Chemometric analysis of the global pattern of volatile organic compounds in the exhaled breath of patients with COVID-19, post-COVID and healthy subjects. Proof of concept for post-COVID assessment

• Published in: Talanta (Oxford) Vol. 236; p. 122832
• Main Authors: Zamora-Mendoza, Blanca Nohemí, Díaz de León-Martínez, Lorena, Rodríguez-Aguilar, Maribel, Mizaikoff, Boris, Flores-Ramírez, Rogelio
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.01.2022
• Subjects: Chemometric data analysis; chemometrics; COVID-19; COVID-19 infection; Cross-Sectional Studies; discriminant analysis; electronic nose; Electronic nose system; Exhaled breath analysis; Healthy Volunteers; Humans; Post-COVID syndrome; principal component analysis; SARS-CoV-2; support vector machines; Volatile Organic Compounds; COVID-19; Volatile organic compounds; Exhaled breath analysis; Electronic nose system; Post-COVID syndrome; Chemometric data analysis
• DOI: 10.1016/j.talanta.2021.122832
• ISSN: 0039-9140; 1873-3573

The objective of this research was to evaluate the application of an electronic nose and chemometric analysis to discriminate volatile organic compounds between patients with COVID-19, post-COVID syndrome and controls in exhaled breath samples. A cross-sectional study was performed on 102 exhaled breath samples, 42 with COVID-19, 30 with the post-COVID syndrome and 30 control subjects. Breath-print analysis was performed by the Cyranose 320 electronic nose with 32 sensors. Group data were evaluated by Principal Component Analysis (PCA), Canonical Discriminant Analysis (CDA), and Support Vector Machine (SVM), and the test's diagnostic power was evaluated through a Receiver Operaring Characteristic curve(ROC curve). The results of the chemometric analysis indicate in the PCA a 97.6% (PC1 = 95.9%, PC2 = 1.0%, PC3 = 0.7%) of explanation of the variability between the groups by means of 3 PCs, the CDA presents a 100% of correct classification of the study groups, SVM a 99.4% of correct classification, finally the PLS-DA indicates an observable separation between the groups and the 12 sensors that were related. The sensitivity, specificity of post-COVID vs. controls value reached 97.6% (87.4%–99.9%) and 100% (88.4%–100%) respectively, according to the ROC curve. As a perspective, we consider that this technology, due to its simplicity, low cost and portability, can support strategies for the identification and follow-up of post-COVID patients. The proposed classification model provides the basis for evaluating post-COVID patients; therefore, further studies are required to enable the implementation of this technology to support clinical management and mitigation of effects. [Display omitted] •Post-COVID syndrome screening is vital in the present pandemic.•Electronic nose is capable of distinguish between VOCs fingerprints in exhaled breath.•Discrimination between COVID-19, post-COVID and controls breath fingerprints.•PCA 96.39% of explanation of the variability.•CDA and SVM 100% and 99.4% of correct classification respectively.