All Ion Fragmentation Analysis Enhances the Untargeted Profiling of Glucosinolates in Brassica Microgreens by Liquid Chromatography and High-Resolution Mass Spectrometry

Abstract

An analytical approach based on reversed-phase liquid chromatography coupled to electrospray ionization Fouriertransform mass spectrometry in negative ion mode (RPLC-ESI-(−)-FTMS) was developed for the untargeted characterization of glucosinolates (GSL) in the polar extracts of four Brassica microgreen crops, namely, garden cress, rapeseed, kale, and broccoli raab. Specifically, the all ion f ragmentation (AIF) operation mode enabled by a quadrupole-Orbitrap mass spectrometer, i.e., the systematic fragmentation of all ions generated in the electrospray source, followed by the acquisition of an FTMS spectrum, was exploited. First, the best qualifying product ions for GSL were recognized from higher-energy collisional dissociation (HCD)-FTMS 2 spectra of representative standard GSL. Extracted ion chromatograms (EIC) were subsequently obtained for those ions from RPLC-ESI(−)-AIF-FTMS data referred to microgreen extracts, by plotting the intensity of their signals as a function of retention time. The alignment of peaks detected in the EIC traces was finally exploited for the recognition of peaks potentially related to GSL, with the EIC obtained for the sulfate radical anion [SO 4 ] •− (exact m/z 95.9523) providing the highest selectivity. Each putative GSL was subsequently characterized by HCD-FTMS 2 analyses and by collisionally induced dissociation (CID) multistage MS n (n = 2, 3) acquisitions based on a linear ion trap mass spectrometer. As a result, up to 27 different GSLs were identified in the four Brassica microgreens. The general method described in this work appears as a promising approach for the study of GSL, known and novel, in plant extracts.

Prospects of microgreens as budding living functional food: Breeding and biofortification through OMICS and other approaches for nutritional security

“…Proteomics and metabolomics have been explored in several microgreens viz; broccoli ( Sun et al, 2015 ), brassica ( Castellaneta et al, 2022 ) and other leafy vegetables also ( Sahoo et al, 2022 ). Novel datasets for microgreens may be generated through research activities, and prospects of encroachment in OMICS approaches.…”

“…Candidate genes have been identified and incorporated in desirable plant through Genomics (QTL mapping), transcriptomics and transgenic approaches for microgreens related traits (shelf life and nutrient content: Fe, Zn) in different plant systems viz cabbage ( Wu et al, 2008 ), broccoli ( Gardner et al, 2016 ), wheat ( Krishnappa et al, 2022 ), lettuce ( Hayashi et al, 2012 ), melon ( Dai et al, 2022 ) and chickpea ( Mahto et al, 2022 ). Metabolomics and proteomics have been explored in several microgreens viz., broccoli ( Sun et al, 2015 ), brassica ( Castellaneta et al, 2022 ) and other leafy vegetables also ( Sahoo et al, 2022 ). Thus, it is evident that we may identify and predict candidate genes associated with microgreens related traits (shelf life, desirable nutrients content, developmental rate and phytochemicals) may be incorporated in targeted crop varieties of fruits and vegetables utilizing the crop specific data bases of genomics, transcriptomics and metabolomics, marker-assisted selection, GWAS, bioinformatics, AI approaches utilizing the databases of specific crop transgenic CRISPR/Cas9 and gene editing approaches ( Mathiazhagan et al, 2021 ; Valdes et al, 2021 ; Chakravorty et al, 2022 ; Parmar et al, 2022 ; Sahoo et al, 2022 ) .…”

Broccoli, Amaranth, and Red Beet Microgreen Juices: The Influence of Cold-Pressing on the Phytochemical Composition and the Antioxidant and Sensory Properties

“…Previous studies have shown that broccoli, amaranth and red beet microgreens are high in bioactive compounds such as vitamins, glucosinolates, isothiocyanates, phenolic compounds and betalains and have good antioxidant properties [ 4 , 8 ]. However, there are few studies that provide insight into the biocompound profiles of these microgreens and their correlation with antioxidant properties [ 9 , 10 , 11 , 12 , 13 , 14 ]. In addition, microgreens can be successfully used for healthy beverage production [ 15 , 16 , 17 , 18 ] or incorporated into various bakery/confectionary products [ 19 ].…”

Toward Machine Learning-Driven Mass Spectrometric Identification of Trichothecenes in the Absence of Standard Reference Materials

“…Several alternative but complementary strategies have been developed to identify “unknown unknown” toxins using untargeted approaches. The retrospective analysis of high-resolution mass spectrometry (HRMS) data has been used most frequently. , Another nontargeted strategy has involved data-independent MS/MS analysis for screening and quantitation of secondary metabolites in green tea and cruciferous vegetables …”

“…9 nontargeted strategy has involved data-independent MS/MS analysis for screening and quantitation of secondary metabolites in green tea 11 and cruciferous vegetables. 12 A final example is the use of precursor ion scans (PIS) on triple quadrupole mass spectrometers (QQQ). Fragment ions common to compounds sharing structural features are scanned for in Q3 of the mass spectrometer, then Q1 is interrogated for the precursor ions producing those fragments.…”