The aroma of wine is traditionally analyzed by sensory methods or by using gas chromatography; both analytical methodologies are slow and expensive and do not allow continuous monitoring. For this reason, interest in rapid methods has increased in recent times. Electronic noses (e-noses) stand out for their high sensitivity, speed, low cost, and little or no sample preparation. They present, however, low selectivity, which requires advance analytical methods to distinguish compounds. Here, we present a low-cost e-nose device for the analysis and identification of distinct varieties of wine. Chemical analysis data are compared to e-nose data through a principal component analysis (PCA) and a k-means clustering algorithm to establish relationships between varieties of wines and the e-nose classification capability. The results show that e-nose technology found significant differences between the analyzed samples, and furthermore, classifying the samples in accordance with the chemical analysis classification. The maximal accuracy obtained was 100% using the k-means algorithm for binary classification with N = 21 samples. Thus the potential of e-nose technology was shown in the wine industry for the identification and classification of wine varieties or quality.

Lethal Bronzing Disease (LB) is a disease of palms caused by the 16SrIV-D phytoplasma. A low-cost electronic nose (eNose) prototype was trialed for its detection. It includes an array of eight Taguchi-type (MQ) sensors (MQ135, MQ2, MQ3, MQ4, MQ5, MQ9, MQ7, and MQ8) controlled by an Arduino NANO^® microcontroller, using heater voltages that vary sinusoidally over a 2.5 min cycle. Samples of uninfected, early symptomatic, moderate symptomatic, and late symptomatic infected palm leaves of the cabbage palm were processed and analyzed. MQ sensor responses were subjected to a 256 element discrete Fourier transform (DFT), and harmonic component amplitudes were reviewed by principal component analysis (PCA). The experiment was repeated three times, each showing clear evidence of differences in sensor responses between the samples of uninfected leaves and those in the early stages of infection. Within each experiment, four groups of responses were identified, demonstrating the ability of the unit to repeatedly distinguish healthy leaves from diseased ones; however, detection of the severity of infection has not been demonstrated. By selecting appropriate coefficients (here demonstrated with plots of MQ5 Cos1 vs. MQ8 Sin3), it should be possible to build a ruleset classifier to identify healthy and unhealthy samples.

Currently, urine samples for bacterial or fungal infections require a long diagnostic period (48 h). In the present work, a point-of-care device known as an electronic nose (eNose) has been designed based on the “smell print” of infections, since each one emits various volatile organic compounds (VOC) that can be registered by the electronic systems of the device and recognized in a very short time. Urine samples were analyzed in parallel using urine culture and eNose technology. A total of 203 urine samples were analyzed, of which 106 were infected and 97 were not infected. A principal component analysis (PCA) was performed using these data. The algorithm was initially capable of correctly classifying 49% of the total samples. By using SVM-based models, it is possible to improve the accuracy of the classification up to 74% when randomly using 85% of the data for training and 15% for validation. The model is evaluated as having a correct classification rate of 74%. In conclusion, the diagnostic accuracy of the eNose in urine samples is high, promising and amenable for further improvement, and the eNose has the potential to become a feasible, reproducible, low-cost and high-precision device to be applied in clinical practice for the diagnosis of urinary tract infections.