ArticlePloS one2021
Multivariate method for prediction of fumonisins B1 and B2 and zearalenone in Brazilian maize using Near Infrared Spectroscopy (NIR).
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 31 citations in OpenAlex.
- Toward smart and in-situ mycotoxin detection in food via vibrational spectroscopy and machine learning.Food chemistry: X · 2025Review
- Rapid screening of fumonisins in maize using near-infrared spectroscopy (NIRS) and machine learning algorithms.Food chemistry: X · 2025Article
- Article
- Recent Advances in Mycotoxin Determination in Fish Feed Ingredients.Molecules (Basel, Switzerland) · 2023Review
- Article
- Research Progress of Applying Infrared Spectroscopy Technology for Detection of Toxic and Harmful Substances in Food.Foods (Basel, Switzerland) · 2022Review
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Authors and funding
6 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Fumonisins (FBs) and zearalenone (ZEN) are mycotoxins which occur naturally in grains and cereals, especially maize, causing negative effects on animals and humans. Along with the need for constant monitoring, there is a growing demand for rapid, non-destructive methods. Among these, Near Infrared Spectroscopy (NIR) has made great headway for being an easy-to-use technology. NIR was applied in the present research to quantify the contamination level of total FBs, i.e., fumonisin B1+fumonisin B2 (FB1+FB2), and ZEN in Brazilian maize. From a total of six hundred and seventy-six samples, 236 were analyzed for FBs and 440 for ZEN. Three regression models were defined: one with 18 principal components (PCs) for FB1, one with 10 PCs for FB2, and one with 7 PCs for ZEN. Partial least square regression algorithm with full cross-validation was applied as internal validation. External validation was performed with 200 unknown samples (100 for FBs and 100 for ZEN). Correlation coefficient (R), determination coefficient (R2), root mean square error of prediction (RMSEP), standard error of prediction (SEP) and residual prediction deviation (RPD) for FBs and ZEN were, respectively: 0.809 and 0.991; 0.899 and 0.984; 659 and 69.4; 682 and 69.8; and 3.33 and 2.71. No significant difference was observed between predicted values using NIR and reference values obtained by Liquid Chromatography Coupled to Tandem Mass Spectrometry (LC-MS/MS), thus indicating the suitability of NIR to rapidly analyze a large numbers of maize samples for FBs and ZEN contamination. The external validation confirmed a fair potential of the model in predicting FB1+FB2 and ZEN concentration. This is the first study providing scientific knowledge on the determination of FBs and ZEN in Brazilian maize samples using NIR, which is confirmed as a reliable alternative methodology for the analysis of such toxins.
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