FAIR Data Fund use case: Sharing high-quality LC-MS/MS spectral data of over 100 emerging chemical risks in the food chain

Fed­eri­co Padil­la-Gon­za­lez (Wagenin­gen Uni­ver­si­ty & Research) talks about his project sup­port­ed by the FAIR Data Fund (2023 edi­tion).

What is your project about?

My project focus­es on enhanc­ing food safe­ty by shar­ing high-qual­i­ty spec­tral data for over 100 emerg­ing chem­i­cal risks in the food chain. These chem­i­cals, which include pes­ti­cides and indus­tri­al con­t­a­m­i­nants, are among the risks pri­or­i­tized by the Euro­pean Food Safe­ty Author­i­ty due to their tox­i­c­i­ty and per­sis­tence in the envi­ron­ment. Despite their sig­nif­i­cance, the nec­es­sary spec­tral data for accu­rate­ly iden­ti­fy­ing these com­pounds in food and envi­ron­men­tal sam­ples is large­ly unavail­able. Using liq­uid chro­matog­ra­phy cou­pled with high-res­o­lu­tion tan­dem mass spec­trom­e­try (LC-HRMS/MS), we gen­er­at­ed and rig­or­ous­ly curat­ed spec­tral data for each com­pound, ensur­ing reli­a­bil­i­ty. By mak­ing this dataset pub­licly acces­si­ble and inte­grat­ing it into estab­lished data­bas­es like Mas­sIVE — GNPS, we aim to sup­port lab­o­ra­to­ries and reg­u­la­to­ry bod­ies in detect­ing these con­t­a­m­i­nants more effi­cient­ly. Ulti­mate­ly, the project con­tributes to bet­ter mon­i­tor­ing and man­age­ment of these chem­i­cal risks, thus pro­mot­ing a safer glob­al food sup­ply.

What are some key results that you can share so far?

One of the key out­comes of the project is that around 70% of the chem­i­cals in our dataset do not have a reli­able match with exist­ing spec­tral libraries like GNPS, indi­cat­ing that our dataset fills a crit­i­cal gap in avail­able resources. This sug­gests that our work is pro­vid­ing nov­el and valu­able infor­ma­tion that can enhance the detec­tion of a select­ed set of chem­i­cal con­t­a­m­i­nants. Addi­tion­al­ly, we suc­cess­ful­ly test­ed the spec­tral library on a sam­ple of sewage sludge intend­ed for use as plant fer­til­iz­er. Our library allowed us to iden­ti­fy a con­t­a­m­i­nant that might have oth­er­wise been missed using con­ven­tion­al resources, high­light­ing its prac­ti­cal val­ue in real-world food pro­duc­tion sys­tems.

How is the FAIR DATA Fund help­ing you with your project? What is the added val­ue?

The FAIR DATA Fund was essen­tial in mak­ing this project a suc­cess. The fund allowed us to ensure that the spec­tral data is find­able, acces­si­ble, inter­op­er­a­ble, and reusable (FAIR), thus max­i­miz­ing its impact. Fur­ther­more, it spurred the devel­op­ment of larg­er-scale ini­tia­tives with­in Wagenin­gen Food Safe­ty Research, where we aim to expand the spec­tral library to cov­er between 1,000 and 2,000 food con­t­a­m­i­nants and residues. For each com­pound, we are gen­er­at­ing at least six dif­fer­ent spec­tra at vary­ing col­li­sion ener­gies to enhance the dataset’s com­pre­hen­sive­ness. Part of this expand­ed dataset will also be made pub­licly acces­si­ble, offer­ing valu­able resources to the sci­en­tif­ic com­mu­ni­ty and fur­ther sup­port­ing glob­al efforts to improve food safe­ty.

Fed­eri­co Padil­la-Gon­za­lez (Wagenin­gen Uni­ver­si­ty & Research) is one of the FAIR Data Fund 2023 grantees.