Journal Highlight: Non-destructive detection of dicyandiamide in infant formula powder using multi-spectral imaging coupled with chemometrics

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  • Published: May 15, 2017
  • Author: spectroscopyNOW
  • Channels: Chemometrics & Informatics
thumbnail image: Journal Highlight: Non-destructive detection of dicyandiamide in infant formula powder using multi-spectral imaging coupled with chemometrics

Multi-spectral imaging at 405–970 nm coupled with back-propagation neural networks has been used successfully for the detection of dicyandiamde in infant formula powder.

Non-destructive detection of dicyandiamide in infant formula powder using multi-spectral imaging coupled with chemometrics

Journal of the Science of Food and Agriculture, 2017, 97, 2094-2099
Changhong Liu, Wei Liu, Jianbo Yang, Ying Chen and Lei Zheng

Abstract: Dicyandiamide (DCD) contamination of milk and milk products has become an urgent and broadly recognised topic as a result of several food safety scares. This study investigated the potential of using multi-spectral imaging (405–970 nm) coupled with chemometrics for detection of DCD in infant formula powder. Partial least squares (PLS), least squares-support vector machines (LS-SVM), and back-propagation neural network (BPNN) were applied to develop quantitative models. Compared with PLS and LS-SVM, BPNN considerably improved the prediction performance with coefficient of determination in prediction (inline image) = 0.935 and 0.873, residual predictive deviation (RPD) = 3.777 and 3.060 for brand 1 and brand 2 of infant formula powders, respectively. Besides, multi-spectral imaging was able to differentiate unadulterated infant formula powder from samples containing 0.01% DCD with no misclassification using BPNN model. The study demonstrated that multi-spectral imaging combined with chemometrics enables rapid and non-destructive detection of DCD in infant formula powder.

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