Department of Food Science and Technology, Science and Research Branch, Islamic Azad University, Tehran, Iran.
School of Chemistry, College of Science, University of Tehran, Tehran, Iran.
J Sci Food Agric. 2021 May;101(7):2696-2703. doi: 10.1002/jsfa.10894. Epub 2020 Nov 2.
The adulteration of milk by hazardous chemicals like surfactants has recently increased. It conceals the quality of the product to gain profit. As milk and milk-based products are consumed by many people, novel analytical procedures are needed to detect these adulterants. This study focused on Fourier-transform infrared (FTIR) spectroscopy equipped with an attenuated total reflection (ATR) accessory, and near-infrared (NIR) spectroscopy for the determination of milk-surfactant adulteration using a genetic algorithm (GA) coupled with multivariate methods. The model surfactant was sodium dodecyl sulfate (SDS), and its concentration varied from 1.94-19.4 gkg in adulterated samples.
Prominent peaks in the spectral range of 5500-6400 cm , 1160-1260 cm and 1049-1080 cm may correspond to the sulfonate group in SDS. A genetic algorithm could significantly reduce the number of variables to almost one third by selecting the specific wavenumber region. Principal component analysis (PCA) for ATR and NIR data indicated separate clusters of samples in terms of the concentration level of SDS (P ≤ 0.05). Partial least squares regression (PLSR) was used to determine the maximum R value for ATR and NIR data for calibration, cross-validation and prediction, which were 0.980, 0.972, 0.980, and 0.970, 0.937, and 0.956 respectively. The results showed apparent differences between unadulterated and adulterated samples using partial least squares-discriminant analysis (PLS-DA), which was validated by the permutation test.
The results clearly show the successful application of the proposed methods with multivariate analysis in the selection of variables, classification, clustering, and identification of the adulterant in amounts as low as 1.94 gkg in milk. © 2020 Society of Chemical Industry.
最近，牛奶等乳制品中掺入了表面活性剂等有害物质的情况有所增加。这种行为是为了牟取利益，掩盖产品质量。由于牛奶和以牛奶为基础的产品被许多人食用，因此需要新的分析程序来检测这些掺杂物。本研究使用傅里叶变换红外（FTIR）光谱仪和衰减全反射（ATR）附件，以及近红外（NIR）光谱仪，结合遗传算法（GA）和多元方法，研究了用于检测牛奶-表面活性剂掺假的方法。模型表面活性剂为十二烷基硫酸钠（SDS），其浓度在掺假样品中从 1.94-19.4 gkg 变化。
在光谱范围 5500-6400 cm 、1160-1260 cm 和 1049-1080 cm 处的谱峰可能对应 SDS 中的磺酸盐基团。遗传算法通过选择特定的波数区域，可以将变量数量显著减少到近三分之一。ATR 和 NIR 数据的主成分分析（PCA）表明，SDS 浓度水平的样品聚类明显（P ≤ 0.05）。ATR 和 NIR 数据的偏最小二乘回归（PLSR）用于确定校准、交叉验证和预测的最大 R 值，分别为 0.980、0.972、0.980 和 0.970、0.937 和 0.956。偏最小二乘判别分析（PLS-DA）结果表明，未掺假和掺假样品之间存在明显差异，该结果通过置换检验得到验证。
多元分析在变量选择、分类、聚类和鉴定低至 1.94 gkg 的牛奶掺杂物方面的成功应用，结果清晰可见。© 2020 英国化学学会。
