Honey exposed to laser-induced breakdown spectroscopy for chaos-based botanical classification and fraud assessment

Abstract

This record corresponds to a peer-reviewed Q1 Journal Citation Reports (JCR) indexed journal article published in Chemometrics and Intelligent Laboratory Systems (Volume 199, 2020).

This work presents an innovative analytical methodology for the botanical classification of honey and the detection of food fraud through the integration of laser-induced breakdown spectroscopy (LIBS) and advanced chaos-based chemometric modeling. The study addresses one of the most challenging problems in food authentication, namely the identification of low-level adulteration of honey with rice syrup, a C3-plant-derived adulterant that is particularly difficult to detect using conventional analytical techniques.

LIBS was employed to generate high-dimensional elemental emission spectra from honey samples of different botanical origins. To extract meaningful discriminatory information from these complex spectral datasets, two complementary mathematical strategies were developed and evaluated: feature selection using the Relief-F algorithm and the computation of chaotic parameters based on spectral autocorrelation coefficients. These chaos-based descriptors proved especially sensitive to subtle compositional variations, enabling robust classification and adulteration detection.

The proposed methodology achieved classification accuracies above 95% for botanical origin discrimination and successfully detected rice syrup adulteration at concentrations as low as 2% (w/w), with detection rates exceeding 90% and further improvements when individual honey varieties were modeled independently. The approach demonstrated superior performance compared to conventional linear feature-based models, highlighting the added value of chaos theory for spectroscopic data interpretation.

This study establishes a novel, rapid, and minimally destructive framework for food authentication, combining LIBS with advanced chemometric and nonlinear analysis tools. The results demonstrate the strong potential of the proposed methodology for real-time quality control and fraud detection in the food sector, with applicability beyond honey to other complex food matrices.