Fluid Sample Classification via Automated Profile Analysis
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Solution Overview
Problem
Current methods for analyzing complex separation profiles in biopharmaceutical manufacturing are time-consuming, costly, and prone to operator bias, especially when dealing with overlapping molecule-peak bands.
Innovation Solution
A computer-assisted method for classifying samples based on a two-dimensional analysis of relative amount and constituent profile similarity to a reference sample, using a similarity score that can be automated and implemented using computer analysis software.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated computer analysis is implemented, then analysis speed and objectivity improve, but initial implementation cost and complexity increase
Solution Approach 1:
The patent replaces manual visual inspection and operator-based analysis with automated computer analysis software that processes separation profiles. The system uses algorithmic comparison of relative amounts and constituent profiles against reference samples, eliminating the need for operator interpretation while maintaining high accuracy in sample classification.
Solution Approach 2:
The patent creates digital copies of reference samples and separates their constituents to establish reference profiles. These digital reference profiles are then used for automated comparison with test samples, allowing rapid classification without repeatedly analyzing physical reference materials.
2Measurement precision
If detailed analysis of overlapping peaks is performed, then measurement precision improves, but analysis time and cost increase
Solution Approach 1:
The patent applies a two-dimensional analysis that evaluates both relative amounts and constituent profiles simultaneously. This partial analysis approach focuses on the most critical comparison dimensions rather than attempting to resolve every overlapping peak individually, achieving sufficient precision for sample classification while maintaining rapid processing speeds.
Solution Approach 2:
The patent introduces a two-dimensional analysis framework that compares samples based on relative amount (one dimension) and constituent profile similarity (second dimension). This dimensional approach allows the system to classify samples effectively without needing to fully resolve complex overlapping peaks in the traditional single-dimensional peak analysis.
3Adaptability or versatility
If manual operator analysis is used, then flexibility in handling complex cases is maintained, but operator bias is introduced and consistency decreases
Solution Approach 1:
The patent implements a feedback mechanism where the automated analysis system compares test samples against stored reference profiles and provides classification results. The system can be configured with classification criteria and thresholds, and the reference profiles themselves can be updated based on accumulated data, allowing the system to adapt to different sample types while maintaining consistent automated analysis.
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
This method enables fast, non-operator dependent classification, reducing manufacturing time and costs, while minimizing bias and allowing for efficient decision-making in biopharmaceutical processes.
Implementation Method 1
chromatographic separation steps are usually needed to remove various contaminants and impurities from the product
Implementation Method 2
either by chromatography or electrophoresis
Data Source
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AI summary
Disclosed is a method 100 for classifying a fluid sample. The method comprises the steps of: a. at least partially separating 110 one or more of the chemical constituents of the fluid sample; b. measuring and recording 120 the amount of separated chemical constituents of the sample during, or after, the chemical separation; c. measuring and recording 130 the spatial or time separation profile of sample constituents, during or after separation, and providing a data set of the same; d. comparing 140 the amount of said separated constituents to one or more reference samples; e. comparing 150 the spatial or time separation profile to the corresponding profile of the or each reference sample; f. assigning 160 a similarity score to the sample based on the similarity of the amount or the profile comparisons of the separated constituents, as performed under steps d 140 and e 150 above, or both, with the equivalent amount and/or profile of the or each reference sample respectively; g. providing 170 a classification of the sample based on the similarity score.