Department of Food Science and Technology, The Ohio State University, 110 Parker Food Science and Technology Building, 2015 Fyffe Road, Columbus, OH 43210, USA.
J Agric Food Chem. 2013 Feb 13;61(6):1205-11. doi: 10.1021/jf303814g. Epub 2013 Jan 31.
A simple and fast method for the detection and quantification of milk adulteration was developed using portable and hand-held infrared (IR) spectrometers. Milk samples were purchased from local supermarkets (Columbus, OH, USA) and spiked with tap water, whey, hydrogen peroxide, synthetic urine, urea, and synthetic milk in different concentrations. Spectral data were collected using mid-infrared (MIR) and near-infrared (NIR) spectrometers. Soft independent modeling of class analogy (SIMCA) classification models exhibited tight and well-separated clusters allowing the discrimination of control from adulterated milk samples. Partial least-squares regression (PLSR) was used to estimate adulteration levels, and results showed high coefficients of determination (R(2)) and low standard errors of prediction (SEP). Classification and quantification models indicated that the tested MIR systems were superior to NIR systems in monitoring milk adulteration. This method can be potentially used as an alternative to traditional methods due to their simplicity, sensitivity, low energy cost, and portability.
一种简单快速的牛奶掺假检测和定量方法已经开发出来，使用便携式和手持式红外（IR）光谱仪。牛奶样品从当地超市（美国俄亥俄州哥伦布市）购买，并在不同浓度下用水、乳清、过氧化氢、合成尿液、尿素和合成牛奶进行掺假。使用中红外（MIR）和近红外（NIR）光谱仪采集光谱数据。软独立建模分类类比（SIMCA）分类模型显示出紧密且分离良好的聚类，允许区分掺假和未掺假的牛奶样品。偏最小二乘回归（PLSR）用于估计掺假水平，结果表明具有较高的决定系数（R²）和较低的预测标准误差（SEP）。分类和定量模型表明，测试的 MIR 系统在监测牛奶掺假方面优于 NIR 系统。由于其简单、灵敏、低能耗和便携性，这种方法可能会替代传统方法。
