The Experts below are selected from a list of 35649 Experts worldwide ranked by ideXlab platform
Kuanglin Chao - One of the best experts on this subject based on the ideXlab platform.
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Quantitative analysis of Melamine in milk powders using near-infrared hyperspectral imaging and band ratio
Journal of Food Engineering, 2016Co-Authors: Min Huang, Stephen R. Delwiche, Jianwei Qin, Changyeun Mo, Carlos Esquerre, Kuanglin Chao, Moon S. Kim, Qibing ZhuAbstract:Since 2008, the detection of the adulterant Melamine (2,4,6-triamino-1,3,5-triazine) in food products has become the subject of research due to several food safety scares. Near-infrared (NIR) hyperspectral imaging offers great potential for food safety and quality research because it combines the features of vibrational spectroscopy and digital imaging. In this study, NIR hyperspectral imaging was investigated for quantitative evaluation of Melamine particles in nonfat and whole milk powders. Melamine was mixed into milk powders in a concentration range of 0.02-1.00% (w/w). A NIR hyperspectral imaging system was used to acquire images (938-1654 nm) of Melamine powder, whole milk powder, nonfat milk powder, and mixtures of Melamine and each of the milk powders. Two optimal bands (1447 nm and 1466 nm) were selected by a linear correlation algorithm with pure milk and pure Melamine. Band ratio (B1447/1466) images coupled with a single threshold were used to create resultant images to visualize identification and distribution of the Melamine adulterant particles in milk powders. The identification results were verified by spectral feature comparison between separated mean spectra of Melamine pixels and milk pixels. Linear correlations (r) were found between the number of pixels identified as containing Melamine and Melamine concentration in nonfat milk and whole milk powders, which were 0.980 and 0.970 or higher, respectively. The study demonstrated that the combination of NIR hyperspectral imaging and simple band ratioing was promising for rapid quantitative analysis of Melamine in milk powders.
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Detection of Melamine in milk powders using near-infrared hyperspectral imaging combined with regression coefficient of partial least square regression model
Talanta, 2016Co-Authors: Jongguk Lim, Jianwei Qin, Changyeun Mo, Giyoung Kim, Insuck Baek, Xiaping Fu, Kuanglin Chao, Moon S. Kim, Byoung Kwan ChoAbstract: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 990-1700 nm 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.
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Detection of Melamine in milk powders based on NIR hyperspectral imaging and spectral similarity analyses
Journal of Food Engineering, 2014Co-Authors: Xiaping Fu, Jianwei Qin, Jongguk Lim, Hoyoung Lee, Ana Garrido-varo, Dolores Pérez-marín, Kuanglin Chao, Moon S. Kim, Yibin YingAbstract:Melamine (2,4,6-triamino-1,3,5-triazine) contamination of food has become an urgent and broadly recognized topic as a result of several food safety scares in the past five years. Hyperspectral imaging techniques that combine the advantages of spectroscopy and imaging have been widely applied for a variety of food quality and safety evaluations. In this study, near-infrared (NIR) hyperspectral imaging technique was investigated to detect low levels (≤1.0%) of Melamine particles in milk powders. Following image preprocessing (normalization and background removal), the spectrum of each pixel in the sample images was compared to the pure Melamine spectrum by spectral similarity measures including spectral angle measure (SAM), spectral correlation measure (SCM), and Euclidian distance measure (EDM). The three similarity analysis methods provided comparable results for Melamine particle detection where imaging allowed visualization of the distribution of Melamine particles within images of milk powder mixture samples that were prepared with various Melamine concentrations. The classification results were verified by spectral feature comparison between separated mean spectra of Melamine pixels and milk powder pixels. The study demonstrated that a combination of NIR hyperspectral imaging technique and spectral similarity analyses was an effective method for Melamine adulteration discrimination in milk powders. The method described in this study can also be applied to other chemicals or multi-chemicals adulterant detection in milk powders.
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Investigation of NIR hyperspectral imaging for discriminating Melamine in milk powder
Sensing for Agriculture and Food Quality and Safety V, 2013Co-Authors: Xiaping Fu, Jianwei Qin, Jongguk Lim, Hoyoung Lee, Kuanglin Chao, Moon S. Kim, Yibin YingAbstract:Melamine (2,4,6-triamino-1,3,5-triazine) contamination of food has become an urgent and broadly recognized issue for which rapid and accurate identification methods are needed by the food industry. In this study, the feasibility and effectiveness of near-infrared (NIR) hyperspectral imaging was investigated for detecting Melamine in milk powder. Hyperspectral NIR images (144 bands spanning from 990 to 1700 nm) were acquired for Petri dishes containing samples of milk powder mixed with Melamine at various concentrations (0.02% to 1%). Spectral bands that showed the most significant differences between pure milk and pure Melamine were selected, and two-band difference analysis was applied to the spectrum of each pixel in the sample images to identify Melamine particles in milk powders. The resultant images effectively allowed visualization of Melamine particle distributions in the samples. The study demonstrated that NIR hyperspectral imaging techniques can qualitatively and quantitatively identify Melamine adulteration in milk powders. ? 2013 SPIE.
Moon S. Kim - One of the best experts on this subject based on the ideXlab platform.
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Quantitative analysis of Melamine in milk powders using near-infrared hyperspectral imaging and band ratio
Journal of Food Engineering, 2016Co-Authors: Min Huang, Stephen R. Delwiche, Jianwei Qin, Changyeun Mo, Carlos Esquerre, Kuanglin Chao, Moon S. Kim, Qibing ZhuAbstract:Since 2008, the detection of the adulterant Melamine (2,4,6-triamino-1,3,5-triazine) in food products has become the subject of research due to several food safety scares. Near-infrared (NIR) hyperspectral imaging offers great potential for food safety and quality research because it combines the features of vibrational spectroscopy and digital imaging. In this study, NIR hyperspectral imaging was investigated for quantitative evaluation of Melamine particles in nonfat and whole milk powders. Melamine was mixed into milk powders in a concentration range of 0.02-1.00% (w/w). A NIR hyperspectral imaging system was used to acquire images (938-1654 nm) of Melamine powder, whole milk powder, nonfat milk powder, and mixtures of Melamine and each of the milk powders. Two optimal bands (1447 nm and 1466 nm) were selected by a linear correlation algorithm with pure milk and pure Melamine. Band ratio (B1447/1466) images coupled with a single threshold were used to create resultant images to visualize identification and distribution of the Melamine adulterant particles in milk powders. The identification results were verified by spectral feature comparison between separated mean spectra of Melamine pixels and milk pixels. Linear correlations (r) were found between the number of pixels identified as containing Melamine and Melamine concentration in nonfat milk and whole milk powders, which were 0.980 and 0.970 or higher, respectively. The study demonstrated that the combination of NIR hyperspectral imaging and simple band ratioing was promising for rapid quantitative analysis of Melamine in milk powders.
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Detection of Melamine in milk powders using near-infrared hyperspectral imaging combined with regression coefficient of partial least square regression model
Talanta, 2016Co-Authors: Jongguk Lim, Jianwei Qin, Changyeun Mo, Giyoung Kim, Insuck Baek, Xiaping Fu, Kuanglin Chao, Moon S. Kim, Byoung Kwan ChoAbstract: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 990-1700 nm 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.
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Detection of Melamine in milk powders based on NIR hyperspectral imaging and spectral similarity analyses
Journal of Food Engineering, 2014Co-Authors: Xiaping Fu, Jianwei Qin, Jongguk Lim, Hoyoung Lee, Ana Garrido-varo, Dolores Pérez-marín, Kuanglin Chao, Moon S. Kim, Yibin YingAbstract:Melamine (2,4,6-triamino-1,3,5-triazine) contamination of food has become an urgent and broadly recognized topic as a result of several food safety scares in the past five years. Hyperspectral imaging techniques that combine the advantages of spectroscopy and imaging have been widely applied for a variety of food quality and safety evaluations. In this study, near-infrared (NIR) hyperspectral imaging technique was investigated to detect low levels (≤1.0%) of Melamine particles in milk powders. Following image preprocessing (normalization and background removal), the spectrum of each pixel in the sample images was compared to the pure Melamine spectrum by spectral similarity measures including spectral angle measure (SAM), spectral correlation measure (SCM), and Euclidian distance measure (EDM). The three similarity analysis methods provided comparable results for Melamine particle detection where imaging allowed visualization of the distribution of Melamine particles within images of milk powder mixture samples that were prepared with various Melamine concentrations. The classification results were verified by spectral feature comparison between separated mean spectra of Melamine pixels and milk powder pixels. The study demonstrated that a combination of NIR hyperspectral imaging technique and spectral similarity analyses was an effective method for Melamine adulteration discrimination in milk powders. The method described in this study can also be applied to other chemicals or multi-chemicals adulterant detection in milk powders.
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Investigation of NIR hyperspectral imaging for discriminating Melamine in milk powder
Sensing for Agriculture and Food Quality and Safety V, 2013Co-Authors: Xiaping Fu, Jianwei Qin, Jongguk Lim, Hoyoung Lee, Kuanglin Chao, Moon S. Kim, Yibin YingAbstract:Melamine (2,4,6-triamino-1,3,5-triazine) contamination of food has become an urgent and broadly recognized issue for which rapid and accurate identification methods are needed by the food industry. In this study, the feasibility and effectiveness of near-infrared (NIR) hyperspectral imaging was investigated for detecting Melamine in milk powder. Hyperspectral NIR images (144 bands spanning from 990 to 1700 nm) were acquired for Petri dishes containing samples of milk powder mixed with Melamine at various concentrations (0.02% to 1%). Spectral bands that showed the most significant differences between pure milk and pure Melamine were selected, and two-band difference analysis was applied to the spectrum of each pixel in the sample images to identify Melamine particles in milk powders. The resultant images effectively allowed visualization of Melamine particle distributions in the samples. The study demonstrated that NIR hyperspectral imaging techniques can qualitatively and quantitatively identify Melamine adulteration in milk powders. ? 2013 SPIE.
Jianwei Qin - One of the best experts on this subject based on the ideXlab platform.
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Quantitative analysis of Melamine in milk powders using near-infrared hyperspectral imaging and band ratio
Journal of Food Engineering, 2016Co-Authors: Min Huang, Stephen R. Delwiche, Jianwei Qin, Changyeun Mo, Carlos Esquerre, Kuanglin Chao, Moon S. Kim, Qibing ZhuAbstract:Since 2008, the detection of the adulterant Melamine (2,4,6-triamino-1,3,5-triazine) in food products has become the subject of research due to several food safety scares. Near-infrared (NIR) hyperspectral imaging offers great potential for food safety and quality research because it combines the features of vibrational spectroscopy and digital imaging. In this study, NIR hyperspectral imaging was investigated for quantitative evaluation of Melamine particles in nonfat and whole milk powders. Melamine was mixed into milk powders in a concentration range of 0.02-1.00% (w/w). A NIR hyperspectral imaging system was used to acquire images (938-1654 nm) of Melamine powder, whole milk powder, nonfat milk powder, and mixtures of Melamine and each of the milk powders. Two optimal bands (1447 nm and 1466 nm) were selected by a linear correlation algorithm with pure milk and pure Melamine. Band ratio (B1447/1466) images coupled with a single threshold were used to create resultant images to visualize identification and distribution of the Melamine adulterant particles in milk powders. The identification results were verified by spectral feature comparison between separated mean spectra of Melamine pixels and milk pixels. Linear correlations (r) were found between the number of pixels identified as containing Melamine and Melamine concentration in nonfat milk and whole milk powders, which were 0.980 and 0.970 or higher, respectively. The study demonstrated that the combination of NIR hyperspectral imaging and simple band ratioing was promising for rapid quantitative analysis of Melamine in milk powders.
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Detection of Melamine in milk powders using near-infrared hyperspectral imaging combined with regression coefficient of partial least square regression model
Talanta, 2016Co-Authors: Jongguk Lim, Jianwei Qin, Changyeun Mo, Giyoung Kim, Insuck Baek, Xiaping Fu, Kuanglin Chao, Moon S. Kim, Byoung Kwan ChoAbstract: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 990-1700 nm 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.
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Detection of Melamine in milk powders based on NIR hyperspectral imaging and spectral similarity analyses
Journal of Food Engineering, 2014Co-Authors: Xiaping Fu, Jianwei Qin, Jongguk Lim, Hoyoung Lee, Ana Garrido-varo, Dolores Pérez-marín, Kuanglin Chao, Moon S. Kim, Yibin YingAbstract:Melamine (2,4,6-triamino-1,3,5-triazine) contamination of food has become an urgent and broadly recognized topic as a result of several food safety scares in the past five years. Hyperspectral imaging techniques that combine the advantages of spectroscopy and imaging have been widely applied for a variety of food quality and safety evaluations. In this study, near-infrared (NIR) hyperspectral imaging technique was investigated to detect low levels (≤1.0%) of Melamine particles in milk powders. Following image preprocessing (normalization and background removal), the spectrum of each pixel in the sample images was compared to the pure Melamine spectrum by spectral similarity measures including spectral angle measure (SAM), spectral correlation measure (SCM), and Euclidian distance measure (EDM). The three similarity analysis methods provided comparable results for Melamine particle detection where imaging allowed visualization of the distribution of Melamine particles within images of milk powder mixture samples that were prepared with various Melamine concentrations. The classification results were verified by spectral feature comparison between separated mean spectra of Melamine pixels and milk powder pixels. The study demonstrated that a combination of NIR hyperspectral imaging technique and spectral similarity analyses was an effective method for Melamine adulteration discrimination in milk powders. The method described in this study can also be applied to other chemicals or multi-chemicals adulterant detection in milk powders.
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Investigation of NIR hyperspectral imaging for discriminating Melamine in milk powder
Sensing for Agriculture and Food Quality and Safety V, 2013Co-Authors: Xiaping Fu, Jianwei Qin, Jongguk Lim, Hoyoung Lee, Kuanglin Chao, Moon S. Kim, Yibin YingAbstract:Melamine (2,4,6-triamino-1,3,5-triazine) contamination of food has become an urgent and broadly recognized issue for which rapid and accurate identification methods are needed by the food industry. In this study, the feasibility and effectiveness of near-infrared (NIR) hyperspectral imaging was investigated for detecting Melamine in milk powder. Hyperspectral NIR images (144 bands spanning from 990 to 1700 nm) were acquired for Petri dishes containing samples of milk powder mixed with Melamine at various concentrations (0.02% to 1%). Spectral bands that showed the most significant differences between pure milk and pure Melamine were selected, and two-band difference analysis was applied to the spectrum of each pixel in the sample images to identify Melamine particles in milk powders. The resultant images effectively allowed visualization of Melamine particle distributions in the samples. The study demonstrated that NIR hyperspectral imaging techniques can qualitatively and quantitatively identify Melamine adulteration in milk powders. ? 2013 SPIE.
Xiaping Fu - One of the best experts on this subject based on the ideXlab platform.
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Detection of Melamine in milk powders using near-infrared hyperspectral imaging combined with regression coefficient of partial least square regression model
Talanta, 2016Co-Authors: Jongguk Lim, Jianwei Qin, Changyeun Mo, Giyoung Kim, Insuck Baek, Xiaping Fu, Kuanglin Chao, Moon S. Kim, Byoung Kwan ChoAbstract: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 990-1700 nm 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.
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Detection of Melamine in milk powders based on NIR hyperspectral imaging and spectral similarity analyses
Journal of Food Engineering, 2014Co-Authors: Xiaping Fu, Jianwei Qin, Jongguk Lim, Hoyoung Lee, Ana Garrido-varo, Dolores Pérez-marín, Kuanglin Chao, Moon S. Kim, Yibin YingAbstract:Melamine (2,4,6-triamino-1,3,5-triazine) contamination of food has become an urgent and broadly recognized topic as a result of several food safety scares in the past five years. Hyperspectral imaging techniques that combine the advantages of spectroscopy and imaging have been widely applied for a variety of food quality and safety evaluations. In this study, near-infrared (NIR) hyperspectral imaging technique was investigated to detect low levels (≤1.0%) of Melamine particles in milk powders. Following image preprocessing (normalization and background removal), the spectrum of each pixel in the sample images was compared to the pure Melamine spectrum by spectral similarity measures including spectral angle measure (SAM), spectral correlation measure (SCM), and Euclidian distance measure (EDM). The three similarity analysis methods provided comparable results for Melamine particle detection where imaging allowed visualization of the distribution of Melamine particles within images of milk powder mixture samples that were prepared with various Melamine concentrations. The classification results were verified by spectral feature comparison between separated mean spectra of Melamine pixels and milk powder pixels. The study demonstrated that a combination of NIR hyperspectral imaging technique and spectral similarity analyses was an effective method for Melamine adulteration discrimination in milk powders. The method described in this study can also be applied to other chemicals or multi-chemicals adulterant detection in milk powders.
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Investigation of NIR hyperspectral imaging for discriminating Melamine in milk powder
Sensing for Agriculture and Food Quality and Safety V, 2013Co-Authors: Xiaping Fu, Jianwei Qin, Jongguk Lim, Hoyoung Lee, Kuanglin Chao, Moon S. Kim, Yibin YingAbstract:Melamine (2,4,6-triamino-1,3,5-triazine) contamination of food has become an urgent and broadly recognized issue for which rapid and accurate identification methods are needed by the food industry. In this study, the feasibility and effectiveness of near-infrared (NIR) hyperspectral imaging was investigated for detecting Melamine in milk powder. Hyperspectral NIR images (144 bands spanning from 990 to 1700 nm) were acquired for Petri dishes containing samples of milk powder mixed with Melamine at various concentrations (0.02% to 1%). Spectral bands that showed the most significant differences between pure milk and pure Melamine were selected, and two-band difference analysis was applied to the spectrum of each pixel in the sample images to identify Melamine particles in milk powders. The resultant images effectively allowed visualization of Melamine particle distributions in the samples. The study demonstrated that NIR hyperspectral imaging techniques can qualitatively and quantitatively identify Melamine adulteration in milk powders. ? 2013 SPIE.
Jongguk Lim - One of the best experts on this subject based on the ideXlab platform.
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Detection of Melamine in milk powders using near-infrared hyperspectral imaging combined with regression coefficient of partial least square regression model
Talanta, 2016Co-Authors: Jongguk Lim, Jianwei Qin, Changyeun Mo, Giyoung Kim, Insuck Baek, Xiaping Fu, Kuanglin Chao, Moon S. Kim, Byoung Kwan ChoAbstract: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 990-1700 nm 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.
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Detection of Melamine in milk powders based on NIR hyperspectral imaging and spectral similarity analyses
Journal of Food Engineering, 2014Co-Authors: Xiaping Fu, Jianwei Qin, Jongguk Lim, Hoyoung Lee, Ana Garrido-varo, Dolores Pérez-marín, Kuanglin Chao, Moon S. Kim, Yibin YingAbstract:Melamine (2,4,6-triamino-1,3,5-triazine) contamination of food has become an urgent and broadly recognized topic as a result of several food safety scares in the past five years. Hyperspectral imaging techniques that combine the advantages of spectroscopy and imaging have been widely applied for a variety of food quality and safety evaluations. In this study, near-infrared (NIR) hyperspectral imaging technique was investigated to detect low levels (≤1.0%) of Melamine particles in milk powders. Following image preprocessing (normalization and background removal), the spectrum of each pixel in the sample images was compared to the pure Melamine spectrum by spectral similarity measures including spectral angle measure (SAM), spectral correlation measure (SCM), and Euclidian distance measure (EDM). The three similarity analysis methods provided comparable results for Melamine particle detection where imaging allowed visualization of the distribution of Melamine particles within images of milk powder mixture samples that were prepared with various Melamine concentrations. The classification results were verified by spectral feature comparison between separated mean spectra of Melamine pixels and milk powder pixels. The study demonstrated that a combination of NIR hyperspectral imaging technique and spectral similarity analyses was an effective method for Melamine adulteration discrimination in milk powders. The method described in this study can also be applied to other chemicals or multi-chemicals adulterant detection in milk powders.
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Investigation of NIR hyperspectral imaging for discriminating Melamine in milk powder
Sensing for Agriculture and Food Quality and Safety V, 2013Co-Authors: Xiaping Fu, Jianwei Qin, Jongguk Lim, Hoyoung Lee, Kuanglin Chao, Moon S. Kim, Yibin YingAbstract:Melamine (2,4,6-triamino-1,3,5-triazine) contamination of food has become an urgent and broadly recognized issue for which rapid and accurate identification methods are needed by the food industry. In this study, the feasibility and effectiveness of near-infrared (NIR) hyperspectral imaging was investigated for detecting Melamine in milk powder. Hyperspectral NIR images (144 bands spanning from 990 to 1700 nm) were acquired for Petri dishes containing samples of milk powder mixed with Melamine at various concentrations (0.02% to 1%). Spectral bands that showed the most significant differences between pure milk and pure Melamine were selected, and two-band difference analysis was applied to the spectrum of each pixel in the sample images to identify Melamine particles in milk powders. The resultant images effectively allowed visualization of Melamine particle distributions in the samples. The study demonstrated that NIR hyperspectral imaging techniques can qualitatively and quantitatively identify Melamine adulteration in milk powders. ? 2013 SPIE.