NIR Feedstuff Prediction with Outlier-Filtered Spectrum Matching
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Solution Overview
Problem
Existing methods for identifying and predicting feedstuff raw materials using near infrared spectroscopy are prone to errors due to human mistakes, instrumental inaccuracies, and data heterogeneity, leading to incorrect classifications and misleading results.
Innovation Solution
A computer-implemented method that removes outliers from a database of known feedstuff spectra before similarity analysis with an unknown sample's spectrum, using techniques like pairwise correlation, average dissimilarity, centroid-based removal, and derivative analysis to enhance precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If near infrared spectroscopy is used for routine identification of feedstuffs, then the method is suitable for regular use, but human mistakes in selection lead to incorrect classification and erroneous results
Solution Approach 1:
The system performs preliminary classification of the unknown spectrum against a database of reference spectra before proceeding with quantitative analysis. This preliminary action identifies the most likely feedstuff type, enabling the system to select the appropriate calibration model automatically and prevent human selection errors.
Solution Approach 2:
The system provides feedback by comparing the unknown spectrum with reference spectra and presenting the most likely matches with confidence levels. This feedback mechanism allows operators to verify classifications and correct potential errors before final analysis, improving both reliability and user confidence.
2Adaptability or versatility
If similarity search is performed on the full database, then all possible matches are considered, but false positives reduce prediction reliability
Solution Approach 1:
The system applies local quality by focusing the similarity search on a subset of reference spectra that are most relevant to the unknown sample. Based on initial spectral characteristics, the system identifies and prioritizes matches from specific regions of the database, reducing false positives while maintaining comprehensive coverage of likely candidates.
Solution Approach 2:
The system dynamically adjusts similarity search parameters such as tolerance thresholds and weighting factors based on the characteristics of the unknown spectrum and the database contents. This parameter optimization balances comprehensive matching with false positive reduction, improving prediction reliability without sacrificing adaptability.
3Productivity
If quantitative analysis is performed without preliminary classification, then analysis can proceed directly, but incorrect calibration selection leads to erroneous results
Solution Approach 1:
The system performs preliminary classification to identify the feedstuff type and select the appropriate calibration model before conducting quantitative analysis. This preliminary step ensures that the correct calibration parameters and algorithms are applied, maintaining measurement precision while enabling direct analysis through automated model selection.
Solution Approach 2:
The system achieves universality by implementing a single integrated platform that handles both qualitative classification and quantitative analysis. The automated calibration selection based on preliminary classification allows the same system to serve multiple functions accurately, maintaining precision across different feedstuff types without requiring separate analysis pathways.
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 method significantly reduces the likelihood of incorrect assignments, resulting in more precise and reliable predictions of feedstuff raw materials.
Implementation Method 1
subjecting a sample of an unknown feedstuff raw material and/or feedstuff to near infrared spectroscopy
Data Source
AI summary
A method for predicting a feedstuff and/or feedstuff raw material is described. The method comprises providing a near infrared (NIR) spectrum of a sample of an unknown feedstuff raw material and/or feedstuff. The absorption intensities of wavelengths or wavenumbers in the spectrum are transformed to give a query vector. A set of database vectors of a population of spectra of known feedstuff raw materials and/or feedstuffs is also provided, and an outlier database vector is removed based on different comparison methods. The similarity between the query vector and each of database vectors is analyzed to produce a similarity value, and the feedstuff raw material and/or feedstuff of the database vector with the highest similarity is assigned to the sample.