Title :
Neural Network Based Method for Melamine Analysis in Liquid Milk
Author :
Smirnov, Sergey V.
Author_Institution :
Unimilk Joint Stock Co., Russia
Abstract :
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. It was found that infrared spectroscopy is an effective tool to detect melamine in liquid milk. The limit of detection (LOD) below 1 ppm (0.75 ppm) can be reached if a correct spectrum pre-processing (pre-treatment) technique and a correct multivariate (MDA) algorithm: partial least squares regression (PLS), polynomial PLS (Poly-PLS), or artificial neural network (ANN)-is used for spectrum analysis. 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 analysis. The technique can be applied for the automation of milk analysis.
Keywords :
dairy products; food safety; infrared spectroscopy; least squares approximations; neural nets; regression analysis; artificial neural network based method; liquid milk; melamine analysis; mid-infrared spectroscopies; multivariate algorithm; near-infrared spectroscopies; polynomial partial least squares regression; spectroscopy data; Artificial neural networks; Calibration; Dairy products; Petroleum; Powders; Spectroscopy; artificial neural network (ANN); food; liquid milk; partial least squares regression (PLS);
Conference_Titel :
Intelligent Computation Technology and Automation (ICICTA), 2011 International Conference on
Conference_Location :
Shenzhen, Guangdong
Print_ISBN :
978-1-61284-289-9
DOI :
10.1109/ICICTA.2011.535
