Near-infrared spectroscopy as a tool for rapid screening of deoxynivalenol in wheat flour and its applicability in the industry

ABSTRACT

This study aimed to evaluate the applicability and efficiency of Near-Infrared Spectroscopy (NIR) by using dispersive NIR and Fourier Transform NIR to analyse 267 samples of Brazilian wheat flour contaminated with deoxynivalenol (DON). For this, Partial Least-squares Discriminant Analysis (PLS-DA) and Principal Component Analysis-Linear Discriminant Analysis (PC-LDA) were used as discriminatory methods. Next, the samples were classified according to the maximum tolerated limits (MTL) for DON in Brazil, 750 μg kg⁻¹, and two groups were established for the calibration set: category A (≤450 μg kg⁻¹), non-contaminated or below the MTL; and category B (>450 μg kg⁻¹), contaminated or above the MTL. Validation samples through PLS-DA showed correct classification rates in the range of 85–87.5% and presented a 10–15% error; for PC-LDA, the hit rate was over 85% with an error of 10–15%. The present findings demonstrate that NIR is an excellent alternative method to classify wheat flour samples according to DON content.

KEYWORDS:

• Food safety
• mycotoxins
• infrared spectroscopy
• screening
• classification methods
• Brazil

Acknowledgments

The authors would like to thank the Federal University of Santa Maria (UFSM), Coordination for the Improvement of Higher Education Personnel (CAPES) and LAMIC for enabling the development of this project.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This study was funded by the National Council for Scientific and Technological Development (CNPq), in the form of an fellowship of research productivity (PQ; grant number 310190/2018-5) awarded to CAM.