Balan Biji, Dhaulaniya Amit S, Jamwal Rahul, Yadav Amit, Kelly Simon, Cannavan Andrew, Singh Dileep K
Soil Microbial Ecology and Environment Toxicology Laboratory, Department of Zoology, University of Delhi, Delhi 110007, India.
Food and Environmental Protection Laboratory, International Atomic Energy Agency, Vienna International Centre, Vienna, Austria.
Spectrochim Acta A Mol Biomol Spectrosc. 2020 Oct 15;240:118628. doi: 10.1016/j.saa.2020.118628. Epub 2020 Jun 19.
Adulteration of milk to gain economic benefit has become a common practice in recent years. Sucrose is illegally added in milk to reconstitute its compositional requirement by improving the total solid contents. The present study is aimed to use FTIR spectroscopy in combination with multivariate chemometric modelling for the differentiation and quantification of sucrose in cow milk. Pure milk and adulterated milk spectra (0.5-7.5% w/v) were observed in the spectral region 4000-400 cm. Principal component analysis (PCA) was used for the discrimination of pure milk and adulterated milk. Soft independent modelling of class analogy (SIMCA) was able to classify test samples with a classification efficiency of 100%. Partial least square regression (PLS-R) and principle component regression (PCR) models were established for normal spectra, 1st derivative and 2nd derivative for the quantification of sucrose in milk. PLS-R model (normal spectra) in the combined wavenumber range of 1070-980 cm showed the best prediction based on parameters like coefficient of determination (R) (Cal: 0.996; Val: 0.993), RMSE (Cal: 0.15% w/v; Val: 0.20% w/v), RE% (Cal: 4.9% w/v; Val: 5.1% w/v) and RPD (13.40). This method has a detection level of 0.5% w/v sucrose adulteration.
近年来，为谋取经济利益而掺假牛奶已成为一种常见做法。非法向牛奶中添加蔗糖，通过提高总固体含量来重新构成其成分要求。本研究旨在使用傅里叶变换红外光谱（FTIR）结合多元化学计量学建模来区分和定量牛奶中的蔗糖。在4000 - 400 cm光谱区域观察了纯牛奶和掺假牛奶（0.5 - 7.5% w/v）的光谱。主成分分析（PCA）用于区分纯牛奶和掺假牛奶。类软独立建模（SIMCA）能够以100%的分类效率对测试样品进行分类。建立了偏最小二乘回归（PLS - R）和主成分回归（PCR）模型，用于对牛奶中蔗糖的正常光谱、一阶导数光谱和二阶导数光谱进行定量。在1070 - 980 cm的组合波数范围内，基于决定系数（R）（校准集：0.996；验证集：0.993）、均方根误差（RMSE）（校准集：0.15% w/v；验证集：0.20% w/v）、相对误差百分比（RE%）（校准集：4.9% w/v；验证集：5.1% w/v）和相对预测偏差（RPD）（13.40）等参数，PLS - R模型（正常光谱）显示出最佳预测效果。该方法对蔗糖掺假的检测限为0.5% w/v。
