Can I read Volatilomic Profiling of Citrus Juices by Dual-Detection HS-GC-MS-IMS and Machine Learning—An Alternative Authentication Approach on EtoBox?

Volatilomic Profiling of Citrus Juices by Dual-Detection HS-GC-MS-IMS and Machine Learning—An Alternative Authentication Approach by Rebecca Brendel; Sebastian Schwolow; Sascha Rohn; Philipp Weller is a Agricultural and Biological Sciences article available to read on EtoBox.

What is Volatilomic Profiling of Citrus Juices by Dual-Detection HS-GC-MS-IMS and Machine Learning—An Alternative Authentication Approach about?

A prototype dual-detection headspace-gas chromatography-mass spectrometry-ion mobility spectrometry (HS-GC-MS-IMS) system was used for the analysis of the volatile profile of 47 Citrus juices including grapefruit, blood orange, and common sweet orange juices without requiring any sample pretreatment. Next to reduced measurement times, substance identification could be improved substantially in case of co-elution by considering the characteristic drift times and m/z ratios obtained by IMS and MS. To discriminate the volatile profiles of the different juice types, extensive data analysis was performed with both datasets, respectively. By principal component analysis (PCA), a distinct separation between grapefruit and orange juices was observed. While in the IMS data grapefruit juices not from fruit juice concentrate could be separated from grapefruit juices reconstituted from fruit juice concentrate, in the MS data, the blood orange juices could be differentiated from the orange juices. This observation leads to the assumption that the IMS and MS data contain different information about the composition of the volatile profile. Subsequently, linear discriminant analysis (LDA), support

Who reads Volatilomic Profiling of Citrus Juices by Dual-Detection HS-GC-MS-IMS and Machine Learning—An Alternative Authentication Approach?

Author

Rebecca Brendel; Sebastian Schwolow; Sascha Rohn; Philipp Weller

Publisher

American Chemical Society (ACS)

Published

2021

Language

EN

Field

Agricultural and Biological Sciences (Life Sciences)