Online detection and quantification of ergot bodies in cereals using near infrared hyperspectral imaging

Abstract

The occurrence of ergot bodies (sclerotia of Claviceps purpurea) in cereals presents a high toxicity risk for animals and humans due to the alkaloid content. To reduce this risk, the European Commission fixed an ergot concentration limit of 0.1% in all feedstuffs containing unground cereals, and a limit of 0.05% in 'intervention' cereals destined for humans. This study sought to develop a procedure based on near infrared hyperspectral imaging and multivariate image analysis to detect and quantify ergot contamination in cereals. Hyperspectral images were collected using an NIR hyperspectral line scan combined with a conveyor belt. All images consisted of lines of 320 pixels that were acquired at 209 wavelength channels (1100-2400 nm). To test the procedure, several wheat samples with different levels of ergot contamination were prepared. The results showed a correlation higher than 0.99 between the predicted values obtained using chemometric tools such as partial least squares discriminant analysis or support vector machine and the reference values. For a wheat sample with a level of ergot contamination as low as 0.01 %, it was possible to identify groups of pixels detected as ergot to conclude that the sample was contaminated. In addition, no false positives were obtained with non-contaminated samples. The limit of detection was found to be 145 mg/kg and the limit of quantification 341 mg/kg. The reproducibility tests of the measurements performed over several weeks showed that the results were always within the limits allowed. Additional studies were done to optimise the parameters in terms of number of samples analysed per unit of time or conveyor belt speed. It was shown that ergot can be detected using a speed of 1-100 mm/s and that a sample of 250 g can be analysed in 1 min.

NIR hyperspectral imaging spectroscopy and chemometrics for the discrimination of roots and crop residues extracted from soil samples

“…Vermeulen et al reported that spectra in a pixel are often a mixture of the pure constituent spectra. 34 Spectra acquired in the central part of crop residues were not affected by this phenomenon because crop residues are quite large elements and it was easy to acquire pure spectra on it. This confusion due to the low spatial resolution of the instrument and the mix of spectra was also well observed on predicted images of roots.…”

Estimating percentages of fusarium-damaged kernels in hard wheat by near-infrared hyperspectral imaging

“…At the same time, Delwiche and Gaines (2005), working with single kernel reflectance from two spot-illuminated, fiber optic conveyance spectrometers arrived at the same conclusions as Polder's group. Elsewhere, Vermeulen et al (2012) described the use of HSI to detect ergot bodies (sclerotia of the fungus Claviceps purpurea) among raw grain. Very recent work by Senthilkumar et al (2017) documents the use of HSI to classify ochratoxin A (produced by Penicillium spp.)…”

Detection of Melamine in Soybean Meal Using Near-Infrared Microscopy Imaging with Pure Component Spectra as the Evaluation Criteria

“…NIRM imaging is a type of NIR imaging and has better spatial resolution. In the past decade, NIR imaging has become a powerful analytical tool for detecting contaminants and defects in agrofood products [19][20][21][22][23][24][25]. For feed products, several investigations have been conducted on the feasibility of using NIRM to detect, identify, and quantify processed animal byproducts [26][27][28][29][30] and on using NIR imaging for the complete screening of compound feeds [31].…”