Data for the application of a robust MALDI mass spectrometry approach for bee pollen investigation

Braglia, Chiara ; Alberoni, Daniele ; Di Gioia, Diana ; Giacomelli, Alessandra ; Bocquet, Michel ; Bulet, Philippe (2024) Data for the application of a robust MALDI mass spectrometry approach for bee pollen investigation. University of Bologna. DOI 10.6092/unibo/amsacta/7717. [Dataset]
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Abstract

The dataset contains results on honeybee pollen extracted and analyzed with MALDI MS. Our experiment involved honeybee pollen grains from five different Italian regions (Campania, Sardinia, Sicily, South Tirol and Tuscany) through the spring 2023. Pollen sample were collected from the honeybee colonies with standard pollen traps every two weeks for four months. The generated dataset concern different pollen extraction methods. In order to define the best experimental conditions to record robust and the most representative and distinguishable spectra for a pollen species, different conditions of extraction were tested, four different solutions: (a) 2M acetic acid 2M (2M AA) and 50% acetonitrile (50% ACN); (b) AA 2M; (c) 2% ACN and 0.1% trifluoroacetic acid (0.1% TFA); and (d) a solution of 1% TFA) and two mechanical extraction methods (stirring and ultrasonication). Moreover, 10-fold serial dilutions of the crude extracted material from the different bee pollen balls were evaluated (10, 100 and 1,000 times) to determine the most appropriate sample to matrix ratio.

Abstract
Document type
Dataset
Creators
CreatorsAffiliationORCID
Braglia, ChiaraUniversity of Bolognahttps://orcid.org/0000-0002-1637-896X
Alberoni, DanieleUniversity of Bolognahttps://orcid.org/0000-0002-2394-2880
Di Gioia, DianaUniversity of Bolognahttps://orcid.org/0000-0002-0181-1572
Giacomelli, AlessandraUNA-APIhttps://orcid.org/0000-0002-6012-2937
Bocquet, MichelApimediahttps://orcid.org/0000-0002-1584-1481
Bulet, PhilippeUniversity Grenoble Alpeshttps://orcid.org/0000-0001-9016-265X
Keywords
mass spectrometry, molecular mass fingerprint, trifluoroacetic acid, acetonitrile, machine learning model, plant biodiversity.
Subjects
DOI
Contributors
Name
Affiliation
ORCID
Type
Braglia, Chiara
University of Bologna
Researcher
Alberoni, Daniele
University of Bologna
Contact person
Di Gioia, Diana
University of Bologna
Project manage
Giacomelli, Alessandra
UNA-API
Project member
Bocquet, Michel
Apimedia
Data Curator
Bulet, Philippe
University Grenoble Alpes
Supervisor
Deposit date
10 Jun 2024 16:09
Last modified
10 Jun 2024 16:09
Project name
AGRITECH - National Research Centre for Agricultural Technologies
Funding program
MUR-EC - PNRR Missione 4 Componente 2 Investimento 1.4
URI

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