Journal
FOOD CONTROL
Volume 85, Issue -, Pages 259-268Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.foodcont.2017.10.008
Keywords
Maize; Defect detection; Mycotoxins; Multivariate Image Analysis; Multivariate calibration
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Funding
- Freeray S.r.l.
- Fornasier Tiziano C. S.a.s.
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This article describes the development of a fast and inexpensive method based on digital image analysis for the automated quantification of the percentage of defective maize (%DM). Defective kernels tend to foster high levels of mycotoxins like Deoxynivalenol (DON), which represents a risk for the health of humans and of farm animals. In this work, 332 RGB images of 83 mixtures containing different amounts of defective maize kernels were acquired using a digital camera. The mixtures were also analysed with a commercial ELISA test kit to determine their concentration of DON, that resulted highly correlated with the amount of defective kernels. Each image was then converted into a signal, named colourgram, which codifies its colour-related information content. The colourgrams were firstly explored using Principal Component Analysis. Then, calibration models of the %DM values were developed using Partial Least Squares (PLS) and interval PLS. The best interval PLS model allowed to predict the %DM values of external test set samples with a root mean square error value equal to 2.6%. Based on the output of this model it was also possible to highlight the defective-maize areas within the images, confirming the significance of the proposed approach. (C) 2017 Elsevier Ltd. All rights reserved.
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