Indirect evaluation of watermelon volatile profile: Detection of subtle changes with e-nose systems
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Título
Indirect evaluation of watermelon volatile profile: Detection of subtle changes with e-nose systemsAutoría
Fecha de publicación
2024Editor
ElsevierISSN
0023-6438Cita bibliográfica
FREDES, Alejandro, et al. Indirect evaluation of watermelon volatile profile: Detection of subtle changes with e-nose systems. LWT, 2024, 203: 116337.Tipo de documento
info:eu-repo/semantics/articleVersión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
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 ... [+]
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. [-]
Publicado en
LWT - Food Science and Technology, , 2024, 203: 116337Entidad financiadora
Ministerio de Ciencia, Innovación y Universidades | Generalitat Valenciana
Código del proyecto o subvención
MICIU/ICTI2017-2020/AGL2014-53398-C2-2-R | MICIU/ICTI2017-2020/AGL2017-85563-C2-1-R-AR | PID2020-116055RB-C21 | PROMETEO/2021/072 | Santiago Grisolía/2013/032
Derechos de acceso
info:eu-repo/semantics/openAccess
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