Hyperspectral Flow Cell Sensing for Noise-Robust Compound Concentration
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Solution Overview
Problem
Existing near-infrared spectroscopy (NIRS) methods for sensing compound concentrations in fluids suffer from poor repeatability due to environmental noise, affecting chemometric glucose determination models.
Innovation Solution
A non-contact system using hyperspectral imaging and machine learning, specifically a convolutional neural network, to predict compound concentrations by capturing hyperspectral images, preprocessing spectral signals, and applying reference corrections, enabling precise compound concentration monitoring in bioreactor fluids.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If near-infrared spectroscopy (NIRS) is used for sensing compound concentrations, then sensing capability is provided, but measurement precision deteriorates due to environmental noise
Solution Approach 1:
The patent transitions from conventional single-point NIRS measurement to hyperspectral imaging that captures spectral information across multiple dimensions (spatial x, spatial y, and spectral wavelength). This dimensional expansion allows the system to collect comprehensive spectral data from multiple locations simultaneously, enabling more robust concentration measurements that are less susceptible to environmental noise at any single point.
Solution Approach 2:
The patent combines multiple spectral measurements from different spatial locations into a single hyperspectral dataset. By merging information from numerous pixels across the field of view, the system creates a consolidated spectral signal that averages out environmental noise while preserving the compound concentration information, thereby improving measurement precision.
2Reliability
If conventional NIRS methods are used, then sensing operation is simple, but reliability deteriorates due to poor repeatability
Solution Approach 1:
The patent replaces conventional mechanical or contact-based sensing approaches with non-contact hyperspectral imaging. This substitution eliminates the need for physical probes or sensors that may introduce contamination or variability, thereby improving repeatability and reliability while maintaining operational simplicity through automated image-based measurement.
Solution Approach 2:
The patent creates optical copies (hyperspectral images) of the sample rather than making direct physical contact. By capturing spectral information through non-contact imaging, the system produces repeatable measurements that can be digitally stored and analyzed, eliminating variability introduced by physical sensor contact or environmental interference.
3Loss of substance
If contact-based sensing is used, then sensing capability is provided, but loss of substance increases due to cross-contamination risks
Solution Approach 1:
The patent introduces light as an intermediary between the sensor and the sample. Instead of direct physical contact between the sensing element and the fluid sample, the system uses electromagnetic radiation to transfer information about compound concentrations. This intermediary approach eliminates cross-contamination risks while maintaining high measurement precision through spectral analysis.
Solution Approach 2:
The patent replaces mechanical contact-based sensing with optical sensing. By substituting physical probe insertion with non-contact hyperspectral imaging, the system eliminates the pathway for cross-contamination while preserving the ability to accurately measure compound concentrations through spectral fingerprinting of the sample.
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
The system provides accurate and repeatable compound concentration predictions, reducing cross-contamination risks and enhancing monitoring capabilities in bioreactor environments.
Implementation Method 1
near-infrared spectroscopy (NIRS) only provides a single point of spectral information of the sample
Data Source
AI summary
A non-contact system for the sensing the concentration of a compound includes a hyperspectral imaging device configured to capture a hyperspectral image of a fluid, a flow cell configured to enable the capturing of a hyperspectral image of a fluid, a process, and a memory. The memory includes instructions stored thereon which, when executed by the processor, cause the system to generate a hyperspectral image of the fluid in the flow cell, generate several spectral signals based on the hyperspectral image, provide the spectral signal as an input to a machine learning network, and predict by the machine learning network the concentration of a compound in a fluid.