An electronic nose (E-nose) is an olfactory system that can sense odors (volatile compounds) from food. The equipment is based on a set of gas sensors capable of capturing differences in chemicals resealed by foodstuff . E-nose systems have been widely applied to fruits, however \to the best of our knowledge Pitaya has not yet been assessed. Therefore, this work aimed to propose a Low-cost E-nose system to assess Pitaya’s shelf-life using Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis (PLS-DA). 140 fruits were divided into 5 classes (day 0, 7, 14, 21 and 25) and were evaluated using an E-nose setting with 8 Metal Oxide Sensors (MOS). Pre-treatments that showed the best performance for PCA were mean center; additionally, for PLS-DA model, smoothing and the first derivative was used. PC1 and PC2 explained 68.10% of the variability in Pitaya fruit for the different storage days. The scores for PC1 moved from positive Day 0 to negative Day 14, while PC2 separated Day 7, 21 and 25 positive scores from Day 0 and 14 negatives scores. PLS-DA model (12 LVs) showed accuracy over 77% for all classes except for Day 14 for the prediction set, and overall accuracy of 87%. MQ3 and MQ4 presented the best response among sensors, which explains the transformations in the fruit during storage, either for malic acid content (OH group) and respiration metabolites, such as CO2 . Therefore, the E-nose equipment demonstrated to be a good way to classify Pitaya fruit. In combination with chemometrics, it can be used in the industry to monitor pitaya shelf-life, specially spotting the days 21 and 25 that in this work showed an accuracy of 100%, which would help to decide the maximum storage period and the selection of these fruits to specific destination/product.