Bioparticle Classification from Phase-Domain Backscattered Light
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing optical-based detection techniques for bioparticles, such as those used in healthcare and pharmaceutical systems, often discard phase information from back-scattering signals due to computational challenges, limiting their ability to accurately discriminate between different types of particles, particularly in complex biological samples.
Innovation Solution
A method and device that utilize phase analysis of back-scattering signals, specifically through optical tweezers, to extract phase-domain coefficients using transforms like Fourier, Hilbert, or Hartley, and apply these to pre-train a machine learning classifier for accurate particle detection and classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If phase information from back-scattering signals is discarded to simplify computational processing, then device complexity is reduced, but measurement precision and particle discrimination accuracy deteriorate
Solution Approach 1:
The patent extracts and utilizes specifically the phase component of back-scattering signals while separating it from magnitude information. By focusing only on the phase domain through phase-domain transforms, the system extracts the informative component that provides superior particle discrimination without needing to process the entire complex signal, thus resolving the contradiction between computational simplicity and measurement precision.
Solution Approach 2:
The patent transitions from analyzing signals in the time or frequency domain to analyzing them in the phase domain. This dimensional change in signal representation allows the system to access previously unused information contained in the phase component, achieving enhanced particle discrimination accuracy without proportionally increasing computational complexity.
2Measurement precision
If phase analysis is implemented to improve particle classification accuracy, then measurement precision is improved, but computational complexity increases
Solution Approach 1:
The patent replaces complex general-purpose signal processing mechanisms with specialized phase-domain transform operations. By substituting standard Fourier magnitude analysis with phase-specific transforms (Fourier, Hilbert, or Hartley phase extraction), the system achieves high classification accuracy through more efficient, targeted computational operations rather than brute-force complex signal analysis.
Solution Approach 2:
The patent changes the fundamental parameter being analyzed from magnitude or full complex signal to phase angle information. This parameter change enables the system to achieve superior particle classification by focusing computational resources on the phase component, which contains distinctive particle characteristics, rather than processing all signal parameters equally.
3Ease of operation
If standard magnitude-based signal analysis is used, then ease of operation is maintained, but the ability to discriminate complex bioparticles deteriorates
Solution Approach 1:
The patent applies phase-domain transforms as a preliminary processing step before classification. By pre-processing the back-scattering signals to extract phase information and represent particles in the phase domain, the system prepares the data in an optimized format that enhances subsequent classification performance while maintaining operational simplicity through automated pipeline processing.
Solution Approach 2:
The patent introduces phase-domain coefficients as an intermediary representation between the raw back-scattering signal and the final classification decision. This intermediate phase-based feature set serves as a bridge that translates complex optical signals into discriminative parameters, improving bioparticle discrimination while keeping the overall system operationally simple through automated feature extraction.
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
Enhances the discriminative potential for identifying and differentiating micron- or nano-sized bioparticles, especially complex cancer cells, with improved accuracy and sensitivity, suitable for point-of-care diagnostics and in-vivo biosensing.
Implementation Method 1
using optical tweezers, for example using miniaturized and integrated optical tweezers like optical fiber tweezers
Implementation Method 2
acquiring a temporal signal from laser light backscattered by each specimen
Data Source
AI summary
A method and device for detecting and/or classifying particles of organic based compounds, i.e. bioparticles, from a backscattered light fingerprint, in a liquid dispersion sample, said method using an electronic data processor for detecting and/or classifying particles in the sample, the method uses the electronic data processor for pre-training a machine learning classifier with a plurality of specimen particles, and includes the steps of: emitting a laser modulated by a modulation frequency onto each specimen; acquiring a temporal signal from laser light backscattered by each specimen for a plurality of temporal periods of a predetermined duration for each specimen; calculating specimen phase-domain coefficients from the acquired specimen signal for each of the temporal periods by applying a phase-domain transform; and using the calculated specimen coefficients to pre-train the machine learning classifier for detecting and/or classifying the particles.