Animal Feed Mixing Uniformity Detection via PCA
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Solution Overview
Problem
Existing methods for ensuring uniformity in animal feed mixtures in intensive farming are inefficient due to complex calibration requirements, high computing costs, and the need for chemical and physical parameter determination, leading to delayed indication of uniformity and reduced efficiency.
Innovation Solution
A process and apparatus using near-infrared spectroscopy and Principal Component Analysis (PCA) to detect electromagnetic spectra and determine the uniformity of animal feed mixtures without requiring chemical or physical parameter calculations, allowing for simple and efficient stopping of the mixing process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex calibration algorithms and chemical/physical parameter calculations are used to determine uniformity, then measurement precision is improved, but device complexity and computing cost increase
Solution Approach 1:
The patent extracts only the essential information needed for uniformity determination from the electromagnetic spectra, using Principal Component Analysis to identify and retain only the most significant variance components. This eliminates the need for complex full-spectrum calibration algorithms while maintaining measurement precision, directly resolving the contradiction between accuracy and system complexity.
Solution Approach 2:
The patent replaces complex mechanical calibration procedures and chemical/physical parameter calculations with an automated statistical analysis system based on Principal Component Analysis. This substitution eliminates manual calibration steps and reduces computing costs while maintaining or improving uniformity determination accuracy.
2Measurement precision
If chemical and physical parameter analysis processes are used to calculate nutrient element concentrations, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent performs Principal Component Analysis on a training set of spectra beforehand to establish the relationship between spectral variance and uniformity. During actual mixing, only the pre-identified principal components need to be calculated, enabling rapid real-time uniformity assessment without time-consuming chemical or physical parameter analysis, thus resolving the time-precision contradiction.
Solution Approach 2:
Instead of calculating all chemical and physical parameters to determine uniformity, the patent uses only the first few principal components that capture the majority of spectral variance. This partial analysis approach provides sufficient precision for uniformity determination while dramatically reducing computation time and eliminating the need for complete parameter calculation.
3Measurement precision
If complete chemical and physical parameter determination is performed, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The patent extracts only the critical variance information from electromagnetic spectra that is directly relevant to uniformity determination, discarding redundant spectral information. This extraction approach maintains measurement precision while enabling faster processing and improving overall mixing productivity.
Solution Approach 2:
The patent replaces time-consuming chemical and physical parameter determination methods with rapid statistical analysis of electromagnetic spectra using Principal Component_analysis. This substitution maintains uniformity assessment accuracy while dramatically improving mixing process efficiency and productivity.
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 quick and reliable determination of feed mixture uniformity with low computing costs, facilitating efficient mixing and distribution of nutrient elements to animals.
Implementation Method 1
a first and a second detection device arranged for detecting a first and a second sequence of electromagnetic spectra reflected by the food product
Data Source
Figure 1
Figure 2a~2d
Figure 2e~2h
AI summary
Process for mixing a food product for farm animals comprising a step of mixing a food product and two steps of detecting a first and a sequence of electromagnetic spectra reflected by the food product, leading to the acquisition of corresponding sets of detected data. The process also comprises a first calculation step of a plurality of principal components (PCi) by means of Principal Component Analysis (PCA) of the first set of detected data and a step of selecting a first principal component (PCI). The process also comprises a second step of calculating a second modified data set, comprising a plurality of first principal component values (PC1n), by means of Principal Component Analysis (PCA) of the second set of detected data, and a third step of calculating the standard deviation (PCldev) and a confidence interval (I) of the first principal component values (PC1n) on the basis of said standard deviation (PCldev). The process finally comprises a step of comparing the first principal component values (PC1n) with the confidence interval (I) and a step of stopping the mixing on the basis of such comparison.