Molecular Characterization of Cooking Processes: A Metabolomics Decoding of Vaporous Emissions for Food Markers and Thermal Reaction Indicators.

Thermal processing of food plays a fundamental role in everyday life. Whereas most researchers study thermal processes directly in the matrix, molecular information in the form of non- and semivolatile compounds conveyed by vaporous emissions is often neglected. We performed a metabolomics study of processing emissions from 96 different food items to define the interaction between the processed matrix and released metabolites. Untargeted profiling of vapor samples revealed matrix-dependent molecular spaces that were characterized by Fourier-transform ion cyclotron resonance-mass spectrometry and ultra-performance liquid chromatography-mass spectrometry. Thermal degradation products of peptides and amino acids can be used for the differentiation of animal-based food from plant-based food, which generally is characterized by secondary plant metabolites or carbohydrates. Further, heat-sensitive processing indicators were characterized and discussed in the background of the Maillard reaction. These reveal that processing emissions contain a dense layer of information suitable for deep insights into food composition and control of cooking processes based on processing emissions.

C2-addition patterns emerging from acetylene and nickel sulfide in simulated prebiotic hydrothermal conditions.

Chemical complexity is vital not only for the origin of life but also for biological evolution. The chemical evolution of a complex prebiotic mixture containing acetylene, carbon monoxide (CO), and nickel sulfide (NiS) has been analyzed with mass spectrometry as an untargeted approach to reaction monitoring. Here we show through isotopic 13C-labelling, multiple reaction products, encompassing diverse CHO and CHOS compounds within the complex reaction mixture. Molecules within the same chemical spaces displayed varying degrees of 13C-labelling, enabling more robust functional group characterization based on targeted investigations and differences in saturation levels among the described classes. A characteristic C2-addition pattern was detected in all compound classes in conjunction with a high diversity of thio acids, reminiscent of extant microbial C2-metabolism. The analysis involved a time-resolved molecular network, which unveiled the behavior of sulfur in the system. At the onset of the reaction, early formed compounds contain more sulfur atoms compared to later emerging compounds. These results give an essential insight into the still elusive role of sulfur dynamics in the origin of life. Moreover, our results provide temporally resolved evidence of the progressively increasing molecular complexity arising from a limited number of compounds.

DBDIpy: a Python library for processing of untargeted datasets from real-time plasma ionization mass spectrometry.

MOTIVATION: Plasma ionization is rapidly gaining popularity for mass spectrometry (MS)-based studies of volatiles and aerosols. However, data from plasma ionization are delicate to interpret as competing ionization pathways in the plasma create numerous ion species. There is no tool for detection of adducts and in-source fragments from plasma ionization data yet, which makes data evaluation ambiguous. SUMMARY: We developed DBDIpy, a Python library for processing and formal analysis of untargeted, time-sensitive plasma ionization MS datasets. Its core functionality lies in the identification of in-source fragments and identification of rivaling ionization pathways of the same analytes in time-sensitive datasets. It further contains elementary functions for processing of untargeted metabolomics data and interfaces to an established ecosystem for analysis of MS data in Python. AVAILABILITY AND IMPLEMENTATION: DBDIpy is implemented in Python (Version ≥ 3.7) and can be downloaded from PyPI the Python package repository (https://pypi.org/project/DBDIpy) or from GitHub (https://github.com/leopold-weidner/DBDIpy). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Real-Time Monitoring of Miniaturized Thermal Food Processing by Advanced Mass Spectrometric Techniques.

Mass spectrometry is a popular and powerful analytical tool to study the effects of food processing. Industrial sampling, real-life sampling, or challenging academic research on process-related volatile and aerosol research often demand flexible, time-sensitive data acquisition by state-of-the-art mass analyzers. Here, we show a laboratory-scaled, miniaturized, and highly controllable setup for the online monitoring of aerosols and volatiles from thermal food processing based on dielectric barrier discharge ionization (DBDI) mass spectrometry (MS). We demonstrate the opportunities offered by the setup from a foodomics perspective to study emissions from the thermal processing of wheat bread rolls at 210 °C by Fourier transformation ion cyclotron resonance MS. As DBDI is an emerging technology, we compared its ionization selectivity to established atmospheric pressure ionization tools: we found DBDI preferably ionizes saturated, nitrogenous compounds. We likewise identified a sustainable overlap in the selectivity of detected analytes with APCI and electrospray ionization (ESI). Further, we dynamically recorded chemical fingerprints throughout the thermal process. Unsupervised classification of temporal response patterns was used to describe the dynamic nature of the reaction system. Compared to established tools for real-time MS, our setup permits one to monitor chemical changes during thermal food processing at ultrahigh resolution, establishing an advanced perspective for real-time mass spectrometric analysis of food processing.

Elucidation of the non-volatile fingerprint in oven headspace vapor from bread roll baking by ultra-high resolution mass spectrometry.

Untargeted research on vapor arising during the thermal processing of food has so far focused on volatile aroma compounds. In this study, we present an oven atmosphere sampling strategy to trap headspace aerosols along with semi- and non-volatile molecules liberated during the baking of wheat bread rolls. The collected vapor condensate was analyzed for its molecular fingerprinting using direct infusion ultra-high resolution mass spectrometry. We detected up to 4,700 molecular species in a vapor sample from bread rolls baked at 230 °C for 15 min. Beyond the global profiling of the underlying matrix, our method can follow complex reaction cascades during the baking process, such as the formation of advanced glycation end-products like maltosine through the interface of trapped vapor. Further, process parameters such as baking temperature and duration were used to model the dynamic liberation of molecules to the oven atmosphere by a response surface methodology approach.