Grape Must as a Bioelectrochemical Processor

We explore spontaneous voltage oscillations in grape must (mustalevria) fermentation systems. This study uses multichannel differential electrode arrays. Seven platinum-iridium (Pt/Ir) electrode pairs tracked bioelectrochemical changes for 200,000 s. They showed complex patterns over time and space. Frequencies varied from 0.00044 to 0.00215 Hz. Power spectral density analysis showed brown noise traits. The spectral slopes ranged from -2.01 to -3.28. This indicates strong temporal integration and memory effects during fermentation. Environmental correlation analysis showed temperature as the primary modulator (<i>r</i> = 0.245-0.558), while humidity exhibited negative correlations (-0.052 to -0.245). Binary state analysis showed that the system uses natural Boolean logic. XOR gates had the highest entropy at 0.93 bits. This suggests that there is significant temporal asynchrony across different spatial areas. Principal component analysis found activation patterns without a single strong mode. It needed 3-4 components to capture 77.6% of the system's variance. The fermentation medium showed uneven metabolic activity across different areas. Also, the electrode locations were statistically independent, with mutual information below 0.206 bits. These findings show that traditional food fermentation systems work like self-organizing bioelectrochemical processors. They can also perform distributed computation through local metabolic interactions. Brown noise scaling and memory effects can impact fermentation monitoring and control. This means short-term measurements may not accurately predict long-term behavior. This work shows that grape must fermentation can be a model system. It helps us study new computational properties in biological electrochemical systems.