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A Strategy for Sensitive, Large Scale Quantitative Metabolomics

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Published on: May 27, 2014

Multiple and Optimal Screening Subset: a method selecting global characteristic congeners for robust foodomics

Rui Xu^1,2, Huan Zhang^1,2, Michael W Crowder³

¹Human Nutrition Program, Department of Human Sciences, The Ohio State University, Columbus, Ohio, USA 43210.

Briefings in Bioinformatics

|February 22, 2024

Summary

A new Multiple and Optimal Screening Subset (MOSS) method improves feature selection in metabolomics and foodomics. MOSS balances predictor numbers and accuracy, enhancing predictive models for complex analyses like bourbon classification.

Keywords:

foodomics mass spectrometry metabolomics molecular feature selection optimal screening

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A Strategy for Sensitive, Large Scale Quantitative Metabolomics

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Published on: May 27, 2014

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Published on: July 27, 2021

Area of Science:

Metabolomics and Foodomics
Analytical Chemistry
Chemometrics

Background:

Traditional feature selection methods in metabolomics and foodomics can lead to arbitrary and suboptimal model results.
The complexity of food matrices requires advanced analytical techniques for comprehensive molecular analysis.

Purpose of the Study:

To develop and validate a Multiple and Optimal Screening Subset (MOSS) approach for efficient and effective feature selection.
To compare the performance of MOSS against traditional methods in statistical model setup for food analysis.
To achieve a balance between a minimal number of predictors and high predictive accuracy.

Main Methods:

Developed the Multiple and Optimal Screening Subset (MOSS) approach.
Compared five statistical models: Student's t-test, ROC curve, PLS-DA, random forests, and SVM.
Utilized cross-validation to identify feature candidates and determine optimal subset size.
Analyzed 1406 mass spectral features from 122 bourbon samples.

Main Results:

MOSS generated a feature subset for bourbon age prediction with 88% accuracy.
MOSS improved the area under the curve for sweetness prediction to 0.898 using only four predictors.
Compared to top-ranked features (AUC 0.681), MOSS demonstrated superior performance.

Conclusions:

The MOSS approach offers an efficient and effective method for optimal feature selection in metabolomics and foodomics.
MOSS enhances predictive accuracy while minimizing the number of predictors used in statistical models.
This method provides a robust alternative to traditional, potentially arbitrary, feature selection techniques.