Online detection and quantification of particles of ergot bodies in cereal flour using near-infrared hyperspectral imaging

摘要

The objective of this study is to assess near-infrared (NIR) hyperspectral imaging for the detection of ergot bodies at the particle level in cereal flour. For this study, ground ergot body samples and wheat flour samples as well as mixtures of both from 100 to 500,000mgkg(-1) were analysed. Partial least squares discriminant analysis (PLS-DA) models were developed and applied to spectral images in order to detect the ergot body particles. Ergot was detected in 100% of samples spiked at more than 10,000mgkg(-1) and no false-positives were obtained with non-contaminated samples. A correlation of 0.99 was calculated between the reference values and the values predicted by the PLS-DA model. For the cereal flours containing less than 10,000mgkg(-1) of ergot, it was possible for some samples spiked as low as 100mgkg(-1) to detect enough pixels of ergot to conclude that the sample was contaminated. However, some samples were under- or overestimated, which can be explained by the lack of homogeneity in relation to the sampling issue and the thickness of the sample. This study has demonstrated the potential of NIR hyperspectral imaging combined with chemometrics as an alternative solution for discriminating ergot body particles from cereal flour.