Application of near-infrared hyperspectral (NIR) images combined with multivariate image analysis in the differentiation of two mycotoxicogenic Fusarium species associated with maize

Maize (Zea mays L.) is one of the most versatile crops worldwide with high socioeconomic relevance. However, mycotoxins produced by pathogenic fungi are of constant concern in maize production, as they pose serious risks to human and animal health. Thus, the search for rapid detection and/or identification methods for mycotoxins and mycotoxin-producing fungi for application in food safety remain important. In this work, we implemented use of near infrared hyperspectral images (HSI-NIR) combined with pattern recognition analysis, partial-leastsquares discriminant analysis (PLS-DA) of images, to develop a rapid method for identification of Fusarium verticillioides and F. graminearum. Validation of the HSI-NIR method and subsequent analysis was realized using 15 Fusarium spp. isolates. The method was efficient as a rapid, non-invasive, and non-destructive assessment was achieved with 100% accuracy, sensitivity, and specificity for both fungi.

Automated summary

The study successfully developed a rapid method for identifying maize fungi using hyperspectral imaging technology combined with pattern recognition analysis, achieving 100% accuracy, sensitivity, and specificity in non-invasive and non-destructive assessments.