Hyperspectral Meat Identification via Component Gradient Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for identifying raw meat and high-quality fake meat are ineffective, as they rely on appearance features and component comparisons that can be mimicked by fake meat, and existing technologies like chromatography, spectrometry, and molecular biotechnology are costly, time-consuming, and unable to distinguish between naturally formed and recombined meat samples.
Innovation Solution
A method using visible/near-infrared hyperspectral imaging and independent component analysis to identify raw meat and high-quality fake meat based on the gradual linear array change of components, constructing an identification model with a calibration set and prediction set, and applying a K-nearest neighbor algorithm for accurate differentiation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sensory inspection is used to identify meat based on appearance features, then identification can be performed quickly, but fake meat with same appearance and components cannot be distinguished
Solution Approach 1:
The patent changes the identification parameters from macroscopic appearance features to microscopic component distribution characteristics. By analyzing the spatial distribution and gradient changes of meat components at the pixel level, the method can distinguish fake meat that has identical appearance and bulk composition but different internal component distribution patterns
Solution Approach 2:
The patent introduces a new dimension of analysis by examining the spatial gradient distribution of components across the meat cross-section. Instead of only analyzing component presence/absence or bulk composition, the method analyzes how components are distributed and change gradually across space, adding a spatial dimension to the identification process
2Measurement precision
If chromatography or spectrometry is used for qualitative identification by comparing components, then identification can be performed, but it cannot distinguish recombined meat of same type with similar components
Solution Approach 1:
The patent applies local quality analysis by examining the spatial distribution characteristics of components at different locations within the meat cross-section. Fake meat made by recombining minced meat shows uniform or discontinuous component distribution, while natural meat shows continuous gradient changes. The method analyzes local component distribution patterns to distinguish between natural and recombined meat
3Reliability
If molecular biotechnology is used for detection, then reliable detection results can be obtained, but the cost is high, time consumption is long and operation is complicated
Solution Approach 1:
The patent replaces complex mechanical and chemical detection systems (molecular biotechnology) with an optical detection system using hyperspectral imaging. The method uses light interaction with meat components to obtain spectral information, which is then processed to identify meat authenticity. This substitution maintains high reliability while dramatically reducing time and operational complexity
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 allows for quick and accurate identification of raw meat and high-quality fake meat by characterizing the spatial changes in component rates, effectively distinguishing between naturally formed and artificially recombined meat samples despite similar appearances and components.
Implementation Method 1
with the sensitivities of visible/near-infrared hyperspectral signals for contents of components in the meat and spatial distributions thereof
Data Source
AI summary
The present invention relates to the technical field of identification on adulterated meat, and in particular, to a method for identifying raw meat and high-quality fake meat based on a gradual linear array change of a component. The present invention spatially characterizes changing rules of featured components in the meat with the utilization of sensitivities of the visible/near-infrared spectral signals to changes of the components in the meat and the advantage that spectral scanning can acquire optical signals of the samples spatially and consecutively, further constructs the identification model according to differences in components and spectra of a region of interest in the hyperspectral image by taking a derivative for characterizing rates of change of the featured components.