Artificial intelligence driven monitoring system for aging whiskey
This disclosure relates to an AI-driven, non-invasive system configured to analyze whiskey color and maturation outcomes based on sensor-tracked environmental and chemical variables through integrated machine-learning, high-accuracy volume sensing, dielectric-based proof monitoring, and environmental data analysis to forecast how whiskey will age inside a barrel over time, and by leveraging real-time barrel data such as time, proof, volume, temperature, humidity, and evaporation rates, the AI model can accurately analyze the final color, proof, and optimal aging duration for whiskey and other barrel-aged spirits, which can allow distilleries to reduce inconsistencies, improve yield management, and enhance quality control without relying on manual sampling or invasive testing.
1 . An apparatus for analyzing a liquid comprising:
a sensor comprising:
a transmitter configured to transmit an electromagnetic signal through an external surface of a barrel housing a liquid and into the barrel, wherein the transmitter comprises an antenna element positioned external to the barrel; and
a receiver configured to receive an electromagnetic return signal associated with the transmitted electromagnetic signal;
one or more memories; and
one or more processors, coupled to the one or more memories, configured to:
derive at least one data point associated with the liquid based on the transmitted electromagnetic signal and the electromagnetic return signal;
input the at least one data point into a machine-learning model, wherein the machine-learning model is trained on training data comprising a plurality of data points associated with liquids having known alcohol content values; and
output, by the machine-learning model, based on the input, an alcohol content of the liquid.
2 . The apparatus of claim 1 , wherein the at least one data point comprises at least one of a signal-strength change, a frequency shift, or a phase shift.
3 . The apparatus of claim 1 , wherein the transmitter is attached to the external surface of the barrel.
4 . The apparatus of claim 1 , further comprising:
a controller configured to adjust at least one process parameter of a monitoring system based on the alcohol content of the liquid.
5 . The apparatus of claim 1 , wherein the sensor comprises at least one of a radar sensor or a dielectric sensor.
6 . The apparatus of claim 1 , wherein the liquid comprises at least one of a distilled spirit or a fermented liquid.
7 . The apparatus of claim 6 , wherein the one or more processors are configured to track changes in the alcohol content over an aging period of the liquid in the barrel.
8 . The apparatus of claim 1 , wherein the one or more processors are configured to obtain one or more features based on the at least one data point for input to the machine-learning model.
9 . The apparatus of claim 1 , wherein the one or more processors are further configured to:
derive a plurality of data points at different time intervals during an aging period of the liquid in the barrel; and
input the plurality of data points into the machine-learning model to track changes in the alcohol content over the aging period.
10 . The apparatus of claim 1 , further comprising:
one or more environmental sensors configured to measure at least one of temperature or humidity external to the barrel, wherein the one or more processors are configured to input data from the one or more environmental sensors into the machine-learning model along with the at least one data point.
11 . The apparatus of claim 1 , wherein the one or more processors are further configured to:
generate a traceability record comprising at least one of:
a timestamp associated with the derived at least one data point,
the alcohol content output by the machine-learning model, or
an identifier associated with the barrel; and
store the traceability record in a distributed ledger.
12 . The apparatus of claim 1 , wherein the one or more processors are further configured to:
compare the alcohol content output by the machine-learning model to a target alcohol content range; and
generate an alert when the alcohol content is within the target alcohol content range.
13 . A method for analyzing a liquid, the method comprising:
transmitting an electromagnetic signal through an external surface of a barrel housing the liquid and into the barrel, wherein the electromagnetic signal is transmitted from an antenna element positioned external to the barrel;
receiving an electromagnetic return signal associated with the transmitted electromagnetic signal;
deriving at least one data point associated with the liquid based on the transmitted electromagnetic signal and the electromagnetic return signal;
storing the at least one data point in one or more memories;
inputting, by one or more processors, the at least one data point into a machine-learning model, wherein the machine-learning model is trained on training data comprising a plurality of data points associated with liquids having known alcohol content values; and
outputting, by the machine-learning model, based on the input, an alcohol content of the liquid.
14 . The method of claim 13 , wherein the at least one data point comprises at least one of a signal-strength change, a frequency shift, or a phase shift.
15 . The method of claim 13 , wherein the antenna element is attached to the external surface of the barrel.
16 . The method of claim 13 , further comprising:
adjusting, by a controller, at least one process parameter of a monitoring system based on the alcohol content of the liquid.
17 . The method of claim 13 , wherein the liquid comprises at least one of a distilled spirit or a fermented liquid.
18 . A system for analyzing a liquid comprising:
a transmitter to transmit an electromagnetic signal, wherein the transmitter comprises an antenna element positioned external to a barrel containing a liquid;
a receiver to receive an electromagnetic return signal associated with the transmitted electromagnetic signal;
one or more memories; and
one or more processors, coupled to the one or more memories, to:
derive at least one data point associated with the liquid based on the transmitted electromagnetic signal and the electromagnetic return signal;
input the at least one data point into a machine-learning model, wherein the machine-learning model is trained on training data comprising a plurality of data points associated with liquids having known alcohol content values; and
output, by the machine-learning model, based on the input, an alcohol content of the liquid.
19 . The system of claim 18 , wherein:
the one or more processors are configured to track changes in the alcohol content over an aging period of the liquid in the barrel; and
the liquid comprises at least one of a distilled spirit or fermented liquid.
20 . The system of claim 18 , further comprising:
a controller configured to adjust at least one process parameter of a monitoring system based on the alcohol content of the liquid.