A Quick Detection of Melamine Adulteration in Milk and Dairy Products Using First-Order Multivariate Calibration

Abstract

In this work, a reasonable substitute with laborious and expensive liquid chromatography for quick melamine detection in milk and dairy products is outlined. To accomplish this aim, first-order multivariate calibration methods including partial least squares (PLS) and net analyte preprocessing-partial least squares (NAP/PLS) are adopted for melamine quantification in complex food extracts down to 0.07 mg/kg without the need for efficient sample clean up. Solution pH has a significant influence on the performance of the proposed method and poor prediction was observed at pH 2.0 and 4.0. NAP/PLS outperformed PLS for melamine quantification, where analysis was carried out over the spectral domain 215-260 nm and pH 2-8. By using NAP/PLS, melamine is accurately quantified in spiked milk and dairy products (0.5-2.0 mg/kg) containing varying levels of fat (0-29%) and protein (3-23%), which proves the workability of the proposed method for handling wide spectrum of foodstuffs. At spiking level of 0.5 mg/kg, the average recoveries were acceptable (91.2-105.3%). However, modest recoveries were observed for food items containing high levels of protein and fat. Statistical tests confirmed that (with few exceptions) the NAP/PLS is of comparable analytical performance for highly advanced liquid chromatography which gives more chance for the current method to be applied for quick melamine detection in milk and dairy products. The proposed methods would be applied in food laboratories, especially when advanced instruments are not available. PRACTICAL APPLICATIONSA new application of multivariate calibration for melamine detection (down to 0.07 mg/kg) in different types of milk and two dairy products is reported. The proposed analytical method would be of great importance from practical point of view considering the fact that chromatographic separation was avoided. The proposed methods would be applied in food laboratories, especially when advanced instruments are not available.

Determination and Classification of Jordanian Propolis Samples According to Their Elemental Profile

“…X 50×9 is decomposed into T 50×h and L h×9, where h is the number of optimum factors needed for optimum matrix decomposition. The value of h is estimated by leave-one-out cross-validation mythology [19,20] . Once h is computed, scores and loading vectors are viewed to assess the natural clusters of samples and the significance of variables for propolis clustering.…”