Review of linear and nonlinear models in breath analysis by Cyranose 320

Analysis of volatile organic compounds (VOCs) in breath specimens has potential for point of care (POC) screening due to ease of sample collection. While the electronic nose (e-nose) is a standard VOC measure across a wide range of industries, it has not been adopted for POC screening in healthcare. One limitation of the e-nose is the absence of mathematical models of data analysis that yield easily interpreted findings at POC. The purposes of this review were to (1) examine the sensitivity/specificity results from studies that analyzed breath smellprints using the Cyranose 320, a widely used commercial e-nose, and (2) determine whether linear or nonlinear mathematical models are superior for analyzing Cyranose 320 breath smellprints. This systematic review was conducted according to the guidelines of the Preferred Reporting Items for Systematic Review and Meta-Analyses using keywords related to e-nose and breath. Twenty-two articles met the eligibility criteria. Two studies used a linear model while the rest used nonlinear models. The two studies that used a linear model had a smaller range for mean of sensitivity and higher mean (71.0%-96.0%; M = 83.5%) compared to the studies that used nonlinear models (46.9%-100%; M = 77.0%). Additionally, studies that used linear models had a smaller range for mean of specificity and higher mean (83.0%-91.5%; M = 87.2%) compared to studies that used nonlinear models (56.9%-94.0%; M = 76.9%). Linear models achieved smaller ranges for means of sensitivity and specificity compared to nonlinear models supporting additional investigations of their use for POC testing. Because our findings were derived from studies of heterogenous medical conditions, it is not known if they generalize to specific diagnoses.

Automated summary

Analysis of breath VOCs using the Cyranose 320 e-nose showed varying sensitivity and specificity results. Linear models yielded smaller ranges for mean sensitivity and specificity compared to nonlinear models. Further exploration of linear models for point of care testing is warranted.