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.

Ionogel are versatile materials, as they present the electrical properties of ionic liquids and also dimensional stability, since they are trapped in a solid matrix, allowing application in electronic devices such as gas sensors and electronic noses. In this work, ionogels were designed to act as a sensitive layer for the detection of volatiles in a custom-made electronic nose. Ionogels composed of gelatin and a single imidazolium ionic liquid were doped with bare and functionalized iron oxide nanoparticles, producing ionogels with adjustable target selectivity. After exposing an array of four ionogels to 12 distinct volatile organic compounds, the collected signals were analyzed by principal component analysis (PCA) and by several supervised classification methods, in order to assess the ability of the electronic nose to distinguish different volatiles, which showed accuracy above 98%.

Evaluation concerning the presence of bacteria in meat products is mandatory for commercializing these goods. Although food bacteria detection is based on microbiological methods, these assays are usually laborious and time-consuming. In this paper, an electronic nose is used to differentiate Salmonella spp. (SA), Escherichia coli (EC), and Pseudomonas fluorescens (PF) inoculated in raw meat (beef, chicken, and pork) and incubated at 22 °C for 3 days. The obtained data were evaluated by principal component analysis (PCA) and different machine learning algorithms. From the graphical analysis of the PCA, on day 1, the clusters were close to each other for beef, chicken, and pork, while on days 2 and 3, more separated bacteria clusters were obtained regardless of the meat type, allowing for the discrimination of the samples for the latter days. To estimate the growth rates of the microorganisms, the distance between clusters was calculated and provided a pattern for the three bacteria, with the slowest-, moderate-, and fastest-growing being EC, SA, and PF, respectively. Concerning the machine learning algorithms, the accuracy varied from 93.8 to 100% for beef and chicken, while for pork, it varied from 75% to 100%. Thus, these results suggest that the proposed methodology based on electronic nose has the potential for the direct discrimination of bacteria in raw meat, with reduced analysis time, costs, and manipulating steps.

The COVID-19 pandemic highlighted the urgent need for rapid and accurate SARS-CoV-2 detection. Current diagnostic methods often suffer from discomfort, slow results, and limited accuracy in early infection stages. This study proposes a solution: a noninvasive, rapid, and accurate detection approach for point-of-care settings using a metal-oxide-sensor-based electronic nose. This innovative electronic nose uses an array of off-the-shelf gas sensors. These sensors detect and analyze the volatile organic compounds present in saliva and exhaled breath, which change based on the presence of the SARS-CoV-2 virus. We evaluated the discriminatory power of the electronic nose using a suite of machine learning algorithms, specifically K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Neural Networks (NN), and Random Forest, to differentiate between SARS-CoV-2 infected and non-infected samples. Accuracy metrics ranged from 76% to 89% for exhaled breath samples and from 75% to 86% for saliva samples. Optimal accuracy was achieved with the KNN algorithm, yielding an Area Under the Curve (AUC) of 0.861 (95% CI 0.825‒0.897) for saliva and 0.895 (95% CI 0.850‒0.940) for exhaled breath. These results support the feasibility and proof-of-concept performance of a low-cost electronic nose for SARS-CoV-2 detection in a real-world hospital cohort.