Application of an Electronic Nose as a New Technology for Rapid Detection of Adulteration in Honey

• Autor(a) principal: Gonçalves, Wellington Belarmino
• Data de Publicação: 2023
• Outros Autores: Teixeira, Wanderson Sirley Reis [UNESP], Cervantes, Evelyn Perez, Mioni, Mateus de Souza Ribeiro [UNESP], Sampaio, Aryele Nunes da Cruz Encide [UNESP], Martins, Otávio Augusto [UNESP], Gruber, Jonas, Pereira, Juliano Gonçalves [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.3390/app13084881; http://hdl.handle.net/11449/247287
• topic: electronic nose; honey adulteration; honey quality control; machine learning; multivariate analysis; sensors

This work demonstrates the application of an electronic nose (e-nose) for discrimination between authentic and adulterated honey. The developed e-nose is based on electrodes covered with ionogel (ionic liquid + gelatin + Fe3O4 nanoparticle) films. Authentic and adulterated honey samples were submitted to e-nose analysis, and the capacity of the sensors for discrimination between authentic and adulterated honey was evaluated using principal component analysis (PCA) based on average relative response data. From the PCA biplot, it was possible to note two well-defined clusters and no intersection was observed. To evaluate the relative response data as input for autonomous classification, different machine learning algorithms were evaluated, namely instance based (IBK), Kstar, Trees-J48 (J48), random forest (RF), multilayer perceptron (MLP), naive Bayes (NB), and sequential minimal optimization (SMO). Considering the average data, the highest accuracy was obtained for Kstar: 100% (k-fold = 3). Additionally, this algorithm was also compared regarding its sensitivity and specificity, both being 100% for both features. Thus, due to the rapidity, simplicity, and accuracy of the developed methodology, the technology based on e-noses has the potential to be applied to honey quality control.