Local anomaly detection and quantitative analysis of contaminants in soybean meal using near infrared imaging: The example of non-protein nitrogen

检出限 三聚氰胺 异常检测 马氏距离 污染 餐食 检测阈值 色谱法 模式识别(心理学) 计算机科学 化学 分析化学(期刊) 食品科学 数学 人工智能 生物 生态学 有机化学 实时计算
作者
Guanghui Shen,Juan Antonio Fernández Pierna,Vincent Baeten,Yaoyao Cao,Lujia Han,Zengling Yang
出处
期刊:Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy [Elsevier BV]
卷期号:225: 117494-117494 被引量:6
标识
DOI:10.1016/j.saa.2019.117494
摘要

The melamine scandal indicates that traditional targeted detection methods only detect the specifically listed forms of contamination, which leads to the failure to identify new adulterants in time. In order to deal with continually changing forms of adulterations in food and feed and make up for the inadequacy of targeted detection methods, an untargeted detection method based on local anomaly detection (LAD) using near infrared (NIR) imaging was examined in this study. In the LAD method, with a particular size of window filter and at a 99% level of confidence, a specific value of Global H (GH, modified Mahalanobis distance) can be used as a threshold for anomalous spectra detection and quantitative analysis. The results showed an acceptable performance for the detection of contaminations with the advantage of no need of building a ‘clean’ library. And, a high coefficient of determination (R2LAD = 0.9984 and R2PLS-DA = 0.9978) for the quantitative analysis of melamine with a limit of detection lower than 0.01% was obtained. This indicates that the new strategy of untargeted detection has the potential to move from passive to active for food and feed safety control.
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