Coffee Type Recognition Using Volatile Organic Compound Sensors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing coffee grinder and dispenser systems fail to accurately discriminate between different coffee blends based on their volatile organic compound concentrations, leading to inconsistencies in beverage preparation when the wrong coffee is used, and existing sensors only detect the intensity of the coffee's smell rather than its specific blend.
Innovation Solution
A method and system that utilize sensors to detect specific volatile organic compounds like pyrazine, furan, carboxylic acids, and pyridine derivatives, coupled with a neural network algorithm to recognize coffee types by analyzing electrical resistance variations over time, generating a recognition signal to alert operators of any anomalies and adjust grinding parameters accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional sensors are used to detect volatile organic compounds, then the intensity of coffee smell can be detected, but the specific coffee blend cannot be discriminated
Solution Approach 1:
The patent segments the detection of volatile organic compounds into multiple independent sensor channels, each sensitive to specific compound classes (pyrazines, furans, carboxylic acids, pyridines). This segmentation allows the system to detect individual compound concentrations rather than just overall intensity, enabling precise coffee blend discrimination through pattern recognition of the compound profile.
2Reliability
If the hopper is replenished with wrong coffee, then the grinder prepares beverage with unchanged parameters, but the organoleptic properties become incorrect
Solution Approach 1:
The system implements feedback by continuously monitoring the volatile organic compound profile of coffee in the hopper and comparing it against the expected profile for the selected coffee blend. When a discrepancy is detected indicating wrong coffee type, the system provides feedback through operator alerts and can automatically adjust grinding parameters or prevent operation, ensuring reliability without requiring constant manual verification.
3Measurement precision
If sensors detect only TVOC intensity, then the smell strength is measured, but blends with same TVOC cannot be distinguished
Solution Approach 1:
The patent changes the detection parameters from a single TVOC intensity measurement to multiple independent compound concentration measurements. By monitoring the concentrations of different volatile organic compound classes (pyrazines, furans, carboxylic acids, pyridines) separately, the system creates a multidimensional chemical profile that can distinguish between coffee blends even when their overall TVOC intensity is identical, resolving the limitation of conventional sensors.
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
Effectively identifies the type of coffee in the hopper, preventing preparation errors by generating alerts for incorrect coffee and adjusting parameters for optimal brewing, ensuring consistent organoleptic properties in the beverage.
Implementation Method 1
sensors to detect specific volatile organic compounds like pyrazine, furan, carboxylic acids, and pyridine derivatives, coupled with a neural network algorithm to recognize coffee types by analyzing electrical resistance variations over time
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Disclosed is a method of recognizing a type of coffee from a plurality of reference types of coffee, each reference type of coffee being associated with a respective reference signal, said method comprising the steps of sensing, by one or more sensors (301), the presence and concentration of one or more volatile organic compounds in a mixture of volatile organic compounds released from the coffee; generating a signal representative of the coffee according to the volatile organic compounds that have been sensed and their respective concentrations; comparing the generated signal with the reference signals associated with each reference type of coffee; generating a recognition signal in response to the comparison of the generated signal with the reference signals.