2014•Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIERequires access

Quantitative analysis of melamine by multi-way partial least squares model with two-dimensional near-infrared correlation spectroscopy

Renjie Yang, Rong Liu, Kexin Xu, Yanrong Yang

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Abstract

A new approach for quantitative analysis of melamine in milk was proposed based on two-dimensional (2D) correlation near-infrared spectroscopy and multi-way partial least squares (N-PLS) in this paper. 40 pure milk samples and 40 milk samples adulterated with different contents of melamine were prepared. The near-infrared transmittance spectra of all samples were measured at room temperature. Then 2D NIR-NIR correlation spectroscopy under the perturbation of adulterant concentration was calculated and N-PLS model for the melamine concentration was established with 2D correlation spectra (28x51x51). For the prediction set, the root mean square errors of prediction (RMSEP) for melamine concentration was 0.067 g/L and the coefficient correlation between actual reference values and predicted values was 0.999, which means the model has good predictive ability. For comparison purpose, partial least squares (PLS) model was also built using the conventional one-dimensional near-infrared spectra (28x51), where the RMSEP and the coefficient correlation were 0.079 g/L and 0.998, respectively. The average relative prediction error was 22.9% for N-PLS model; whereas it was 122.4% for PLS model. The N-PLS models yielded relatively low RMSEP and average relative prediction error as compared to PLS model. Therefore, N-PLS method was more robust than PLS method for accurate quantification of the concentration of melamine in milk.

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What this paper is about

A new approach for quantitative analysis of melamine in milk was proposed based on two-dimensional (2D) correlation near-infrared spectroscopy and multi-way partial least squares (N-PLS) in this paper. 40 pure milk samples and 40 milk samples adulterated with different contents of melamine were prepared. The near-infrared transmittance spectra of all samples were measured at room temperature. Then 2D NIR-NIR correlation spectroscopy under the perturbation of adulterant concentration was calculated and N-PLS model for the melamine concentration was established with 2D correlation spectra (28x51x51). For the prediction set, the root mean square errors of prediction (RMSEP) for melamine concentration was 0.067 g/L and the coefficient correlation between actual reference values and predicted values was 0.999, which means the model has good predictive ability. For comparison purpose, partial least squares (PLS) model was also built using the conventional one-dimensional near-infrared spectra (28x51), where the RMSEP and the coefficient correlation were 0.079 g/L and 0.998, respectively. The average relative prediction error was 22.9% for N-PLS model; whereas it was 122.4% for PLS model. The N-PLS models yielded relatively low RMSEP and average relative prediction error as compared to PLS model. Therefore, N-PLS method was more robust than PLS method for accurate quantification of the concentration of melamine in milk.

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Available abstract

A new approach for quantitative analysis of melamine in milk was proposed based on two-dimensional (2D) correlation near-infrared spectroscopy and multi-way partial least squares (N-PLS) in this paper. 40 pure milk samples and 40 milk samples adulterated with different contents of melamine were prepared. The near-infrared transmittance spectra of all samples were measured at room temperature. Then 2D NIR-NIR correlation spectroscopy under the perturbation of adulterant concentration was calculated and N-PLS model for the melamine concentration was established with 2D correlation spectra (28x51x51). For the prediction set, the root mean square errors of prediction (RMSEP) for melamine concentration was 0.067 g/L and the coefficient correlation between actual reference values and predicted values was 0.999, which means the model has good predictive ability. For comparison purpose, partial least squares (PLS) model was also built using the conventional one-dimensional near-infrared spectra (28x51), where the RMSEP and the coefficient correlation were 0.079 g/L and 0.998, respectively. The average relative prediction error was 22.9% for N-PLS model; whereas it was 122.4% for PLS model. The N-PLS models yielded relatively low RMSEP and average relative prediction error as compared to PLS model. Therefore, N-PLS method was more robust than PLS method for accurate quantification of the concentration of melamine in milk.

Key concepts: Partial least squares regression, Melamine, Correlation coefficient, Adulterant, Near-infrared spectroscopy, Analytical Chemistry (journal), Mean squared error, Coefficient of determination

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