Validation and transferability study of a method based on near-infrared hyperspectral imaging for the detection and quantification of ergot bodies in cereals

AbstractIn recent years, near-infrared (NIR) hyperspectral imaging has proved its suitability for quality and safety control in the cereal sector by allowing spectroscopic images to be collected at single-kernel level, which is of great interest to cereal control laboratories. Contaminants in cereals include, inter alia, impurities such as straw, grains from other crops, and insects, as well as undesirable substances such as ergot (sclerotium of Claviceps purpurea). For the cereal sector, the presence of ergot creates a high toxicity risk for animals and humans because of its alkaloid content. A study was undertaken, in which a complete procedure for detecting ergot bodies in cereals was developed, based on their NIR spectral characteristics. These were used to build relevant decision rules based on chemometric tools and on the morphological information obtained from the NIR images. The study sought to transfer this procedure from a pilot online NIR hyperspectral imaging system at laboratory level to a NIR hyperspectral imaging system at industrial level and to validate the latter. All the analyses performed showed that the results obtained using both NIR hyperspectral imaging cameras were quite stable and repeatable. In addition, a correlation higher than 0.94 was obtained between the predicted values obtained by NIR hyperspectral imaging and those supplied by the stereo-microscopic method which is the reference method. The validation of the transferred protocol on blind samples showed that the method could identify and quantify ergot contamination, demonstrating the transferability of the method. These results were obtained on samples with an ergot concentration of 0.02 % which is less than the EC limit for cereals (intervention grains) destined for humans fixed at 0.05 %. Online Abstract FigurePictures showing a the manual removal of ergot bodies and b the observation by the stereo-microscopic method (official method); c the metallic holder with the reference material, and d the NIR hyperspectral SisuCHEMA instrument

[1]  Vincent Baeten,et al.  In-House Validation of a near Infrared Hyperspectral Imaging Method for Detecting Processed Animal Proteins in Compound Feed , 2010 .

[2]  Pierre Dardenne,et al.  Authentication and Traceability of Agricultural and Food Products Using Vibrational Spectroscopy , 2010 .

[3]  M. A. Jonker,et al.  Worldwide regulations for mycotoxins in food and feed in 2003 , 2004 .

[4]  G. Rottinghaus,et al.  Determination of Ergot Alkaloid Content in Tall Fescue by Near‐Infrared Spectroscopy , 2005 .

[5]  Kim H. Esbensen,et al.  Representative sampling of large kernel lots I. Theory of Sampling and variographic analysis , 2012 .

[6]  Paul Geladi,et al.  Techniques and applications of hyperspectral image analysis , 2007 .

[7]  P. Dardenne,et al.  Online detection and quantification of ergot bodies in cereals using near infrared hyperspectral imaging , 2012, Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment.

[8]  Vincent Baeten,et al.  Combination of support vector machines (SVM) and near‐infrared (NIR) imaging spectroscopy for the detection of meat and bone meal (MBM) in compound feeds , 2004 .

[9]  R. Krska,et al.  Significance, chemistry and determination of ergot alkaloids: A review , 2008, Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment.

[10]  Rohit Bhargava,et al.  Spectrochemical Analysis Using Infrared Multichannel Detectors , 2007 .

[11]  P. Scott Ergot alkaloids: extent of human and animal exposure , 2009 .

[12]  J. Pierna,et al.  NIR Hyperspectral Imaging Methods for Quality and Safety Control of Food and Feed Products: Contributions to Four European Projects , 2010 .

[13]  J. Pierna,et al.  Hyperspectral Imaging Techniques: an Attractive Solution for the Analysis of Biological and Agricultural Materials , 2007 .