Next Article in Journal
Development of a Sensory Flavor Lexicon for Mushrooms and Subsequent Characterization of Fresh and Dried Mushrooms
Next Article in Special Issue
Dairy by-Products Concentrated by Ultrafiltration Used as Ingredients in the Production of Reduced Fat Washed Curd Cheese
Previous Article in Journal
Pyropheophytin a in Soft Deodorized Olive Oils
Previous Article in Special Issue
Impact of Extending Hard-Cheese Ripening: A Multiparameter Characterization of Parmigiano Reggiano Cheese Ripened up to 50 Months
Article

Application of the UHPLC-DIA-HRMS Method for Determination of Cheese Peptides

1
Department of Chemistry and Biotechnology, School of Science, Tallinn University of Technology, Ehitajate tee 5, 12616 Tallinn, Estonia
2
Center of Food and Fermentation Technologies, Akadeemia tee 15A, 12618 Tallinn, Estonia
3
Institute of Oncology, Riga Stradins University, 13 Pilsonu Str., LV-1002 Riga, Latvia
4
Transplantation Laboratory, Haartman Institute, University of Helsinki, FI-00014 Helsinki, Finland
5
HUSLAB, Helsinki University Hospital, FI-00029 Helsinki, Finland
*
Author to whom correspondence should be addressed.
Foods 2020, 9(8), 979; https://doi.org/10.3390/foods9080979
Received: 30 May 2020 / Revised: 22 July 2020 / Accepted: 22 July 2020 / Published: 23 July 2020
(This article belongs to the Special Issue Cheese and Whey)
Until now, cheese peptidomics approaches have been criticised for their lower throughput. Namely, analytical gradients that are most commonly used for mass spectrometric detection are usually over 60 or even 120 min. We developed a cheese peptide mapping method using nano ultra-high-performance chromatography data-independent acquisition high-resolution mass spectrometry (nanoUHPLC-DIA-HRMS) with a chromatographic gradient of 40 min. The 40 min gradient did not show any sign of compromise in milk protein coverage compared to 60 and 120 min methods, providing the next step towards achieving higher-throughput analysis. Top 150 most abundant peptides passing selection criteria across all samples were cross-referenced with work from other publications and a good correlation between the results was found. To achieve even faster sample turnaround enhanced DIA methods should be considered for future peptidomics applications. View Full-Text
Keywords: dairy product analysis; cheese peptidomics; cheesemaking; data-independent acquisition dairy product analysis; cheese peptidomics; cheesemaking; data-independent acquisition
Show Figures

Figure 1

MDPI and ACS Style

Arju, G.; Taivosalo, A.; Pismennoi, D.; Lints, T.; Vilu, R.; Daneberga, Z.; Vorslova, S.; Renkonen, R.; Joenvaara, S. Application of the UHPLC-DIA-HRMS Method for Determination of Cheese Peptides. Foods 2020, 9, 979. https://doi.org/10.3390/foods9080979

AMA Style

Arju G, Taivosalo A, Pismennoi D, Lints T, Vilu R, Daneberga Z, Vorslova S, Renkonen R, Joenvaara S. Application of the UHPLC-DIA-HRMS Method for Determination of Cheese Peptides. Foods. 2020; 9(8):979. https://doi.org/10.3390/foods9080979

Chicago/Turabian Style

Arju, Georg, Anastassia Taivosalo, Dmitri Pismennoi, Taivo Lints, Raivo Vilu, Zanda Daneberga, Svetlana Vorslova, Risto Renkonen, and Sakari Joenvaara. 2020. "Application of the UHPLC-DIA-HRMS Method for Determination of Cheese Peptides" Foods 9, no. 8: 979. https://doi.org/10.3390/foods9080979

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop