Title of article :
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
Author/Authors :
Balabin، نويسنده , , Roman M. and Smirnov، نويسنده , , Sergey V.، نويسنده ,
Issue Information :
ماهنامه با شماره پیاپی سال 2011
Pages :
7
From page :
562
To page :
568
Abstract :
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 melamineʹs 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 1 ppm 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 1 ppm (0.76 ± 0.11 ppm) 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.
Keywords :
vegetable oil , Biofuel (ethanol–gasoline fuel , biodiesel) , Food (milk-derived products) , Dairy/milk products (liquid milk , milk powder) , Partial least squares regression (PLS) , Artificial Neural Networks (ANN) , infant formula , Support vector machines (SVMs: SVR and LS-SVM)
Journal title :
Talanta
Serial Year :
2011
Journal title :
Talanta
Record number :
1662773
Link To Document :
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