Abstract：
:Understanding which volatile compounds discriminate between products can be useful for quality, innovation or product authenticity purposes. As dataset size and dimensionality increase, linear chemometric techniques like partial least squares discriminant analysis and variable identification (PLS-DA-VID) may not identify the most discriminant compounds. This research compared the performance of self-organizing maps and entropy-based feature selection (SOM-EFS) and PLS-DA-VID to identify discriminant compounds in 17 blue cheese varieties. A total of 172 volatiles were detected using headspace solid phase microextraction, gas chromatography and mass spectrometry, including 1-nonene and 2,6-dimethylpyridine, which were newly identified in blue cheese. Despite SOM-EFS selecting only 14 volatiles compared to 78 for PLS-DA-VID, SOM-EFS proved more effectively discriminant and improved the median five-fold cross-validated prediction accuracy of the model to 0.94 compared to 0.82 for PLS-DA-VID. These findings introduce SOM-EFS as a powerful non-linear exploratory data analysis approach in the field of volatile analytical chemistry.
journal_name
Food Chemjournal_title
Food chemistryauthors
High R,Eyres GT,Bremer P,Kebede Bdoi
10.1016/j.foodchem.2020.128955subject
Has Abstractpub_date
2020-12-31 00:00:00pages
128955eissn
0308-8146issn
1873-7072pii
S0308-8146(20)32838-7journal_volume
347pub_type
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