Nanoparticle Counting via Pulse Waveform Analysis and Segmentation
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Solution Overview
Problem
Existing analysis devices face challenges in accurately counting nanoparticles due to aggregation and assay noise, leading to incorrect detection of biological substances on optical discs, as they fail to distinguish between individual nanoparticles and those in close proximity or aggregates.
Innovation Solution
An analysis device and method that utilize an optical pickup to irradiate a sample analysis disc with a laser beam, detecting light-reception levels and employing a pulse detection circuit to differentiate between single and close nanoparticles, generating detection values and calculating aggregation values based on probability distributions to accurately count nanoparticles and assess their degree of aggregation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If nanobeads are used to label detection target substances on optical discs, then the detection sensitivity is improved, but the measurement precision deteriorates due to aggregation of nanobeads and optical interference between closely spaced nanobeads
Solution Approach 1:
The optical disc surface is divided into multiple unit sections, and the analysis is performed section by section. This segmentation allows the system to handle aggregated nanobeads within each section independently, reducing the impact of optical interference between sections and enabling more accurate counting even when nanobeads are aggregated.
Solution Approach 2:
The system changes the detection parameter from simple light reception level to pulse waveform analysis. By analyzing the temporal characteristics of light pulses (duration, amplitude, shape) rather than just intensity, the system can distinguish between single nanobeads and aggregates, and between nanobeads and assay noise, thereby improving measurement precision while maintaining detection sensitivity.
2Measurement precision
If the concentration of detection target substances is increased to improve detection accuracy, then the counting precision improves, but the aggregation of nanobeads increases causing erroneous counts
Solution Approach 1:
The system uses pulse waveform parameters (duration, amplitude, shape) to characterize nanobead signals. By analyzing these temporal parameters rather than just signal intensity, the system can identify and exclude aggregated nanobeads and assay noise from the count, allowing accurate measurement even at higher concentrations where aggregation is more likely to occur.
Solution Approach 2:
The system incorporates reference signals and statistical analysis to evaluate the distribution of pulse waveforms. By comparing the observed pulse characteristics against expected patterns and using feedback from the unit section analysis, the system can correct for aggregation effects and maintain counting precision across different concentration levels.
3Productivity
If assay procedures are simplified to reduce processing time, then the productivity improves, but assay noise increases leading to erroneous nanobead counts
Solution Approach 1:
The system uses the pulse waveform characteristics themselves to identify and exclude noise. By analyzing the temporal structure of signals (duration, amplitude variations, shape), the system can automatically distinguish between valid nanobead signals and assay noise without requiring additional complex filtering procedures, maintaining both speed and accuracy.
Solution Approach 2:
The analysis is divided into unit sections, allowing parallel processing and efficient evaluation. This segmentation enables the system to quickly process large numbers of signals by handling them in manageable units, reducing processing time while maintaining the ability to filter out assay noise through localized analysis of pulse waveforms in each section.
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 approach enables more precise counting of nanoparticles and reduces the impact of aggregation and assay noise, improving the accuracy of biological substance analysis by distinguishing between individual and aggregated nanoparticles, thereby enhancing the quality of nanoparticle labeling and detection.
Implementation Method 1
an optical pickup configured to irradiate a sample analysis disc with a laser beam and to detect a light-reception level of reflected light from a reaction region
Data Source
AI summary
A controller divides a reaction region into a plurality of unit sections. The controller generates first array data of a measured aggregate value of unit sections in which no nanoparticles exist, a measured aggregation value of unit sections in which a single nanoparticle exists, and measured aggregation values of unit sections in which 2 to n close nanoparticles exist. The controller generates second array data, based on a probability distribution according to a probability theory, in which the second array data most closely approximates the first array data. The controller generates a count value of the nanoparticles in the reaction region, based on the theoretical aggregation value of unit sections in which the single nanoparticle exists and the theoretical aggregation values of unit sections in which the 2 to n close nanoparticles exist.