Experimental and Theoretical Studies on Gas-Phase Fragmentation Reactions of Protonated Methyl Benzoate: Concomitant Neutral Eliminations of Benzene, Carbon Dioxide, and Methanol

Low Energy Collision-Induced Dissociation of Azepine Pictet-Spengler Adducts of Nω-Methylserotonin

Cimitrypazepines are a class of natural products produced by Pictet-Spengler condensation of N_ω-methylserotonin and aldehydes in a manner that produces a seven-membered azepine ring. In this study, the fragmentation behavior of this class of molecules under low-energy CID was investigated in detail. Proposed mechanisms of fragmentation were supported by deuterium labeling and DFT calculations. Loss of methylamine and methylenimine were dominant fragmentation pathways of analogs containing an aliphatic side chain. Loss of methylenimine was found to proceed via unusual methyl cation transfer. Fragmentation of analogs containing an aromatic side chain was strongly influenced by the nature of the substituents and proceeded via a novel retro-Pictet-Spengler pathway and involvement of ion-neutral complexes. In some cases, a gas-phase interconversion between the azepine and β-carboline ring was observed during fragmentation. Detailed analysis of fragmentation behavior provided in this study will serve as a valuable guide for the discovery of new analogs from natural sources.

CID Fragmentation of Deprotonated N-Acyl Aromatic Sulfonamides. Smiles-Type and Nitrogen-Oxygen Rearrangements

The NIST tandem mass spectral library (2020 version) includes over 800 aromatic sulfonamides. In negative mode, upon collisional activation most benzenesulfonamides lose a neutral SO₂ molecule leading to an anilide anion (C₆H₅NH^-, m/z 92). However, for deprotonated N-benzoyl aromatic sulfonamides, the phenoxide ion (C₆H₅O^-, m/z 93.0343) is the principal product ion. A variety of N-acylbenzenesulfonamide derivatives were also found to overwhelmingly produce the phenoxide ion as the most intense product ion. A mechanism is proposed in which, at low energy, a carbonyl oxygen atom (C═O) is transferred to a benzene ring, known as a Smiles-type rearrangement (the amide oxygen atom attacks the arylsulfonyl group at the ipso position), in parallel and determining the reaction at high energy a nitrogen-oxygen rearrangement mechanism leads to the formation of the phenoxide ion. Tandem mass spectra of deprotonated N-benzoyl-¹⁸O-benzenesulfonamide and N-thiobenzoyl-p-toluenesulfonamide confirmed the rearrangement since base peaks at m/z 95.0384 and 123.0270 which correspond to an ¹⁸O phenoxide ion ([C₆H₅¹⁸O]^-) and a 4-methylbenzenethiolate anion ([CH₃C₆H₄S]^-) were observed, respectively. The parallel mechanism is supported by the strong correlation between the observed product ion intensities and the corresponding activation energies obtained by Density Functional Theory calculations. This is an example of a relatively simple ion with a complex path to fragmentation, being a cautionary tale for indiscriminate use of in silico spectra in place of actual measurement.

Volatile compounds profiling by using proton transfer reaction-time of flight-mass spectrometry (PTR-ToF-MS). The case study of dark chocolates organoleptic differences

Direct-injection mass spectrometry (DIMS) techniques have evolved into powerful methods to analyse volatile organic compounds (VOCs) without the need of chromatographic separation. Combined to chemometrics, they have been used in many domains to solve sample categorization issues based on volatilome determination. In this paper, different DIMS methods that have largely outperformed conventional electronic noses (e-noses) in classification tasks are briefly reviewed, with an emphasis on food-related applications. A particular attention is paid to proton transfer reaction mass spectrometry (PTR-MS), and many results obtained using the powerful PTR-time of flight-MS (PTR-ToF-MS) instrument are reviewed. Data analysis and feature selection issues are also summarized and discussed. As a case study, a challenging problem of classification of dark chocolates that has been previously assessed by sensory evaluation in four distinct categories is presented. The VOC profiles of a set of 206 chocolate samples classified in the four sensory categories were analysed by PTR-ToF-MS. A supervised multivariate data analysis based on partial least squares regression-discriminant analysis allowed the construction of a classification model that showed excellent prediction capability: 97% of a test set of 62 samples were correctly predicted in the sensory categories. Tentative identification of ions aided characterisation of chocolate classes. Variable selection using dedicated methods pinpointed some volatile compounds important for the discrimination of the chocolates. Among them, the CovSel method was used for the first time on PTR-MS data resulting in a selection of 10 features that allowed a good prediction to be achieved. Finally, challenges and future needs in the field are discussed.