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Talanta. 2016 May 1;151:183-191. doi: 10.1016/j.talanta.2016.01.035. Epub 2016 Jan 18.

Detection of melamine in milk powders using near-infrared hyperspectral imaging combined with regression coefficient of partial least square regression model.

Author information

1
National Institute of Agricultural Science, Rural Development Administration, Jeonju 54875, Republic of Korea.
2
Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, USDA, 10300 Baltimore Avenue, Beltsville, MD 20705, USA. Electronic address: moon.kim@ars.usda.gov.
3
Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, USDA, 10300 Baltimore Avenue, Beltsville, MD 20705, USA.
4
College of Biosystems Engineering & Food Science, Zhejiang University, Hangzhou 310029, PR China.
5
Department of Bioindustrial Machinery Engineering, Chungnam National University, Daejeon 34134, Republic of Korea.

Abstract

Illegal use of nitrogen-rich melamine (C3H6N6) to boost perceived protein content of food products such as milk, infant formula, frozen yogurt, pet food, biscuits, and coffee drinks has caused serious food safety problems. Conventional methods to detect melamine in foods, such as Enzyme-linked immunosorbent assay (ELISA), High-performance liquid chromatography (HPLC), and Gas chromatography-mass spectrometry (GC-MS), are sensitive but they are time-consuming, expensive, and labor-intensive. In this research, near-infrared (NIR) hyperspectral imaging technique combined with regression coefficient of partial least squares regression (PLSR) model was used to detect melamine particles in milk powders easily and quickly. NIR hyperspectral reflectance imaging data in the spectral range of 990-1700nm were acquired from melamine-milk powder mixture samples prepared at various concentrations ranging from 0.02% to 1%. PLSR models were developed to correlate the spectral data (independent variables) with melamine concentration (dependent variables) in melamine-milk powder mixture samples. PLSR models applying various pretreatment methods were used to reconstruct the two-dimensional PLS images. PLS images were converted to the binary images to detect the suspected melamine pixels in milk powder. As the melamine concentration was increased, the numbers of suspected melamine pixels of binary images were also increased. These results suggested that NIR hyperspectral imaging technique and the PLSR model can be regarded as an effective tool to detect melamine particles in milk powders.

KEYWORDS:

Hyperspectral imaging; Melamine; Milk powder adulteration; Partial least square regression; Regression coefficient

PMID:
26946026
DOI:
10.1016/j.talanta.2016.01.035
[Indexed for MEDLINE]

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