## 3 Materials and Methods
### Data Treatment, Metabolite Annotation, and Molecular Networking  
GC-MS data were initially collected using the Agilent MSD ChemStation D.02.00.275 software (.D format) and converted via the Agilent GC MSD translator. Data were then transformed into mzML format using ProteoWizard's msConvert software (version 3.0) [63] and uploaded to MassIVE [[https://massive.ucsd.edu](https://massive.ucsd.edu)]([https://massive.ucsd.edu](https://massive.ucsd.edu)) (accessed on 26 February 2024). Subsequently, the MSHub deconvolution workflow [44] was applied to generate a spectrum file (.mgf) and a quantification table (.csv), which were directly used as input for GNPS's molecular-library search-GC workflow.  
Processed data were exported to the Agilent MassHunter Quantitative B.04.00 software for integration. The data were filtered for reproducibility by calculating the coefficient of variation (CV) of the signal intensities across samples. Molecular features with a CV > 30% were excluded. Data exhibiting consistent absence or presence across groups were retained. Autoscaling normalization and weight-based correction factors were applied for statistical analysis [64].  
An unsupervised principal component analysis (PCA) was conducted to verify analytical reproducibility and sample distribution. To identify statistically significant molecular features across ripening stages, both univariate (UVA) and multivariate (MVA) statistical analyses were performed using the MetaboAnalyst 6.0 server [65].  
Metabolites were annotated based on a range of criteria, including database searches, molecular ion analysis, isotopic distribution, retention time (RT), MS spectra, fragmentation pathways, and comparison with reference standards. The annotation confidence levelswere reported from 0 to 4 [66]. For the ID levels, 0 corresponds to an unambiguous 3D structure; 1 to a confident 2D structure or reference standard match; 2 to a likely structure or coincidence with the literature spectra; 3 to a possible structure, verified with molecular formula; and 4 to an unknown feature or a basic match with the libraries.  
The databases used included GNPS (Global Natural Products Social Molecular Networking, version 30; [[https://gnps.ucsd.edu](https://gnps.ucsd.edu)]([https://gnps.ucsd.edu](https://gnps.ucsd.edu)) (accessed on 24 December 2024) [29], NIST (National Institute of Standards and Technology, library 2.2 version 2014), and FlavorDB [[https://cosylab.iiitd.edu.in/flavordb/](https://cosylab.iiitd.edu.in/flavordb/)]([https://cosylab.iiitd.edu.in/flavordb/](https://cosylab.iiitd.edu.in/flavordb/)) (accessed on 27 July 2024) [45] to identify flavor-related molecules.  
A molecular network was constructed using GNPS's with the Library Search/Molecular Networking GC workflow. Nodes were linked if their spectrum similarity cosine score was <0.70 with at least six matched peaks. Each node's connections were limited to its top 10 most similar nodes. Families were restricted to a maximum of 100 nodes by removing the lowest-scoring edges. VOCs were annotated based on GNPS library matches with a balance score threshold of \(\geq\)65% to ensure high-confidence identifications [67].  
The resulting network file (.graphml) was visualized in Cytoscape (version 3.10.1) [68]. Low-confidence matches were manually removed from the network. Annotated compounds were classified using ClassyFire [69].