Alcoholic Beverage Flavoring Control Using Spectral and Environmental Sensing
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Solution Overview
Problem
The lack of environmental parameter control during the lengthy process of flavoring alcoholic beverages, such as whiskey, results in inconsistent flavor development, making it difficult to distinguish between good and poor batches.
Innovation Solution
A system and method utilizing a spectral sensor, temperature and pressure sensors, and a machine learning model to collect and analyze optical and environmental data during flavoring sessions, with human taste tester feedback, to train a model for quality control and batch differentiation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If environmental parameters are not controlled during the flavoring process, then the process is simple and requires minimal equipment, but the flavor development becomes inconsistent and quality control deteriorates
Solution Approach 1:
The system monitors and controls multiple environmental parameters (temperature, pressure, spectral properties) during the flavoring process to ensure consistent flavor development. By maintaining parameters within specific ranges, the system achieves reliable flavor consistency without requiring complex manual intervention.
Solution Approach 2:
The system continuously monitors environmental parameters and uses machine learning models to analyze spectral data, providing feedback on flavor development progress. This feedback mechanism allows the system to adjust conditions in real-time, ensuring consistent quality while automating the control process.
2Reliability
If environmental parameters are controlled during the flavoring process, then flavor consistency improves, but the device complexity and monitoring requirements increase
Solution Approach 1:
The control system integrates multiple functions into a single platform: environmental monitoring, spectral analysis, machine learning-based flavor assessment, and automated control. This multi-functional approach achieves reliable quality control while avoiding the need for separate complex systems for each function.
Solution Approach 2:
The system uses machine learning models to automatically analyze spectral data and determine flavor development status without requiring constant human expertise. The system self-regulates by comparing real-time data against learned patterns, reducing the need for manual monitoring while maintaining high reliability.
3Productivity
If traditional flavoring processes are used without environmental control, then the process duration is long (years for whiskey aging), but the process is simple to operate
Solution Approach 1:
The system replaces traditional time-based flavoring processes with controlled environmental conditions and automated monitoring. By using spectral sensors and machine learning to track flavor development in real-time, the system can optimize process duration and achieve desired flavor profiles faster than traditional methods while automating the monitoring that would otherwise require extensive human oversight.
4Measurement precision
If spectral sensors and machine learning models are implemented, then batch differentiation and quality control improve, but the system complexity and initial setup requirements increase
Solution Approach 1:
The system pre-trains machine learning models using spectral data from known good and poor batches before actual flavoring operations. This preliminary training establishes baseline patterns for quality assessment, allowing the system to quickly and accurately differentiate batches during production without requiring complex real-time analysis setup.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system enables consistent flavor development by continuously learning flavor characteristics, allowing for effective differentiation between good and poor batches of alcoholic beverages.
Implementation Method 1
a spectral sensor positioned along an interior surface area of the beverage flavoring container and exposed to the sealable chamber. The spectral sensor is configured to collect optical properties of at least one of the flavoring agent or the alcoholic beverage
Implementation Method 2
At least one of a temperature sensor or a pressure sensor collects environment data of the beverage flavoring container during the flavoring session
Implementation Method 3
At least one of a temperature sensor or a pressure sensor collects environment data of the beverage flavoring container during the flavoring session
Implementation Method 4
a heating/cooling unit configured to adjust a temperature of the alcoholic beverage
Implementation Method 5
a vacuum source in communication with the beverage flavoring container and configured to remove air from the beverage flavoring container
Data Source
AI summary
A method of training a machine learning model for evaluating flavor profiles of an alcoholic beverage includes a spectral sensor collecting optical properties of the beverage or flavoring agent suspended in the beverage within a container. Temperature and pressure sensors collect environment data of the container during a flavoring session. A control apparatus displays a user interface requesting a taste tester to select labels for the session. A control apparatus memory stores selected labels responsive to the taste tester's selections. A feature extraction module extracts features from the optical properties and environment data based on predetermined criteria. A machine learning engine trains a machine learning model to continuously learn flavor characteristics under a plurality of flavoring sessions using the extracted features and selected labels. The trained machine learning model is deployed for quality control and for determining the difference between a good batch and poor batch of the alcoholic beverage.