Tien kleuren tegen gifschimmels: sneller en slimmer mycotoxines opsporen met licht en AI

Wannes luts
De Martelaere

Door slimme belichting en AI‑algoritmes kan de voedselketen sneller screenen op gevaarlijke mycotoxines in granen — zonder dure of trage laboprocedures. Mijn masteronderzoek laat zien dat je met een handvol zorgvuldig gekozen golflengten kleine monsters betrouwbaar kunt beoordelen. Dat opent de deur naar screening op individuele korrels, waardoor voedselveiligheid stijgt én onnodige verspilling van grote partijen (bijv. volledige scheepsladingen) kan dalen.

Waarom dit ertoe doet

Schimmels die gewassen aantasten, kunnen gifstoffen — mycotoxines — produceren, zoals aflatoxinen en deoxynivalenol. Deze stoffen zijn schadelijk voor de gezondheid. In Europa gelden daarom strikte limieten voor mycotoxineconcentraties in voedingsproducten. Vandaag gebeurt kwaliteitscontrole vaak steekproefsgewijs en met labotesten die tijd, geld en gespecialiseerde apparatuur vragen. Wat als we sneller, vaker en goedkoper kunnen screenen — dicht bij de oogst, in een silo of op de productielijn? En wat als we niet alleen batches, maar ook individuele producten kunnen bekijken in plaats van enkel op steekproeven te vertrouwen? Dat verhoogt de veiligheid en helpt verspilling te voorkomen.

Wat ik onderzocht

In mijn masterproef combineerde ik optische spectroscopie met machine learning om monsters van granen (zoals tarwe of maïs) razendsnel te beoordelen. Met spectroscopie meet je de ‘vingerafdruk’ van licht dat door of van een monster komt — bijvoorbeeld fluorescentie of gereflecteerd licht. Die metingen vertaalde ik naar duidelijke beslissingen: is een partij verdacht of lijkt ze veilig binnen gangbare normen? Belangrijk: naast nauwkeurigheid zocht ik naar praktische eenvoud. Daarom onderzocht ik hoe we het aantal benodigde golflengten drastisch kunnen terugbrengen, zodat geen dure set‑ups met een volledige spectrale scan meer nodig zijn. Zo zetten we een stap richting automatische inline‑sortering in de voedingsindustrie.

“Met ongeveer tien slim gekozen golflengten behoud je bijna de prestaties van volledige spectrums, maar wordt het systeem eenvoudiger en goedkoper.”

Wat is er nieuw aan mijn aanpak?

Veel studies gebruiken volledige spectrale informatie: honderden meetpunten over een brede golflengterange. Dat levert veel data op, maar maakt sensoren duurder en verwerking zwaarder. Ik testte selectiemethoden die automatisch die golflengten kiezen die het meest informatief zijn voor mycotoxinedetectie. Zo ontstaat een ‘lightweight’ sensorconcept: minder meetpunten, minder ruis en sneller rekenwerk — zonder de essentie te verliezen.

De resultaten in mensentaal

Het kernresultaat is eenvoudig: je hebt géén volledige regenboog aan data nodig. Met ongeveer tien zorgvuldig geselecteerde golflengten behaal je prestaties die dicht in de buurt komen van klassieke, uitgebreide spectrale metingen. In gewone taal: met weinig invoer kan het systeem nog steeds onderscheid maken tussen veilige en mogelijk gecontamineerde granen. Dat baan maakt voor compacte, betaalbare en draagbare of inline‑systemen die niet alleen de voedselveiligheid verbeteren, maar ook tijdig onnodige voedselverspilling kunnen vermijden.

Van labo naar praktijk

Stel je een graansilo of productielijn voor waar monsters automatisch onder een lichtbron passeren. Een sensor meet slechts een handvol golflengten en een algoritme geeft in realtime een inschatting: ‘oké’ of ‘verdacht — stuur naar het lab’. Zo’n triagesysteem maakt snellere beslissingen mogelijk, beperkt onnodige vernietiging van gezonde batches en houdt tóch de lat voor voedselveiligheid hoog. Het is geen vervanging van referentielaboratoria, maar een slimme filter die ze gerichter inzet.

Waar ligt de winst?

  • Snelheid: minder meetpunten betekent kortere meet‑ en rekentijd.
  • Kost: eenvoudige optiek en elektronica volstaan, wat het systeem betaalbaarder maakt.
  • Schaalbaarheid: door de lage complexiteit is inline of mobiele inzet realistischer.
  • Duurzaamheid: betere triage kan voedselverspilling verminderen

Grenzen en volgende stappen

Een eerlijk verhaal bevat ook nuances. Niet elke mycotoxine laat zich even gemakkelijk ‘zien’ met licht en natuurlijke contaminaties zijn vaak complex (bijv. meerdere toxines tegelijk in complexe ratios). Daarom zijn realistische veldproeven cruciaal: meten op verschillende graansoorten, oogstjaren en vochtigheidscondities. Ook samenwerking met industrie en controle‑instanties is essentieel om de techniek af te stemmen op echte beslismomenten.

Slot

Met licht en AI kunnen we mycotoxines sneller opsporen en tegelijk de drempel verlagen om vaker te testen. Dat is goed voor boeren, verwerkers én consumenten. De volgende stap is duidelijk: de stap van prototypes naar praktijkpilots, zodat deze technologie breder inzetbaar wordt — van veld tot fabriek.

 

Door Wannes Luts De Martelaere — Master in Photonics Engineering (VUB/UGent)

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Universiteit of Hogeschool
Vrije Universiteit Brussel
Thesis jaar
2025
Promotor(en)
Lien Smeesters
Thema('s)
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