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

Gredell, Devin A.; Schroeder, Amelia; Belk, K. E.; Broeckling, Corey D.; Heuberger, Adam L.; Kim, Soo‐Young; King, D. A.; Shackelford, S. D.; Sharp, Julia L.; Wheeler, T. L.; Woerner, D. R.; Prenni, Jessica E.

doi:10.1201/9780429427879-16

Cited by 6 publications

(12 citation statements)

References 1 publication

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“…The resulting data sets are usually very large and multivariate statistical analyses are needed to analyse the data. As an example of the data sets resulting from REIMS, one was reported to consist of 583 samples with each sample consisting of over 4000 data points [ 28 ].…”

Section: What Is Reims?mentioning

confidence: 99%

“…PLSDA is not the only technique which can be used for classification of REIMS data. Other classification techniques (support vector machine with a linear kernel, radial kernel, and polynomial kernel, random forest, K-nearest neighbour, linear and penalized discriminant analysis, extreme gradient boosting, logistic boosting) along with PLSDA have been compared in one study but mixed results were found with the use of the different techniques [ 28 ]. The authors found that not one algorithm could be universally applied for the data set and concluded that that a “one size fits all” approach was not optimal for developing classification models for REIMS data [ 28 ].…”

Section: What Is Reims?mentioning

confidence: 99%

“…As previously mentioned, a range of classification systems or machine learning algorithms can be used to develop predictive models with the performance of each algorithm potentially specific to the attribute being evaluated (e.g., production system or breed). For example, a number of different algorithms were evaluated to predict a range of beef attributes including quality grade, production system, breed type, and muscle tenderness and it was found prediction accuracies ranging from 81.5% to 99% were obtainable depending on the algorithm and attribute [ 28 ]. It is also feasible that, with the potential for REIMS to sample remotely from the MS, multiple algorithms could be deployed on the data after collection as part of an in-line product assessment system, and allows objective classification and verification of attributes as part of a broader supply chain integrity system.…”

Section: Provenancementioning

confidence: 99%

“…Currently, in the Australian beef industry demand exists for a rapid method that can determine levels of fatty acids to distinguish these characteristics on a carcass. Some preliminary evidence has shown REIMS could be used for breed identification on beef minced meat and longissimus muscles [ 7 , 28 ]. Using REIMS with a variety of machine learning algorithm predictive models, Angus have shown a clear separation from other breeds, like Wagyu, where the Angus breed type was predicted with greater than 80% accuracy [ 28 ].…”

Section: Qualitymentioning

confidence: 99%

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Rapid Evaporative Ionization Mass Spectrometry: A Review on Its Application to the Red Meat Industry with an Australian Context

et al. 2021

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The red meat supply chain is a complex network transferring product from producers to consumers in a safe and secure way. There can be times when fragmentation can arise within the supply chain, which could be exploited. This risk needs reduction so that meat products enter the market with the desired attributes. Rapid Evaporative Ionisation Mass Spectrometry (REIMS) is a novel ambient mass spectrometry technique originally developed for rapid and accurate classification of biological tissue which is now being considered for use in a range of additional applications. It has subsequently shown promise for a range of food provenance, quality and safety applications with its ability to conduct ex vivo and in situ analysis. These are regarded as critical characteristics for technologies which can enable real-time decision making in meat processing plants and more broadly throughout the sector. This review presents an overview of the REIMS technology, and its application to the areas of provenance, quality and safety to the red meat industry, particularly in an Australian context.

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Section: What Is Reims?mentioning

confidence: 99%

Section: What Is Reims?mentioning

confidence: 99%

Section: Provenancementioning

confidence: 99%

Section: Qualitymentioning

confidence: 99%

See 2 more Smart Citations

Rapid Evaporative Ionization Mass Spectrometry: A Review on Its Application to the Red Meat Industry with an Australian Context

et al. 2021

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“…Thus, a REIMS data file is analysed as a ‘binned’ mass spectrum over a broad mass range that is treated as multivariate data. To gain biological meaning from REIMS data, statistical methods such as principal component analysis (PCA), linear discriminant analysis (LDA) and random forests are regularly employed 15 . Each of these approaches assesses the entire binned data file data to discover patterns and similarities among common samples and differences between unrelated samples.…”

Section: Introductionmentioning

confidence: 99%

Monitoring recombinant protein expression in bacteria by rapid evaporative ionisation mass spectrometry

Sarsby

McLean

Harman

et al. 2020

Rapid Comm Mass Spectrometry

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Rationale: There is increasing interest in methods of direct analysis mass spectrometry that bypass complex sample preparation steps. Methods: One of the most interesting new ionisation methods is rapid evaporative ionisation mass spectrometry (REIMS) in which samples are vapourised and the combustion products are subsequently ionised and analysed by mass spectrometry (Synapt G2si). The only sample preparation required is the recovery of a cell pellet from a culture that can be analysed immediately. Results: We demonstrate that REIMS can be used to monitor the expression of heterologous recombinant proteins in Escherichia coli. Clear segregation was achievable between bacteria harvesting plasmids that were strongly expressed and other cultures in which the plasmid did not result in the expression of large amounts of recombinant product. Conclusions: REIMS has considerable potential as a near-instantaneous monitoring tool for protein production in a biotechnology environment.

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An appraisal of data collection, analysis, and reporting adopted for water quality assessment: A case of Nigeria water quality research

Ewuzie

Aku

Nwankpa

2021

Heliyon

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The appropriate acquisition and processing of water quality data are crucial for water resource management. As such, published articles on water quality monitoring and assessment are meant to convey essential and reliable information to water quality experts, decision-makers, researchers, students, and the public. The implication is that such information must emanate from data obtained and analysed in an up-to-date, scientifically sound manner. Thus, inappropriate data analysis and reporting techniques could yield misleading results and mar the endeavours of achieving error-free conclusions. This study utilises the findings on water quality assessment in Nigeria over the last 20 years to reveal the likely trends in water quality research regarding data collection, data analysis, and reporting for physicochemical, bacteriological parameters, and trace organics. A total of 123 Web of Science and quartile ranked (Q1-Q4) published articles involving water quality assessment in Nigeria were analysed. Results indicated shortcomings in various aspects of data analysis and reporting. Consequently, we use simulated heatmaps and graphs to illustrate preferred ways of analysing, reporting, and visualising some regularly used descriptive and inferential statistics of water quality variables. Finally, we highlight alternative approaches to the customarily applied water quality assessment methods in Nigeria and emphasise other areas of deficiency that need attention for improved water quality research.

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

Cited by 6 publications

References 1 publication

Rapid Evaporative Ionization Mass Spectrometry: A Review on Its Application to the Red Meat Industry with an Australian Context

Rapid Evaporative Ionization Mass Spectrometry: A Review on Its Application to the Red Meat Industry with an Australian Context

Monitoring recombinant protein expression in bacteria by rapid evaporative ionisation mass spectrometry

An appraisal of data collection, analysis, and reporting adopted for water quality assessment: A case of Nigeria water quality research

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