Dynamic Growth Monitoring of <i>Saccharomyces cerevisiae</i> Based on Microwave Sensor Array

<i>Saccharomyces cerevisiae</i> YPH499 participated in the brewing process of alcoholic products as a key strain. However, there is no effective method for real-time and non-contact monitoring of the dynamic growth of YPH499. In this study, we proposed a nested split-ring resonator (NSRR) array and established a microwave monitoring system for real-time and non-contact monitoring of YPH499. The results demonstrated that microwave parameters can effectively characterize the dynamic growth of YPH-499 and exhibit regular variations. Using principal component analysis (PCA) to extract and integrate detection data from nine resonators (RM1-RM9) of the NSRR array. The results indicated that the first principal component (PC1) could serve as a composite parameter to more effectively characterize the dynamic growth process of YPH-499. Comparative analysis through Logistic dynamic growth curve fitting demonstrated that PC1 exhibited a higher fitting rate (<i>R</i>²) than RM1-RM9 for all four microwave parameters, confirming the necessity of employing the NSRR array for monitoring purposes. The four microwave parameters obtained from RM1-RM9 were fused using Dempster-Shafer (D-S) evidence theory to generate microwave synthetic parameters (MSP1-MSP9). To establish the mapping relationship among MSP, survival rate (η_<i>s</i>), and apoptosis rate (η_<i>A</i>) of YPH-499, we calculated η_<i>s</i> and η_<i>A</i> of YPH-499 at each growth period and established the mapping function based on the temporal relationship. After comparison, we select MSP3 to characterize the η_<i>s</i> and η_<i>A</i> according to <i>R</i>². The <i>R</i>² of MSP3 is as high as 0.9985, indicating a strong correlation among these parameters. It enhanced our ability to evaluate the brewing process.