Comparison of Machine Learning Algorithms for Predictive Modeling of Beef Attributes Using Rapid Evaporative Ionization Mass Spectrometry (REIMS) Data

Ambient mass spectrometry is an analytical approach that enables ionization of molecules under open-air conditions with no sample preparation and very fast sampling times. Rapid evaporative ionization mass spectrometry (REIMS) is a relatively new type of ambient mass spectrometry that has demonstrat...

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Bibliographic Details
Published in:Scientific reports 2019-04, Vol.9 (1), p.5721-5721, Article 5721
Main Authors: Gredell, Devin A., Schroeder, Amelia R., Belk, Keith E., Broeckling, Corey D., Heuberger, Adam L., Kim, Soo-Young, King, D. Andy, Shackelford, Steven D., Sharp, Julia L., Wheeler, Tommy L., Woerner, Dale R., Prenni, Jessica E.
Format: Article
Language:English
Subjects:
631/61/320
639/638/11/296
639/705/531
Algorithms
Artificial intelligence
Beef
Classification
Food Analysis - methods
Humanities and Social Sciences
Ionization
Ions
Learning algorithms
Machine Learning
Mass Spectrometry
Mass spectroscopy
multidisciplinary
Prediction models
Red Meat
Sample preparation
Science
Science (multidisciplinary)
Scientific imaging
United States
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Summary:Ambient mass spectrometry is an analytical approach that enables ionization of molecules under open-air conditions with no sample preparation and very fast sampling times. Rapid evaporative ionization mass spectrometry (REIMS) is a relatively new type of ambient mass spectrometry that has demonstrated applications in both human health and food science. Here, we present an evaluation of REIMS as a tool to generate molecular scale information as an objective measure for the assessment of beef quality attributes. Eight different machine learning algorithms were compared to generate predictive models using REIMS data to classify beef quality attributes based on the United States Department of Agriculture (USDA) quality grade, production background, breed type and muscle tenderness. The results revealed that the optimal machine learning algorithm, as assessed by predictive accuracy, was different depending on the classification problem, suggesting that a “one size fits all” approach to developing predictive models from REIMS data is not appropriate. The highest performing models for each classification achieved prediction accuracies between 81.5–99%, indicating the potential of the approach to complement current methods for classifying quality attributes in beef.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-019-40927-6