Biosensors and Bioanalysis Lab, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, IQUIBICEN, CONICET, CABA 1428, Argentina.
Sensors (Basel). 2012;12(9):12220-34. doi: 10.3390/s120912220. Epub 2012 Sep 5.
Nitrogen compounds like urea and melamine are known to be commonly used for milk adulteration resulting in undesired intoxication; a well-known example is the Chinese episode occurred in 2008. The development of a rapid, reliable and economic test is of relevance in order to improve adulterated milk identification. Cyclic voltammetry studies using an Au working electrode were performed on adulterated and non-adulterated milk samples from different independent manufacturers. Voltammetric data and their first derivative were subjected to functional principal component analysis (f-PCA) and correctly classified by the KNN classifier. The adulterated and non-adulterated milk samples showed significant differences. Best results of prediction were obtained with first derivative data. Detection limits in milk samples adulterated with 1% of its total nitrogen derived from melamine or urea were as low as 85.0 mg · L(-1) and 121.4 mg · L(-1), respectively. We present this method as a fast and robust screening method for milk adulteration analysis and prevention of food intoxication.
已知氮化合物如尿素和三聚氰胺常用于牛奶掺假,导致不良中毒;一个著名的例子是 2008 年中国发生的事件。为了改进掺假牛奶的识别,开发一种快速、可靠和经济的测试方法非常重要。使用金工作电极对来自不同独立制造商的掺假和未掺假牛奶样品进行循环伏安法研究。对伏安数据及其一阶导数进行功能主成分分析(f-PCA),并由 KNN 分类器进行正确分类。掺假和未掺假的牛奶样品显示出显著差异。用一阶导数数据获得了最佳的预测结果。用 1%的三聚氰胺或尿素总氮源对牛奶样品进行掺假时,检测限低至 85.0 mg · L(-1)和 121.4 mg · L(-1)。我们将这种方法作为一种快速而稳健的牛奶掺假分析和防止食物中毒的筛选方法。