Melamine detection by mid- and near-infrared (MIR/NIR) spectroscopy: A quick and sensitive method for dairy products analysis including liquid milk, infant formula, and milk powder

• Published in: Talanta (Oxford) 2011-07, Vol.85 (1), p.562-568
• Main Authors: Balabin, Roman M., Smirnov, Sergey V.
• Format: Article
• Language: English
• Subjects: Algorithms; Animals; Artificial neural networks (ANN); Biofuel (ethanol–gasoline fuel, vegetable oil, biodiesel); Dairy Products - analysis; Dairy/milk products (liquid milk, infant formula, milk powder); Food (milk-derived products); Food Analysis - methods; Food Contamination - analysis; Humans; Infant Formula - chemistry; Limit of Detection; Milk - chemistry; Partial least squares regression (PLS); Powders - analysis; Spectroscopy, Near-Infrared - methods; Support vector machines (SVMs: SVR and LS-SVM); Triazines - analysis
• ISSN: 0039-9140; 1873-3573
• DOI: 10.1016/j.talanta.2011.04.026

Melamine (2,4,6-triamino-1,3,5-triazine) is a nitrogen-rich chemical implicated in the pet and human food recalls and in the global food safety scares involving milk products. Due to the serious health concerns associated with melamine consumption and the extensive scope of affected products, rapid and sensitive methods to detect melamine's presence are essential. We propose the use of spectroscopy data-produced by near-infrared (near-IR/NIR) and mid-infrared (mid-IR/MIR) spectroscopies, in particular—for melamine detection in complex dairy matrixes. None of the up-to-date reported IR-based methods for melamine detection has unambiguously shown its wide applicability to different dairy products as well as limit of detection (LOD) below 1ppm on independent sample set. It was found that infrared spectroscopy is an effective tool to detect melamine in dairy products, such as infant formula, milk powder, or liquid milk. ALOD below 1ppm (0.76±0.11ppm) can be reached if a correct spectrum preprocessing (pretreatment) technique and a correct multivariate (MDA) algorithm—partial least squares regression (PLS), polynomial PLS (Poly-PLS), artificial neural network (ANN), support vector regression (SVR), or least squares support vector machine (LS-SVM)—are used for spectrum analysis. The relationship between MIR/NIR spectrum of milk products and melamine content is nonlinear. Thus, nonlinear regression methods are needed to correctly predict the triazine-derivative content of milk products. It can be concluded that mid- and near-infrared spectroscopy can be regarded as a quick, sensitive, robust, and low-cost method for liquid milk, infant formula, and milk powder analysis.