Non-Invasive Blood Pressure Tracking of Spontaneous Hypertension Rats Using an Electronic Nose

Traditional noninvasive blood pressure measurement methods in experimental animals are time consuming and difficult to operate, particularly for large numbers of animals. In this study, the possibility of sensing fecal odor to estimate the blood pressure status of spontaneous hypertension rats (SHRs) was explored with the aim of establishing a new method for non-invasive monitoring of blood pressure. The body weight and blood pressure of SHRs kept increasing with growth, and the odor information monitored using an E-nose varied with the blood pressure status, particularly for sensors S6 and S7. The fecal information was analyzed using principal component analysis, canonical discriminant analysis and multilayer perception neural networks (MLP) to discriminate SHRs from normal ones, with a 100% correct classification rate. For better prediction of blood pressure, the model built using multiple linear regression analysis, partial least squares regression analysis and multilayer perceptron neural network analysis were used, with coefficients of determination (R²) ranging from 0.8036 to 0.9926. Moreover, the best prediction model for blood pressure was established using MLP analysis with an R²¬ higher than 0.91. Thus, changes in blood pressure levels can be tracked non-invasively, and normotension can be distinguished from hypertension or even at different hypertension levels based on the odor information of rat feces, providing a foundation for non-invasive health monitoring. This work might provide potential instructions for functional food research aimed at lowering blood pressure.