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Sample Consensus Model and Unsupervised Variable Consensus Model for Improving the Accuracy of a Calibration Model.

Zhou Xu¹, Xiaojing Chen², Liuwei Meng³

¹1 National and Local Joint Engineering Research Center of Reliability Analysis and Testing for Mechanical and Electrical Products, Zhejiang Sci-Tech University, Hangzhou, China.

Applied Spectroscopy

|June 1, 2019

Summary

This summary is machine-generated.

Two consensus modeling methods improve spectral data calibration accuracy by leveraging sample and variable information, overcoming challenges of small sample sizes and high dimensionality in quantitative analysis.

Keywords:

Consensus strategy Kohonen self-organizing map high-dimension variable partial least squares regression small sample size

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Area of Science:

Quantitative analysis
Chemometrics
Spectroscopy

Background:

Small sample size and high dimensionality in spectral data analysis often reduce calibration model accuracy.
Partial Least Squares Regression (PLSR) is a common but sometimes limited method for spectral data.
Developing robust calibration models is crucial for accurate quantitative analysis of spectral data.

Purpose of the Study:

To address the poor accuracy of calibration models caused by small sample size and high dimensionality.
To propose and evaluate two novel consensus modeling strategies: sample consensus and unsupervised variable consensus models.
To enhance the predictive performance of quantitative spectral analysis.

Main Methods:

Developed sample consensus and unsupervised variable consensus models.
Utilized Partial Least Squares Regression (PLSR) as the base modeling technique.
Employed Monte Carlo sampling and self-organizing map (SOM) for unsupervised variable clustering.
Applied consensus modeling strategy by weighting multiple PLSR sub-models.

Main Results:

Both sample consensus and unsupervised variable consensus models significantly improved calibration model accuracy.
The proposed methods demonstrated superior performance compared to a single PLSR model.
Effectiveness was validated using three public near-infrared (NIR) or infrared (IR) spectroscopy datasets (corn, wine, soil).

Conclusions:

Sample and unsupervised variable consensus models offer a new approach for accurate quantitative spectral analysis.
These methods effectively utilize both sample and variable information for improved model performance.
The findings provide a pathway to achieve more accurate results in spectral data analysis.