Electronic nose dataset for COPD detection from smokers and healthy people through exhaled breath analysis.

This article presents a database which was obtained by acquiring measurements through a multisensory device called Electronic Nose (E-nose) based on a matrix of metal oxide sensors, in order to discriminate and classify a group of people affected by the respiratory disease Chronic Obstructive Pulmonary Disease (COPD), smokers and healthy control people through exhaled breath analysis. The database consists of 4 groups of measurements which were acquired through the E-nose system: 10 control samples (healthy people), 20 samples of people with COPD, 4 samples of smokers and 10 air samples, where in each group two samples of exhaled breath per person were acquired giving a total of 78 samples (40 from COPD, 20 from control, 8 from smokers and 10 from the air).

Non-Invasive Method to Detect Infection with Mycobacterium tuberculosis Complex in Wild Boar by Measurement of Volatile Organic Compounds Obtained from Feces with an Electronic Nose System.

More effective methods to detect bovine tuberculosis, caused by Mycobacterium bovis, in wildlife, is of paramount importance for preventing disease spread to other wild animals, livestock, and human beings. In this study, we analyzed the volatile organic compounds emitted by fecal samples collected from free-ranging wild boar captured in Doñana National Park, Spain, with an electronic nose system based on organically-functionalized gold nanoparticles. The animals were separated by the age group for performing the analysis. Adult (>24 months) and sub-adult (12-24 months) animals were anesthetized before sample collection, whereas the juvenile (<12 months) animals were manually restrained while collecting the sample. Good accuracy was obtained for the adult and sub-adult classification models: 100% during the training phase and 88.9% during the testing phase for the adult animals, and 100% during both the training and testing phase for the sub-adult animals, respectively. The results obtained could be important for the further development of a non-invasive and less expensive detection method of bovine tuberculosis in wildlife populations.

Academic stress detection on university students during COVID-19 outbreak by using an electronic nose and the galvanic skin response.

Academic stress is an emotion that students experience during their time at the university, sometimes causing physical and mental health effects. Because of the COVID-19 pandemic, universities worldwide have left the classroom to provide the method of teaching virtually, generating challenges, adaptations, and more stress in students. In this pilot study, a methodology for academic stress detection in engineering students at the University of Pamplona (Colombia) is proposed by developing and implementing an artificial electronic nose system and the galvanic skin response. For the study, the student's stress state and characteristics were taken into account to make the data analysis where a set of measurements were acquired when the students were presenting a virtual exam. Likewise, for the non-stress state, a set of measurements were obtained in a relaxation state after the exam date. To carry out the pre-processing and data processing from the measurements obtained previously by both systems, a set of algorithms developed in Python software were used to perform the data analysis. Linear Discriminant Analysis (LDA), K-Nearest Neighbors (K-NN), and Support Vector Machine (SVM) classification methods were applied for the data classification, where a 96 % success rate of classification was obtained with the E-nose, and 100 % classification was achieved by using the Galvanic Skin Response.

Exhaled breath analysis for gastric cancer diagnosis in Colombian patients.

We present here the first study that directly correlates gastric cancer (GC) with specific biomarkers in the exhaled breath composition on a South American population, which registers one of the highest global incidence rates of gastric affections. Moreover, we demonstrate a novel solid state sensor that predicts correct GC diagnosis with 97% accuracy. Alveolar breath samples of 30 volunteers (patients diagnosed with gastric cancer and a controls group formed of patients diagnosed with other gastric diseases) were collected and analyzed by gas-chromatography/mass-spectrometry (GC-MS) and with an innovative chemical gas sensor based on gold nanoparticles (AuNP) functionalized with octadecylamine ligands. Our GC-MS analyses identified 6 volatile organic compounds that showed statistically significant differences between the cancer patients and the controls group. These compounds were different from those identified in previous studied performed on other populations with high incidence rates of this malady, such as China (representative for Eastern Asia region) and Latvia (representative for Baltic States), attributable to lifestyle, alimentation and genetics differences. A classification model based on principal component analysis of our sensor data responses to the breath samples yielded 97% accuracy, 100% sensitivity and 93% specificity. Our results suggest a new and non-intrusive methodology for early diagnosis of gastric cancer that may be deployed in regions lacking well-developed health care systems as a prediagnosis test for selecting the patients that should undergo deeper investigations (e.g., endoscopy and biopsy).