German Federal Institute for Risk Assessment, Max-Dohrn-Straße 8-10, 10589 Berlin, Germany.
German Federal Institute for Risk Assessment, Max-Dohrn-Straße 8-10, 10589 Berlin, Germany.
Food Chem. 2018 Aug 15;257:112-119. doi: 10.1016/j.foodchem.2018.03.007. Epub 2018 Mar 2.
A method for the non-targeted detection of paprika adulteration was developed using Fourier transform mid-infrared (FT-MIR) spectroscopy and one-class soft independent modelling of class analogy (OCSIMCA). One-class models based on commercially available paprika powders were developed and optimised to provide >80% sensitivity by external validation. The performances of the established models for adulteration detection were tested by predicting spiked paprika samples with various types of fraudulent material and levels of adulterations including 1% (w/w) Sudan I, 1% (w/w) Sudan IV, 3% (w/w) lead chromate, 3% (w/w) lead oxide, 5% (w/w) silicon dioxide, 10% (w/w) polyvinyl chloride, and 10% (w/w) gum arabic. Further, the influence of data preprocessing on the model performance was investigated. Relationship between classification results and data preprocessing was identified and specificity >80% was achieved for all adulterants by applying different preprocessing methods including standard normal variate (SNV), first and second derivatives, smoothing, and combinations thereof.
建立了一种基于傅里叶变换中红外光谱（FT-MIR）和单类软独立建模分类类比（OCSIMCA）的辣椒粉非靶向掺假检测方法。基于市售辣椒粉粉末建立并优化了单类模型，通过外部验证提供了>80%的灵敏度。通过预测不同类型掺假物和掺假水平（包括 1%（w/w）苏丹红 I、1%（w/w）苏丹红 IV、3%（w/w）铬酸铅、3%（w/w）氧化铅、5%（w/w）二氧化硅、10%（w/w）聚氯乙烯和 10%（w/w）阿拉伯胶）的辣椒粉加标样品，对建立的掺假检测模型的性能进行了测试。此外，还研究了数据预处理对模型性能的影响。确定了分类结果与数据预处理之间的关系，并通过应用不同的预处理方法（包括标准正态变量（SNV）、一阶和二阶导数、平滑以及它们的组合），实现了对所有掺杂物的特异性>80%。
