Chromatography Reference Standards for Automated Anomaly Diagnosis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Chromatography systems face challenges in accurately detecting and diagnosing anomalous operations due to variations in instrumentation, environmental conditions, and operator errors, which can lead to incorrect analyte detection and severe consequences in medical diagnosis applications.
Innovation Solution
A method for anomaly detection and diagnosis in chromatography systems involves performing a chromatography analysis with a known reference standard, determining peak information, and assessing whether it conforms with expected responses to identify and diagnose anomalies in the sample handling, separation, or detection units, with the option to apply corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional chromatography analysis methods are used, then quantitative information can be obtained for enhancing system performance, but the system cannot reliably detect or diagnose anomalous operations
Solution Approach 1:
The system performs preliminary actions by injecting reference standards before actual sample analysis to establish expected response patterns. This preliminary characterization of system performance enables subsequent anomaly detection without requiring complex real-time monitoring hardware, as the baseline is established in advance through standard injections.
Solution Approach 2:
The system implements feedback by continuously comparing actual chromatogram responses against expected responses derived from reference standards. When deviations are detected, the system generates diagnostic information about potential anomalies in sample handling, separation, or detection units, enabling corrective actions to be taken based on this feedback loop.
2Measurement precision
If tight control over operating parameters is maintained to ensure accurate and reliable analysis, then measurement precision is improved, but the complexity of operation increases
Solution Approach 1:
The system performs self-service by automatically monitoring its own performance through reference standard injections and comparing actual responses against expected responses. This self-diagnosis capability reduces the need for operator intervention and expertise in troubleshooting, as the system independently identifies anomalies in sample handling, separation, or detection units.
Solution Approach 2:
The system uses feedback mechanisms to automatically detect deviations from expected performance patterns. By continuously monitoring chromatogram responses and comparing them against established baselines, the system maintains measurement precision while reducing operational complexity through automated anomaly detection and diagnostic reporting.
3Measurement precision
If reference standards are used for quantitative information, then measurement precision is improved, but the system cannot distinguish between normal variation and actual anomalies
Solution Approach 1:
The system applies segmentation by dividing the anomaly detection task into distinct segments corresponding to different system units: sample handling unit, chromatographic separation unit, and detection unit. Each segment is evaluated independently by analyzing specific aspects of the chromatogram response, enabling precise identification of where anomalies occur without confusion from overall system variations.
Solution Approach 2:
The system implements local quality by examining specific local characteristics of chromatogram responses rather than relying solely on overall quantitative metrics. By analyzing localized features such as peak shapes, retention times, and response patterns in different regions of the chromatogram, the system can distinguish between normal quantitative variations and actual anomalies in specific system units.
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
Enables real-time, automatic detection and diagnosis of anomalies, improving the reliability and accuracy of chromatography systems by identifying and correcting operational issues, thereby reducing the risk of misdiagnosis in medical applications.
Implementation Method 1
Chromatography has been used for decades in a wide variety of fields and industries for separating and analyzing constituents of a sample. The sample is typically dissolved or otherwise introduced in a fluid to form a mobile phase, which is carried through a column or another separation device containing a stationary phase.
Data Source
AI summary
A method for anomaly detection and diagnosis in a chromatography system including a sample handling unit, a chromatographic separation unit, and a detection unit is disclosed. The method can include performing, with the chromatography system, a chromatography analysis of a sample to obtain a chromatogram of the sample, the sample including a known quantity of a reference standard. The method can also include determining peak information corresponding to the reference standard in the chromatogram and determining whether the peak information conforms with an expected response of the chromatography system associated with the reference standard. If the peak information does not conform with the expected response, the method can include determining that there is an anomaly in the operation of the chromatography system and diagnosing a cause of the anomaly as relating to the operation of at least one the sample handling unit, the chromatographic separation unit, and the detection unit.