# Application of an Electronic Nose as a New Technology for Rapid Detection of Adulteration in Honey
## 1 Introduction
Due to the presence of volatile species and its characteristic olfactory pattern, studies on the characterization of honey with e-noses have been described. There are several works in which e-noses have been used to differentiate between honey according to botanic and geographical origins [19; 20; 21], which may also be useful for fraud prevention, since mislabeling is also common. Focusing on adulteration by the addition of components different than honey, [PERSON] et al. studied a method for discrimination between pure Tualang honey and adulteration induced with sugar solutions (from beetroot and sugarcane), reducing the concentrations of pure honey gradually (80%, 60%, 40%, 20%, and 0%). Two techniques were applied, an e-nose and infrared spectroscopy. The data generated by the two techniques were pre-processed using the relative responses or the normalized data and evaluated using different multivariate analyses. Using the merged data from the e-nose and infrared spectroscopy, followed by linear discriminant analysis (LDA), the authors achieved an accuracy of 92.2% for the proposed methodology [22]. [PERSON] et al. demonstrated the ability to discriminate and classify honey of different botanical origins, sugar syrup, and adulterated samples using a combination of an e-nose and an electronic tongue (e-tongue). The authors highlighted the importance of data curation using different classifiers to assign samples correctly. Since the nature of the data from the e-nose and e-tongue was non-linear, the probabilistic neural network (PNN) classifier performed better [23]. Other studies have corroborated these findings, drawing attention to the potential of the e-nose [24] and the e-tongue to detect frauds and other features in honey and emphasized the importance of choosing the correct data treatment method to enhance the sensitivity and specificity of the method [22; 23; 24]. This work demonstrates the application of the e-nose for discrimination between authentic and adulterated honey. Honey samples of different botanic origins (eucalyptus,orange, and wild plants) were obtained from Brazilian producers. The samples were evaluated using conventional techniques (humidity, acidity, diastase enzyme, Lund reaction, Fiehe and Winckler tests), and adulterated samples were related to frauds commonly practiced in the market (addition of different vegetal sugars). The developed e-nose is based on interdigitated electrodes covered with ionogel (ionic liquid + gelatin + Fe\({}_{3}\)O\({}_{4}\)) films and on the variation of the sensor's conductance according to the sample to which it is exposed. Based on conductance data, two strategies were evaluated for the discrimination between authentic and adulterated honey, graphic discrimination (PCA) and machine learning (automated classifiers), the latter being more efficient due to the possibility of automating the treatment of data, reducing subjectivity, and improving the predicting capacity. In this sense, due to the fast response (19 min) and simplicity (non-destructive analysis and without any sample treatment) of the developed methodology, the technology based on the e-nose can be applied to honey quality control.