Department of Biosystems Engineering, Ferdowsi University of Mashhad, Mashhad, Iran.
Department of Biosystems Engineering, Ferdowsi University of Mashhad, Mashhad, Iran.
Food Chem. 2021 Dec 1;364:130406. doi: 10.1016/j.foodchem.2021.130406. Epub 2021 Jun 18.
Turmeric powder is a widely consumed spice, making it an attractive target for adulteration, which is not easily detected. The study examined the simultaneous use of IR spectroscopy in combination with controlled (PCA) and uncontrolled (PLS-DA and CMCA) pattern recognition techniques to detect and classify Sudan Red, starch and metanil yellow fraud in turmeric powder nondestructively. The results showed that the two major peaks in turmeric powder at 1625 cm and 1600 cm are not present in Sudan Red, starch and metanil yellow because these materials lack this functional group. Data distribution at the two PC locations showed clearly scattered clusters according to the four mixing studied models (turmeric powder, turmeric powder-Sudan Red mixture, turmeric powder-starch mixture and turmeric powder-metanil yellow mixture), but there was a clear overlap between turmeric powder and turmeric powder - Sudan red mixture. Both PLS-DA and SIMCA supervised methods showed satisfactory discrimination. The results also showed that in all the sample groups, when the samples were classified by PLS-DA, the values were higher compared to the SIMCA model. The overall precision of the SIMCA and PLS-DA classifier were 82% and 92%, respectively. However, when considering only two main categories adulterated (the samples at the groups 2, 3 and 4) and pure (the samples at the group 1), an acceptable degree of separation between the resulting classes was obtained. Consequently, IR spectroscopy with pattern recognition methods was found to be a promising tool for nondestructive grouping of turmeric powder samples with different types of adulteration in turmeric powder.
姜黄粉是一种广泛食用的香料，因此很容易成为掺假的目标，而这些掺假物不易被察觉。本研究考察了同时使用红外光谱结合控制（PCA）和非控制（PLS-DA 和 CMCA）模式识别技术，对姜黄粉中非破坏性地检测和分类苏丹红、淀粉和间苯二胺黄欺诈。结果表明，姜黄粉中在 1625cm 和 1600cm 处的两个主要峰不存在于苏丹红、淀粉和间苯二胺黄中，因为这些材料缺乏这个官能团。根据四个混合研究模型（姜黄粉、姜黄粉-苏丹红混合物、姜黄粉-淀粉混合物和姜黄粉-间苯二胺黄混合物），数据在两个 PC 位置的分布显示出明显的分散聚类，但姜黄粉和姜黄粉-苏丹红混合物之间存在明显的重叠。PLS-DA 和 SIMCA 监督方法都显示出令人满意的区分。结果还表明，在所有样品组中，当用 PLS-DA 对样品进行分类时，其值比 SIMCA 模型高。SIMCA 和 PLS-DA 分类器的总体精度分别为 82%和 92%。然而，当仅考虑两种主要掺假类型（样品在第 2、3 和 4 组）和纯品（样品在第 1 组）时，获得了可接受的分类间分离度。因此，红外光谱结合模式识别方法被发现是一种有前途的工具，用于对不同类型掺假的姜黄粉样品进行非破坏性分组。
