Disentangling shared and unique variation in multiplatform hazelnut volatilomics using JIVE

In food science, volatile metabolites play a crucial role in determining sensory quality, acceptability and traceability. Fully characterizing the volatilome often requires combining multiple analytical techniques. However, reliably integrating the outcomes of these independent analyses to identify shared and unique information remains a significant challenge. In this paper, we illustrate how the multivariate Joint and Individual Variation Explained (JIVE) approach could be used to face this problem on a multiplatform VOC dataset obtained characterizing the volatilome of hazelnut pastes with GC-MS, PTR-ToF-MS and GC-IMS. While standardized data processing strategies were applied for GC-MS and PTR-ToF-MS, an automated pipeline was developed for GC-IMS to extract untargeted peak tables. The samples, representing three geographical origins, were collected during roasting to capture a wide range of intensities, offering a challenging case study for the proposed approach. The results showed...

In food science, volatile metabolites play a crucial role in determining sensory quality, acceptability and traceability. Fully characterizing the volatilome often requires combining multiple analytical techniques. However, reliably integrating the outcomes of these independent analyses to identify shared and unique information remains a significant challenge. In this paper, we illustrate how the multivariate Joint and Individual Variation Explained (JIVE) approach could be used to face this problem on a multiplatform VOC dataset obtained characterizing the volatilome of hazelnut pastes with GC-MS, PTR-ToF-MS and GC-IMS. While standardized data processing strategies were applied for GC-MS and PTR-ToF-MS, an automated pipeline was developed for GC-IMS to extract untargeted peak tables. The samples, representing three geographical origins, were collected during roasting to capture a wide range of intensities, offering a challenging case study for the proposed approach. The results showed that JIVE effectively separated the variability of each dataset into joint and individual components. A high-level comparison of the three analytical methods, based on variation decomposition and variable distribution, confirmed their complementarity. Additionally, identifying latent variables facilitated the visualization of analytical patterns - both shared and platform-specific - and the selection of related key variable trends, supporting the chemical interpretation of the results. This unsupervised data exploration strategy, based on JIVE, provides clearer interpretation of both shared and technique-specific insights. It supports an objective evaluation of the potential of a multiplatform analysis while offering guidance for selecting the most suitable analytical method in studies constrained to a single technique.