Metabolomics approach to studying minimally processed peach (Prunus persica) fruit

Fresh-cut fruit products for both retail and food service applications have increasingly appeared in the market place in recent years. Among different fruit types, peaches can be used and are highly appreciated as fresh-cut product although their commercial success is limited due to their short shelf-life and the quick onset of wounding-related physiological reactions. In this work we analyzed the technological and physiological changes induced by fresh-cut preparation in three different types of peach cultivars: 'Fayette' (melting), 'Wilhelmina' (non-melting) and 'Ghiaccio3' (stony hard). We performed a metabolite targeting profiling to focus on the changes in organic acid levels, important components of fruit taste and organoleptic quality of peaches. Interestingly, 'Ghiaccio3' showed an increase of several organic acids after cutting while 'Fayette' and 'Wilhelmina' showed unchanged amounts or a general reduction. Cutting induces a similar pattern of change in important metabolites (i.e., dehydroascorbate, alanine) in all the three peach types while other metabolites (i.e., citric acid) appeared to be differentially regulated in the considered peach cultivars

Auto-deconvolution and molecular networking of gas chromatography–mass spectrometry data

We engineered a machine learning approach, MSHub, to enable auto-deconvolution of gas chromatography–mass spectrometry (GC–MS) data. We then designed workflows to enable the community to store, process, share, annotate, compare and perform molecular networking of GC–MS data within the Global Natural Product Social (GNPS) Molecular Networking analysis platform. MSHub/GNPS performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization and quantifies the reproducibility of fragmentation patterns across samples. © 2020, The Author(s), under exclusive licence to Springer Nature America, Inc