« Previous AbstractComparison of Four Extraction Techniques for the Evaluation of Volatile Compounds in Spray-Dried New Zealand Sheep Milk    Next Abstract"Herbivory by the insect diaphorina citri induces greater change in citrus plant volatile profile than does infection by the bacterium, Candidatus Liberibacter asiaticus" »

Food Chem

Title:		"Characterization of blue cheese volatiles using fingerprinting, self-organizing maps, and entropy-based feature selection"
Author(s):		High R; Eyres GT; Bremer P; Kebede B;
Address:		"Department of Food Science, University of Otago, PO Box 56, Dunedin 9054, New Zealand. Electronic address: ryan.high@postgrad.otago.ac.nz. Department of Food Science, University of Otago, PO Box 56, Dunedin 9054, New Zealand. Department of Food Science, University of Otago, PO Box 56, Dunedin 9054, New Zealand. Electronic address: biniam.kebede@otago.ac.nz"
Journal Title:		Food Chem
Year:		2021
Volume:		20201231
Issue:
Page Number:		128955 -
DOI:		10.1016/j.foodchem.2020.128955
ISSN/ISBN:		1873-7072 (Electronic) 0308-8146 (Linking)
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"
Keywords:		Cheese/*analysis Discriminant Analysis *Entropy Food Analysis/*methods Gas Chromatography-Mass Spectrometry Least-Squares Analysis Solid Phase Microextraction Volatile Organic Compounds/*analysis/*isolation & purification Artificial neural network Blue ch;
Notes:		"MedlineHigh, Ryan Eyres, Graham T Bremer, Phil Kebede, Biniam eng England 2021/01/25 Food Chem. 2021 Jun 15; 347:128955. doi: 10.1016/j.foodchem.2020.128955. Epub 2020 Dec 31"