# A Laser-Based Method for the Detection of Honey Adulteration
## 1 Introduction  
Honey is a sweet viscous substance made by bees by collecting nectar from the sweet secretions of trees and plants. Its major constituents are carbohydrates, with the main types of carbohydrates being sugars. The most common types of sugars found in honey are glucose and fructose, which account for honey's sweet flavor [1, 2, 3, 4]. Its consumption has been related with various health benefits and it is widely used as a sugar substitute. A major issue in the market is to tackle food fraud. Honey is one of the most commonly adulterated foods, since it is easy to adulterate with other types of syrup, or lower quality honey [5, 6, 7]. Some common techniques to evaluate and quantitively describe its adulteration is isotope ratio mass spectroscopy [8, 9], gas and liquid chromatography [10, 11], and near-infrared spectroscopy [12]. In this work, we investigate the use of a spectroscopic method, namely laser-induced breakdown spectroscopy (LIBS), for the detection of adulteration of honey samples with glucose syrup. LIBS is a laser-based method, where a focused laser beam is used to excite and ionize a sample, in one step, to produce a plasma on its surface that contains excited atoms, ions, electrons, and also various fragments of molecular species existing in the sample [13, 14]. The plasma, resulting from the interaction of the laser with the sample, emits radiation, which is short-lived, in general, and results from the de-excitation of the excited atoms and ions, the de-accelerating electron (bremsstrahlung), and the different recombination processes that can occur. The most important for the present study are the spectral lines from the de-excitations of atoms and ions, as well as some molecular bands from small molecules, in principle [15]. The main attribute of this method is that the plasma radiation emitted light can be recorded and spectrally analyzed, therefore providing a mean for qualitative and quantitative (under some conditions) analysis of thesample's elemental composition. So far, LIBS has been proposed and applied in many applications, ranging from the analysis of metals [16; 17], to combustion diagnostics [18], art works restoration/authentication [19], and, more recently, even in space exploration [20; 21]. Due to its ability to analyze highly inhomogeneous materials, LIBS has been proposed as an attractive method for elemental mapping and imaging [22]. Applications can be found in a wide range of studies, such as the analysis of historical mortar samples [23] and the detection of selenium enrichment in mushrooms [24]. Moreover, because LIBS can rapidly provide the results and can create large spectroscopic datasets, it is commonly combined with machine learning and chemometric algorithms for classification and calibration/regression purposes [25; 26; 27], providing new tools for the quantitative and qualitative analysis of spectroscopic data. In the last decade, LIBS has been also proposed for the analysis of foodstuff, e.g., for the detection of different substances in food [28; 29] and for classification of edible olive oils [30; 31; 32], milk [33; 34], and honey. Therefore, in a study by [PERSON] et al. [35], LIBS was used for the measurement of the concentrations of some Ca, Mg, and Na minerals using partial least square regression (PLSR). [PERSON] et al. [36] performed LIBS measurements supported by chaotic parameters to categorize six European honeys of diverse botanical origins as well as to detect samples mixed with rice syrup. In the same spirit, [PERSON] et al. [37] employed LIBS, aided by multivariate analytical approaches, to detect and quantify adulterants in honey samples. Similarly, with [37], acacia honey mixed with high-fructose corn syrup and rape honey were analyzed by LIBS in the work of [PERSON] et al. [38], in order to determine the characteristic spectral features of pure honey and adulterants and identify the feature variables that are related to adulteration, and finally to quantify the adulterant content using univariate and multivariate analysis.