Indirect evaluation of watermelon volatile profile: Detection of subtle changes with e-nose systems

• Published in: Food science & technology 2024-07, Vol.203, p.116337, Article 116337
• Main Authors: Fredes, Alejandro, Cebolla-Cornejo, Jaime, Beltrán, Joaquín, Gisbert, Carmina, Picó, Belén, Valcárcel, Mercedes, Roselló, Salvador
• Format: Article
• Language: English
• Subjects: Aroma; Breeding; Citrullus lanatus; Grafting; Volatiles
• ISSN: 0023-6438
• DOI: 10.1016/j.lwt.2024.116337

The effectiveness of e-nose systems as high-throughput tools for volatile profiling in watermelon was investigated focusing on discerning subtle changes induced by the use of different rootstocks. Partial Least Square Discriminant Analysis (PLS-DA) models, both GC-MS and e-nose data, demonstrated moderate performance in classification due to nuanced differences among groups (the same F1 hybrid was used as scion). However, PLS-DA biplots revealed a clear correlation between GC-MS and e-nose data. This methodology enabled the e-nose system to identify the effects of specific root-scion combinations compared to non-grafted controls and detect combinations with more variable volatile profiles. Remarkably, the e-nose system identified samples with anomalous volatile profiles, mirroring the capabilities of GC-MS data. Additionally, PLS models were developed to provide reasonably accurate predictions of key compound contents like geranylacetone, (Z)-6-nonen-1-ol, or (Z)-6-nonenal, crucial for watermelon flavor and taste perception. Overall, this study highlights the potential of e-nose systems in discerning nuanced variations in watermelon volatile profiles affecting aroma. Incorporating volatile profile evaluation capabilities using such systems will significantly optimize quality control processes and plant breeding programs. •The PLS-DA of E-nose data enables high throughput evaluation of volatile profile.•E-nose differentiates the effect of scion-rootstock combinations on volatile profile.•PLS-DA analysis of E-nose data correlated with the results obtained via GC-MS.•Good prediction models developed for indirect quantification of prominent volatiles.