Polymicrobial Detection via Spectral Decomposition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for identifying microbial mixtures using spectrometry or spectroscopy are limited to single-type microorganisms, requiring time-consuming sample preparation and culture processes, and fail to accurately detect multiple microorganisms, leading to errors and inefficiencies.
Innovation Solution
A method that analyzes biological samples using a single measurement to detect and identify multiple microorganisms by constructing candidate models from reference intensity vectors, selecting the best model based on reconstruction error and complexity, and determining the presence of microorganisms through multidimensional digital signal processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If classification-based identification methods are used, then single microorganism identification is achieved, but polymicrobial mixture detection fails
Solution Approach 1:
The spectrum of a polymicrobial mixture is segmented into multiple spectral contributions, each corresponding to a different microorganism present in the sample. The algorithm decomposes the composite spectrum into individual microbial spectra, enabling identification of each component organism rather than treating the mixture as a single entity.
Solution Approach 2:
The method transitions from traditional single-dimension classification to a multi-dimensional spectral decomposition approach. By analyzing the spectrum as a combination of multiple reference spectra with varying weights, the system adds a dimensional layer of complexity that enables simultaneous identification of multiple microorganisms.
2Reliability
If biological treatment and isolation steps are implemented, then sample preparation is thorough, but analysis time increases significantly
Solution Approach 1:
The method extracts the identification capability directly from the raw spectral data without requiring physical extraction or isolation of individual microorganisms through culture methods. The spectral decomposition algorithm extracts information about each microorganism present in the mixture directly from the combined spectrum.
Solution Approach 2:
The patent replaces the mechanical/biological isolation system (culture media, incubation, colony separation) with a computational spectral decomposition system. Instead of physically separating microorganisms through biological processes, the algorithm separates their spectral signatures through mathematical decomposition.
3Quantity of substance
If culture media and incubation processes are used, then microorganism growth is achieved, but cost and complexity increase
Solution Approach 1:
The method enables direct analysis of the original sample without requiring the sample to undergo additional processing or growth steps. The spectral decomposition algorithm allows the sample to 'serve itself' by providing sufficient spectral information for identification of multiple microorganisms in their native mixture state.
4Measurement precision
If preliminary isolation steps are performed, then identification accuracy is improved, but productivity decreases
Solution Approach 1:
The spectral decomposition process enables continuous analysis of polymicrobial samples without interruption for isolation or culture steps. The algorithm processes the spectrum directly and continuously identifies multiple microorganisms, maintaining a continuous workflow rather than requiring discrete isolation and sequential analysis steps.
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 the detection and identification of multiple microorganisms in a biological sample with high accuracy and efficiency, reducing the need for extensive sample preparation and minimizing errors, by using a single measurement to reconstruct intensity vectors and determine taxon presence.
Implementation Method 1
using measurement techniques producing a multidimensional digital signal representative of the sample
Implementation Method 2
use spectrometry or spectroscopy to identify microorganisms
Data Source
Figure 1
Figure 2A
Figure 2B
AI summary
A method for detecting, in a biological sample, at least two microorganisms belonging to two different taxa, represented by intensity vectors Pj obtained by a multidimensional measuring technique, comprises: ■ acquiring a digital signal from the biological sample by means of measurement technology; ■ determining an intensity vector x on the basis of the acquired digital signal; ■ constructing a set {Ŷl} of candidate models Ŷl =(Ŷj, Ŷo)l modelling intensity vector x according to the equation: expression in which: expression in which: o ∀j ∈ [[1, K]], Pj (a)=:Σi K=1aijPi; and o ∀(i,j) ∈ [[1, K]]2, aij is a predefined coefficient; selecting a candidate model Ŷsel from set {Ŷl} according to the equation: Ŷsel= argmin(Cv (Ŷl) + Cc(Ŷl) expression in which: o Cv(Ŷl) is a criterion quantifying a reconstruction error between the intensity vector of biological sample x and the reconstruction of intensity vector xl by a candidate model (Ŷl); and o Cv(Ŷl) is a criterion quantifying the complexity of a candidate model Ŷl; and determining the presence in the biological sample of at least two taxa from when at least two components of vector Ŷj; of Ŷsel are greater than a positive threshold.