Evidence map›Paper›PMID 30952873›Full record

ArticleScientific reports2019

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

Devin A Gredell, Amelia R Schroeder, Keith E Belk, Corey D Broeckling, Adam L Heuberger, Soo-Young Kim, D Andy King, Steven D Shackelford, Julia L Sharp, Tommy L Wheeler and 2 more

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

26 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Devin A GredellCenter for Meat Safety and Quality, Department of Animal Sciences, Colorado State University, Fort Collins, CO, 80523, USA.
Amelia R SchroederDepartment of Mathematics and Statistics, East Tennessee State University, Johnson City, TN, 37614, USA.
Keith E BelkCenter for Meat Safety and Quality, Department of Animal Sciences, Colorado State University, Fort Collins, CO, 80523, USA.
Corey D BroecklingProteomics and Metabolomics Facility, Colorado State University, Fort Collins, CO, 80523, USA.ORCID http://orcid.org/0000-0002-6158-827X
Adam L HeubergerDepartment of Horticulture and Landscape Architecture, Colorado State University, Fort Collins, CO, 80523, USA.
Soo-Young KimDepartment of Statistics, Colorado State University, Fort Collins, CO, 80523, USA.
D Andy KingUSDA-ARS U.S. Meat Animal Research Center, Clay Center, NE, 68933, USA.
Steven D ShackelfordUSDA-ARS U.S. Meat Animal Research Center, Clay Center, NE, 68933, USA.
Julia L SharpDepartment of Statistics, Colorado State University, Fort Collins, CO, 80523, USA.
Tommy L WheelerUSDA-ARS U.S. Meat Animal Research Center, Clay Center, NE, 68933, USA.
Dale R WoernerCenter for Meat Safety and Quality, Department of Animal Sciences, Colorado State University, Fort Collins, CO, 80523, USA.
Jessica E PrenniDepartment of Horticulture and Landscape Architecture, Colorado State University, Fort Collins, CO, 80523, USA. jprenni@colostate.edu.ORCID http://orcid.org/0000-0002-0337-8450

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Red MeatAlgorithmsFood AnalysisMachine LearningMass SpectrometryUnited States

Identifiers

PMID30952873
PMCPMC6450883

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.