Analyte Characterization Using Sensor Envelope Grouping
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Solution Overview
Problem
Conventional chemical detection methods, such as gas chromatography and metal oxide sensors, face challenges in selectively identifying analytes due to overlapping retention times and low selectivity, especially when dealing with complex mixtures or unknown components, leading to difficulties in distinguishing quality issues from random variations.
Innovation Solution
A method involving the comparison of sensor measurements with defined minimum and maximum values, grouping anomalous measurements, and calculating dissimilarity indices to flag and classify deviations, allowing for more accurate characterization of analytes and determination of quality criteria compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional chemical detection methods such as gas chromatography and metal oxide sensors are used, then detection capability is provided, but selectivity deteriorates when several components have comparable retention times or when dealing with complex mixtures
Solution Approach 1:
The patent transforms the detection problem from one-dimensional (single sensor output) to multi-dimensional by using arrays of sensors with different sensitivities and response characteristics. Each sensor in the array provides measurements at different dimensions (sensitivity levels, response times), allowing complex mixtures to be resolved by analyzing patterns across multiple dimensions rather than relying on single-peak resolution.
Solution Approach 2:
The patent segments the detection task by dividing the sensor array into multiple independent sensing elements, each optimized for different analyte groups. Instead of using one sensor to detect all components, the system segments the detection function across multiple sensors with specialized sensitivities, allowing simultaneous detection of multiple analyte classes with high selectivity for each segment.
2Adaptability or versatility
If theoretical reference data from large databases is used for analyte identification, then coverage of possible analytes increases, but reliability deteriorates when samples contain complex mixtures or unknown components that do not match simple individual references
Solution Approach 1:
The patent changes the reference comparison parameter from single analyte profiles to mixture profiles. Instead of comparing sensor outputs against databases of individual analyte references, the system creates and compares reference profiles representing complex mixtures. This parameter transformation allows the system to handle unknown and complex samples by matching overall pattern characteristics rather than requiring exact matches to individual reference analytes.
Solution Approach 2:
The patent creates composite reference profiles that represent complex mixtures of multiple analytes. These composite references combine the characteristics of multiple individual analytes into unified reference patterns, allowing the system to identify mixture samples by comparing against composite rather than individual references, thereby improving reliability for complex sample types.
3Ease of operation
If statistical comparison methods are used to determine quality criteria compliance, then analysis is provided, but ability to distinguish quality issues from random variations deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors sensor responses and compares them against dynamically updated reference ranges. The system provides feedback on deviation patterns, using historical data and control charts to distinguish systematic quality issues from random variations. This feedback loop enables the system to learn from past measurements and improve its ability to detect genuine quality problems while filtering out normal variability.
Data Source
AI summary
A method and apparatus are provided for characterizing a product sample for example in comparison to a reference sample using a sensor such as a gas chromatograph or a MOS sensor. This characterization may comprise an indication of whether or not the product sample conforms to a quality criterion. The comparison of the sensor output measurements for the product sample is compared to maximum and minimum value curves, which may be derived from measurements of the reference sample, whereby adjacent samples outside the envelope defined by these maximum and minimum values are grouped together. A dissimilarity index may be determined for the anomalous values as a whole, or on a per group basis. The groups may be classified depending on the shape they describe, in particular the presence, or not, of peaks, and correspondingly the shape of the corresponding part of the envelope. These determinations may then be used as the basis of the conformity indication, and also the basis for attempting to identify the cause of any anomalies, in particular the identification of foreign components.