Stress is a leading cause of several disease types, yet
it is underdiagnosed
as current diagnostic methods are mainly based on self-reporting and
interviews that are highly subjective, inaccurate, and unsuitable
for monitoring. Although some physiological measurements exist (e.g., heart rate variability and cortisol), there are no
reliable biological tests that quantify the amount of stress and monitor
it in real time. In this article, we report a novel way to measure
stress quickly, noninvasively, and accurately. The overall detection
approach is based on measuring volatile organic compounds (VOCs) emitted
from the skin in response to stress. Sprague Dawley male rats (n = 16) were exposed to underwater trauma. Sixteen naive
rats served as a control group (n = 16). VOCs were
measured before, during, and after induction of the traumatic event,
by gas chromatography linked with mass spectrometry determination
and quantification, and an artificially intelligent nanoarray for
easy, inexpensive, and portable sensing of the VOCs. An elevated plus
maze during and after the induction of stress was used to evaluate
the stress response of the rats, and machine learning was used for
the development and validation of a computational stress model at
each time point. A logistic model classifier with stepwise selection
yielded a 66–88% accuracy in detecting stress with a single
VOC (2-hydroxy-2-methyl-propanoic acid), and an SVM (support vector
machine) model showed a 66–72% accuracy in detecting stress
with the artificially intelligent nanoarray. The current study highlights
the potential of VOCs as a noninvasive, automatic, and real-time stress
predictor for mental health.