Python workflow for the selection and identification of marker peptides-proof-of-principle study with heated milk.

The analysis of almost holistic food profiles has developed considerably over the last years. This has also led to larger amounts of data and the ability to obtain more information about health-beneficial and adverse constituents in food than ever before. Especially in the field of proteomics, software is used for evaluation, and these do not provide specific approaches for unique monitoring questions. An additional and more comprehensive way of evaluation can be done with the programming language Python. It offers broad possibilities by a large ecosystem for mass spectrometric data analysis, but needs to be tailored for specific sets of features, the research questions behind. It also offers the applicability of various machine-learning approaches. The aim of the present study was to develop an algorithm for selecting and identifying potential marker peptides from mass spectrometric data. The workflow is divided into three steps: (I) feature engineering, (II) chemometric data analysis, and (III) feature identification. The first step is the transformation of the mass spectrometric data into a structure, which enables the application of existing data analysis packages in Python. The second step is the data analysis for selecting single features. These features are further processed in the third step, which is the feature identification. The data used exemplarily in this proof-of-principle approach was from a study on the influence of a heat treatment on the milk proteome/peptidome.

Impacts of Fungicide Treatment and Conventional Fertilization Management on the Potato Metabolome (Solanum tuberosum L.) Evaluated with UPLC-IMS-QToF.

Two field trials were conducted to investigate the influence of fungicide and fertilization management on the potato tubers' metabolome (Solanum tuberosum L.). Thereby, fungicides and conventional fertilizers were varied in terms of quantities, number and date of applications, physical state, and product composition. Following a water-methanol-based extraction, samples were analyzed using an UPLC-IMS-QToF and multivariate data analysis. Fungicide application led to significant changes in the tubers' metabolome. Flavonoids were increasingly expressed as a natural response to impending fungal or viral infections in an untreated group, while the phytoalexin rishitinol was highly abundant in groups with fungicide application. In contrast to fungicides, the application of conventional fertilizers did not cause significant alterations in the tubers' compound composition. Consequently, the impact of fungicide application could be rated as more important than the fertilization-derived influence, which might be because of a gentler adaption to fertilization than to the acute stress of fungicide applications.

Metabolomics-Based Approach for the Discrimination of Potato Varieties ( Solanum tuberosum) using UPLC-IMS-QToF.

One hundred eighty-two authentic potato samples ( Solanum tuberosum) of known variety were collected from various German regions in 2016 and 2017. Samples were extracted with a liquid-liquid-extraction protocol that included isopropanol, methanol, and water in order to focus on lipophilic metabolites. The analysis of nonpolar extracts was performed using an UPLC-IMS-QToF-MS system; data sets obtained were evaluated via multivariate data analysis. A selection of 14 key metabolites with a significant difference in their abundance profiles was identified. This set of markers contained four hydroxylated glucocerebrosides, two phosphoinositols, one phosphocholine, and seven acylated sterol glucosides based on stigmasterol and ß-sitosterol, which primarily enable the varietal discrimination. Fragments and neutral losses commonly appearing within one class or subclass of lipids were summarized within a new database that included ion mobility data. The performance of the approach was verified with twenty-nine commercial potato samples.

Benefits of high resolution IC-ICP-MS for the routine analysis of inorganic and organic arsenic species in food products of marine and terrestrial origin.

A recently developed and validated method for simultaneous determination of 17 inorganic and organic arsenic compounds in marine biota has been successfully applied to routine analysis of different food products, including fish, shellfish, edible algae, rice, and other types of grain. During one year, approximately 250 food samples were analyzed, mostly fish and rice. Long-term stability and robustness of the system was observed and reproducible results for certified reference materials were ensured by means of control charts. The separation was performed by ion-pair chromatography on an anion-exchange column to separate anionic, neutral, and cationic arsenic species in one chromatographic run. Hyphenation to ICP-MS allowed element-specific and sensitive detection of the different arsenic species with a detection limit as low as 8 ng As L(-1 )in the sample extract, which is equivalent to 2 ng As g(-1) in the original sample. Special emphasis was laid on the analysis of marine algae and rice samples. These food types can contain elevated levels of the very toxic inorganic arsenic species (up to 90% in rice) and therefore are the focus of interest in the food industry. In marine algae, inorganic arsenic was mainly present as arsenate whereas in rice arsenite predominated.