Multi-Wavelength Optical Detection for Fusarium Kernel Separation
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Solution Overview
Problem
Current methods for detecting and removing fusarium-infected kernels from grain are inefficient, as they fail to accurately distinguish between healthy and infected kernels based on light reflection and scattering, leading to residual mycotoxins in the grain supply, which affects food safety and economic value.
Innovation Solution
A method and apparatus that utilize distinct wavelengths of light to measure and compare the amplitude of reflected and scattered light from kernels, using statistical analysis to set a threshold for identifying infected kernels, and a mechanical system to separate them based on these criteria, with a light source emitting at 505 nm and 590 nm wavelengths and a microprocessor to determine kernel infection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If single wavelength light detection is used, then device complexity is reduced, but measurement precision deteriorates due to inability to distinguish healthy and infected kernels
Solution Approach 1:
The patent transitions from single-wavelength detection to multi-wavelength detection, adding a spectral dimension to the measurement. By measuring light reflection at multiple wavelengths (e.g., 450 nm, 530 nm, 630 nm), the system creates a spectral signature for each kernel that enables differentiation between healthy and infected kernels, thereby improving measurement precision without excessive complexity increase.
Solution Approach 2:
The patent changes the parameter of light wavelength from a single value to multiple discrete values. By selecting specific wavelengths where infected and healthy kernels exhibit different reflectance characteristics, the system enhances detection accuracy. The use of multiple wavelengths provides additional discriminatory parameters that improve kernel classification.
2Productivity
If simple sieving method is used, then device complexity is reduced, but productivity deteriorates due to incomplete removal of infected kernels
Solution Approach 1:
The patent replaces purely mechanical sieving with an optical detection and mechanical ejection system. Light sources illuminate kernels, photodetectors measure reflection characteristics, and a control system actuates ejection mechanisms to remove infected kernels. This substitution of mechanical sorting with optical sensing improves productivity by enabling more accurate and faster identification of infected kernels.
Solution Approach 2:
The system allows infected kernels to be automatically identified and ejected without manual intervention. The optical detection system continuously monitors passing kernels, and the control system automatically triggers ejection when infection is detected, enabling self-service operation that improves processing efficiency.
3Measurement precision
If multi-wavelength detection is used, then measurement precision is improved, but use of energy increases due to multiple light sources
Solution Approach 1:
The patent employs periodic or pulsed illumination instead of continuous multi-wavelength lighting. Light sources are activated in sequence at different wavelengths, with each pulse lasting only long enough to capture the reflection signal. This periodic action reduces overall energy consumption compared to continuous illumination while maintaining the benefits of multi-wavelength detection for improved measurement precision.
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
Effectively reduces mycotoxin content in grain by accurately identifying and removing infected kernels, achieving a 93% correct identification rate and reducing mycotoxin levels by 84% on average, improving grain quality and food safety.
Implementation Method 1
a light source emitting at 505 nm and 590 nm wavelengths
Implementation Method 2
The amplitude of the reflected and scattered light is measured by a detector
Data Source
AI summary
Fusarium infected grain is separated by comparing reflected and transmitted light at two wavelengths, one at which the light is substantially reflected and scattered the same by healthy and infected kernels, the other at which the light is reflected and scattered to a significantly greater degree by infected than healthy kernels. An apparatus having a rotating apertured cylinder, with a low internal vacuum, allows comparison of individual kernels. When comparison indicates that a kernel is infected, a lever dislodges it from the cylinder allowing it to fall into a receptacle for infected kernels. Kernels remaining on the cylinder are scraped off to fall into a receptacle for healthy kernels. Although results vary, to some extent depending on the degree of infection, approximately 90% of healthy kernels and 5% of infected kernels are deemed “healthy”, while approximately 10% of healthy kernels and 95% of infected kernels are deemed “infected,” reducing the level of infected kernels.