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KMID : 1024420130170040377
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Food Engineering Progress
2013 Volume.17 No. 4 p.377 ~ p.386
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Prediction of the Melamine Particle Concentration in Milk Powder Using Hyperspectral Reflectance Imaging and Partial Least Square Regression Model
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Lim Jong-Guk
Kim Moon-S.
Baek In-Suck
Mo Chang-Yeun
Lee Ho-Young
Kang Suk-Won
Lee Kang-Jin
Kim Gi-Young
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Abstract
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Melamine has been reported to be responsible for kidney stones and renal failure among infants and children. Con-ventional detection methods, High-Performance Liquid Chromatography (HPLC) and Gas Chromatography (GC), aresensitive enough to detect trace amounts of the contaminant, but they are time consuming, expensive, and labor-intensive. Hyperspectral imaging methods, which combine spectroscopy and imaging, can provide rapid and non-destructive means to assess the quality and safety of agricultural products. In this study, near-infrared hyperspectralreflectance imaging combined with partial least square regression analysis was used to predict melamine particleconcentration in dry milk powder. Melamine particles, with concentration levels ranging from 0.02% to 1% byweight ratio (g/g), were mixed with dry milk powder and used for the experiment. Hyperspectral reflectance imagesin the wavelength range from 992.0nm to 1682.1nm were acquired for the mixtures. Then PLSR models weredeveloped with several preprocessing methods. Optimal wavelength bands were selected from 1454.5nm to 1555.6nm using beta-coefficients from the PLSR model. The best PLSR result for predicting melamine concentration inmilk powder was obtained using a 1st order derivative pretreatment with Rv=0.974, SEP=±0.055%, and F=6.
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KEYWORD
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melamine, milk powder, hyperspectral reflectance spectrum, PLSR
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