首页|Lacombe Research and Development Centre Reports Findings in Machine Learning (Us ing machine-learning approaches to investigate the volatile-compound fingerprint of fishy off-flavour from beef with enhanced healthful fatty acids)
Lacombe Research and Development Centre Reports Findings in Machine Learning (Us ing machine-learning approaches to investigate the volatile-compound fingerprint of fishy off-flavour from beef with enhanced healthful fatty acids)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting originating from Lacombe, Can ada, by NewsRx correspondents, research stated, “Machine learning classification approaches were used to discriminate a fishy off-flavour identified in beef wit h health-enhanced fatty acid profiles. The random forest approach outperformed ( P <0.001; receiver operating characteristic curve: 99.8 % , sensitivity: 99.9 % and specificity: 93.7 %) the lo gistic regression, partial least-squares discrimination analysis and the support vector machine (linear and radial) approaches, correctly classifying 100 % and 82 % of the fishy and non-fishy meat samples, respectively.” Our news editors obtained a quote from the research from Lacombe Research and De velopment Centre, “The random forest algorithm identified 20 volatile compounds responsible for the discrimination of fishy from non-fishy meat samples. Among t hose, seven volatile compounds (pentadecane, octadecane, gdodecalactone, dodeca nal, (E,E)-2,4-heptadienal, 2-heptanone, and ethylbenzene) were selected as sign ificant contributors to the fishy off-flavour fingerprint, all being related to lipid oxidation.”
LacombeCanadaNorth and Central Ameri caCyborgsEmerging TechnologiesMachine Learning
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