English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Over the past years, the application of electronic nose devices has been investigated as a potential tool forassessing food freshness. This relies on the application of various pattern recognition methods to provideaccurate classification and regression models. The models' accuracy depends on the number of samplesused during the training process. This often leads to unstable and unreliable classifiers in the case offood quality assessment, where the number of samples is typically less than 200 for a given experiment.The aim of this work is to tackle this problem through the development of a series of ensemble-basedclassifiers and regression models using support vector machines and electronic nose datasets based onthe previously published work of this group. It was found that the developed ensemble provides a higherprediction accuracy compared to the single model approach when estimating the freshness scoreassigned by the sensory panel; achieving an overall accuracy of 84.1% compared to 72.7% in the case ofthe single classifier model. Another set of calibration ensembles were developed based on SVMregression,in order to predict bacterial species counts, achieving an increase in the average overallperformance of 85.0%, compared to 76.5% when a single classifier was applied. This increase in thepredictive power therefore suggests that combining an electronic nose with ensemble-based systems can be used as an innovative method to assess the freshness of beef fillets

Ensemble-based support vector machine classifiers as an efficient tool for quality assessment of beef fillets from electronic nose data