchain and Internet of Things (IoT) technologies provide concurrent traceability throughout the supply chain. This assessment integrates traditional and modern analytical techniques, highlighting their precision, practicality, and sustainability. AI-enhanced spectroscopy and hybrid multimodal systems represent significant future opportunities for achieving rapid, cost-effective, and scalable authentication of honey. Present challenges encompass model generalizability, global data harmonization, and validation in practical contexts. The development of spectroscopic–AI hybrids represents notable progress toward intelligent, traceable, and decentralized detection systems, enhancing authenticity and transparency within the global honey industry.