Comparison of machine learning algorithms for predictive modeling of beef attributes using rapid evaporative ionization mass spectrometry (REIMS) data

Authors: GREDELL, DEVIN - Colorado State University; SCHROEDER, AMELIA - East Tennessee State University; BELK, KEITH - Colorado State University; BROECKLING, COREY - Colorado State University; HEUBERGER, ADAM - Colorado State University; KIM, SOO-YOUNG - Colorado State University; King, David - Andy; Shackelford, Steven; SHARP, JULIA - Colorado State University; Wheeler, Tommy; WOERNER, DALE - Colorado State University; PRENNI, JESSICA - Colorado State University

Submitted to: Scientific Reports
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/26/2019
Publication Date: 4/5/2019
Citation: Gredell, D.A., Schroeder, A.R., Belk, K.E., Broeckling, C.D., Heuberger, A.L., Kim, S., King, D.A., Shackelford, S.D., Sharp, J.L., Wheeler, T.L., Woerner, D.R., Prenni, J.E. 2019. Comparison of machine learning algorithms for predictive modeling of beef attributes using rapid evaporative ionization mass spectrometry (REIMS) data. Scientific Reports. 9:5721. https://doi.org/10.1038/s41598-019-40927-6.
DOI: https://doi.org/10.1038/s41598-019-40927-6

Interpretive Summary: Online commercial meat quality assessment has progressed to instrument measurement of visual attributes. The next major step involves real-time assessment of chemical components of meat associated with important quality attributes. Rapid evaporative ionization mass spectrometry (REIMS) is a relatively new technology initially developed with biomedical applications in mind, but has also proven to be valuable for the analysis of food. Here, we present an evaluation of REIMS as an objective measure for the assessment of beef quality attributes. Eight different machine learning algorithms were compared for the generation of predictive models using REIMS data to classify USDA quality grade, production background, breed type and muscle tenderness. Prediction accuracies ranged between 81.5 to 99%, indicating the potential of the approach to complement current methods for classifying quality attributes in beef. These results lay the groundwork for future evaluation of REIMS in an on-line production setting to complement current meat classification methodologies and enable objective sorting and verification of meat products by attributes with high economic value.

Technical Abstract: 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.