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Journal of Spectroscopy
Volume 2016, Article ID 5868170, 11 pages
http://dx.doi.org/10.1155/2016/5868170
Research Article

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

1College of Engineering, China Agricultural University, Haidian District, Beijing 100083, China
2Engineering College, Jiangxi Agricultural University, Nanchang 330045, China
3Valorisation of Agricultural Products Department, Walloon Agricultural Research Centre (CRA-W), Henseval Building, 24 Chaussée de Namur, 5030 Gembloux, Belgium

Received 27 July 2016; Accepted 30 August 2016

Academic Editor: Antonio A. Dos Santos

Copyright © 2016 Zengling Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Soybean meal was adulterated with melamine with the purpose of boosting the protein content for unlawful interests. In recent years, the near-infrared (NIR) spectroscopy technique has been widely used for guaranteeing food and feed security for its fast, nondestructive, and pollution-free characteristics. However, there are problems with using near-infrared (NIR) spectroscopy for detecting samples with low contaminant concentration because of instrument noise and sampling issues. In addition, methods based on NIR are indirect and depend on calibration models. NIR microscopy imaging offers the opportunity to investigate the chemical species present in food and feed at the microscale level (the minimum spot size is a few micrometers), thus avoiding the problem of the spectral features of contaminants being diluted by scanning. The aim of this work was to investigate the feasibility of using NIR microscopy imaging to identify melamine particles in soybean meal using only the pure component spectrum. The results presented indicate that using the classical least squares (CLS) algorithm with the nonnegative least squares (NNLS) algorithm, without needing first to develop a calibration model, could identify soybean meal that is both uncontaminated and contaminated with melamine particles at as low a level as 50 mg kg−1.