Chip-Based Spectrometer Authentication Using Tuned LED Illumination
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Solution Overview
Problem
Existing systems for authenticating and classifying materials at the molecular level are expensive, require expert operators, and are limited in accessibility and practicality.
Innovation Solution
The use of chip-based spectrometers combined with a tuned set of light-emitting diodes (LEDs) to enhance the detection of absorption and fluorescence spectra, along with machine learning models processed locally on mobile devices or system-on-chip (SOC) hardware, to classify light spectra and construct a unique spectral fingerprint.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional spectrometers (UV, FTIR, NMR) are used for material classification, then measurement precision is improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent segments the spectral analysis task by using multiple discrete LED light sources at specific wavelengths (UV, visible, NIR ranges) rather than requiring a continuous spectrum from a complex traditional spectrometer. Each LED provides a targeted illumination for detecting specific molecular vibrations or fluorescence, simplifying the overall system while maintaining classification accuracy.
Solution Approach 2:
The patent creates a universal authentication system that can classify multiple types of materials (food, pharmaceuticals, chemicals) using a single integrated device with multiple LED sources and a common image sensor. This multi-functional approach eliminates the need for separate specialized spectrometers for different material types, reducing device complexity while maintaining precision across diverse applications.
2Measurement precision
If traditional spectrometers are used for authentication, then measurement precision is improved, but ease of operation deteriorates due to expert knowledge requirements
Solution Approach 1:
The patent implements self-service operation through automated machine learning classification. The system automatically captures spectral data from multiple LED illuminations, processes the information through pre-trained AI models, and generates authentication results without requiring user intervention or expert knowledge. The image sensor and processing unit work autonomously to complete the authentication task.
Solution Approach 2:
The patent replaces the mechanical complexity of traditional spectrometer operation with electronic and computational methods. Instead of requiring manual adjustment of optical components and expert interpretation of spectral patterns, the system uses LED illumination, image sensing, and machine learning algorithms to automatically perform authentication, significantly improving ease of operation.
3Device complexity
If chip-based spectrometers with LED sources are used, then device complexity is reduced, but measurement precision may deteriorate
Solution Approach 1:
The patent applies local quality by using multiple LED light sources with specific wavelength characteristics targeted at different spectral regions (UV for fluorescence, visible for color analysis, NIR for molecular vibrations). Each LED provides optimized illumination for detecting specific material properties, ensuring that the simplified chip-based system achieves precision comparable to traditional spectrometers through strategically selected local spectral measurements.
Solution Approach 2:
The patent compensates for the limited spectral resolution of chip-based spectrometers by adding the dimension of multiple illumination wavelengths. Instead of relying on a single continuous spectrum, the system captures data across multiple discrete wavelength regions, creating a comprehensive spectral profile that maintains authentication accuracy while using simpler hardware.
4Adaptability or versatility
If multiple LED light sources are used for comprehensive spectral analysis, then adaptability is improved, but use of energy increases
Solution Approach 1:
The patent employs periodic action by sequentially activating different LED light sources rather than illuminating all wavelengths simultaneously. The system activates UV LEDs, visible LEDs, and NIR LEDs in sequence during the measurement process, allowing comprehensive spectral analysis while minimizing total energy consumption compared to continuous multi-wavelength illumination.
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 provides a cost-effective and user-friendly method for authenticating and classifying materials, reducing dependency on specialized equipment and expertise, and enabling broad applicability across various industries.
Implementation Method 1
enhance the detection of absorption and fluorescence spectra
Implementation Method 2
enhance the detection of absorption and fluorescence spectra
Data Source
AI summary
A system and method for authenticating and classifying products using hyper-spectral imaging is disclosed. In some implementation, the method comprises: illuminating a sample material with light emitted from a plurality of light-emitting diodes (LEDs); collecting spectra of light reflected, transmitted, or emitted by the sample material using a chip-based spectrometer; processing the collected spectra with a computing device to determine a numerical difference between sample spectra and reference spectra from a training set; classifying the sample material based on the numerical difference using a machine learning model stored in the computing device; generating a unique spectral fingerprint of the sample material for authentication purposes; and providing an output indicating the authenticity of the sample material.