A Laser-Based Method for the Detection of Honey Adulteration
Dimitrios Stefas, Nikolaos Gyftokostas, Panagiotis Kourelias, Eleni Nanou, Vasileios Kokkinos, Christos Bouras, Stelios Couris
Abstract
In the present work, laser-induced breakdown spectroscopy, aided by some machine learning algorithms (i.e., linear discriminant analysis (LDA) and extremely randomized trees (ERT)), is used for the detection of honey adulteration with glucose syrup. In addition, it is shown that instead of the entire LIBS spectrum, the spectral lines of inorganic ingredients of honey (i.e., calcium, sodium, and potassium) can be also used for the detection of adulteration providing efficient discrimination. The constructed predictive models attained high classification accuracies exceeding 90% correct classification.
Topics & Concepts
Linear discriminant analysisLaser-induced breakdown spectroscopyArtificial intelligencePattern recognition (psychology)MathematicsChemistryMachine learningChromatographyLaserComputer sciencePhysicsOpticsLaser-induced spectroscopy and plasmaBee Products Chemical AnalysisCultural Heritage Materials Analysis