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

Abstract

Given that honey is among the top ten foods with the highest adulteration rate in the European Union, in this research, a tool has been developed to tackle this malpractice. The combination of laser-induced breakdown spectroscopy (LIBS) and chaotic parameters has been employed to classify six European honeys of different botanical origins as well as detect samples containing the usually elusive rice syrup adulteration in weight concentrations as low as 2%.The profiles of the LIBS emission spectra can be used to faithfully classify honey in terms of botanical origin by combining information extracted directly from the spectra with simple linear modeling. In contrast, the detection of low amounts of rice syrup in honey is not as straightforward, which is why algorithms based on chaotic parameters such as shifted (lag-k) autocorrelation coefficients were employed to extract underlying information representative of adulterated samples. Since these algorithms are capable of detecting slight changes in the composition of honeys, it has been possible to identify these adulterations with a success rate greater than 90% when samples from honeys of different botanical origins are combined into the same model, and over 95% when individual honey types are analyzed.