Research progress of laser-induced breakdown spectroscopy combined with machine learning in honey authenticity assessment and quality identification

  • ZENG Fanti ,
  • LI Chunyu ,
  • ZHAO Xuejun
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  • 1(School of Criminal Investigation, People’s Public Security University of China, Beijing 100038, China)
    2(Shanghai Key Laboratory of Crime Scene Evidence, Shanghai Institute of Forensic Science and Technology, Shanghai 200072, China)

Received date: 2025-08-07

  Revised date: 2025-10-10

  Online published: 2026-06-15

Abstract

The authentication and quality evaluation of honey are essential for ensuring food safety and consumer protection.While traditional detection methods provide a technical foundation for honey quality monitoring, they are often limited by cumbersome sample preparation and low resolution.Laser-induced breakdown spectroscopy (LIBS), when combined with machine learning (ML), presents a powerful alternative.This approach enables the rapid, in situ analysis of honey’s elemental composition, leveraging its wide detection range, high sensitivity, and high spatial resolution to provide a robust basis for quality assessment.This review systematically examines the innovative applications of LIBS for identifying honey adulteration.It analyzes the applicability of various spectral preprocessing and data modeling methods for processing LIBS data and provides an outlook on the future application prospects and research trends in this field.

Cite this article

ZENG Fanti , LI Chunyu , ZHAO Xuejun . Research progress of laser-induced breakdown spectroscopy combined with machine learning in honey authenticity assessment and quality identification[J]. Food and Fermentation Industries, 2026 , 52(10) : 451 -460 . DOI: 10.13995/j.cnki.11-1802/ts.044157

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