Accurate fermentation monitoring through multi sensorial approach
FERMENT
Funded by: MUR
Calls: PRIN 2022
Start date: 2023-09-28 End date: 2025-09-27
Total Budget: EUR 261.600,00 INO share of the total budget: EUR 58.100,00
Scientific manager: UNIVERSITA DI SALERNO and for INO is: Ponzoni Andrea
Organization/Institution/Company main assignee: UNIVERSITA DI SALERNO
Calls: PRIN 2022
Start date: 2023-09-28 End date: 2025-09-27
Total Budget: EUR 261.600,00 INO share of the total budget: EUR 58.100,00
Scientific manager: UNIVERSITA DI SALERNO and for INO is: Ponzoni Andrea
Organization/Institution/Company main assignee: UNIVERSITA DI SALERNO
other Organization/Institution/Company involved:
Università di Salerno
Abstract: Fermentation is a complex biochemical reaction that can be triggered in an autonomous or controlled way in organic substances subjected to certain conditions and / or to suitable catalysts. Controlled priming is used, among other things, in the production of some alcoholic beverages such as wine and beer, starting from musts of different origins but which have in common a sugar content (glucose, fructose, maltose etc.) which, by fermenting, are transformed into alcohol.
The monitoring of a set of chemicals during the fermentation process allows the producers to suddenly operate to modify the process parameters with the aim to optimize the different phases and maximize the final quality of the final product, by eliminating any risk of unwanted phenomena such as stuck or sluggish fermentation.
The research project “Accurate fermentation monitoring through multi-sensorial approach” aims to study the integration among bio-, gas- and density-sensors to evaluate a broad set of fermentation markers, such as the ethanol, lactic acid and glycerol concentration in the liquid phase, the solution density, and the volatile phase composition (particularly acetic acid and CO2). Since none of these physical/chemical parameters is fully representative of the fermentation process, but instead their combination could be, a data fusion from this wide range of sensors/parameters will be employed to accurately characterize the fermentation processes of alcoholic beverages.
New sensors will be optimized and ad hoc conditioning circuits will be realized too. The integration between conditioning and pre-processing unit will lead to the creation of smart transducers with both wired and wireless interfaces capable of carrying out and transmitting measurements in real time. Suitable edge and fog computing layers will allow acquired data to be fused in order to obtain more accurate measurements and deep knowledge of the monitored process. The so designed and realized architecture will be tested respect to a real fermentation process.
The monitoring of a set of chemicals during the fermentation process allows the producers to suddenly operate to modify the process parameters with the aim to optimize the different phases and maximize the final quality of the final product, by eliminating any risk of unwanted phenomena such as stuck or sluggish fermentation.
The research project “Accurate fermentation monitoring through multi-sensorial approach” aims to study the integration among bio-, gas- and density-sensors to evaluate a broad set of fermentation markers, such as the ethanol, lactic acid and glycerol concentration in the liquid phase, the solution density, and the volatile phase composition (particularly acetic acid and CO2). Since none of these physical/chemical parameters is fully representative of the fermentation process, but instead their combination could be, a data fusion from this wide range of sensors/parameters will be employed to accurately characterize the fermentation processes of alcoholic beverages.
New sensors will be optimized and ad hoc conditioning circuits will be realized too. The integration between conditioning and pre-processing unit will lead to the creation of smart transducers with both wired and wireless interfaces capable of carrying out and transmitting measurements in real time. Suitable edge and fog computing layers will allow acquired data to be fused in order to obtain more accurate measurements and deep knowledge of the monitored process. The so designed and realized architecture will be tested respect to a real fermentation process.