Distribution assessment and quantification of counterfeit melamine in powdered milk by NIR imaging methods. (15th June 2015)

• Record Type: Journal Article
• Authors: Huang, Yue; Tian, Kuangda; Min, Shungeng; Xiong, Yanmei; Du, Guorong
• Is Part Of: Food chemistry. Volume 177(2015)
• Page Start: 174
• Page End: 181
• Publication Date: 2015-06-15
• Subjects: Melamine (PubChem CID: 7955); Near infrared imaging -- Powdered milk -- Melamine -- Classical least squares -- Alternative least squares; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2015.01.029
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: Visually identify the forbidden additive in powdered milk by NIR microscopy. Different chemometrics treatments for decomposing multivariate data cube. Fast, simple pretreatment and environmentally friendly analytical process. Low price for analysis compared to wet chemical approaches. Potential for on-line analysis. Abstract: This paper presents a rapid calculation method for the imaging process in the identification and quantification of prohibited additives in milk. Data abstraction methods such as principal component analysis (PCA), classical least squares regression (CLS), and alternative least squares regression (ALS) were used. Different multivariate calculations provided possibilities of quantifying near-infrared (NIR) spectral data cube obtained from the surface of the complex mixture. The results of principal component decomposition confirmed that sample mixture identification is feasible using the PCA–CCI methods. Subsequently, CLSI was used for the direct quantitative analysis of the specific component. Behaving more conveniently than PLS without modeling, CLSI can obtain quantitative information as that melamine generally distribute at the low concentration range of 0–0.5 w/w. Moreover, ALSI can quantify the target component with higher accuracy than CLSI. Standard error of residue to predicted value is 0.0838. Lack of fit is 0.0841. Explanation of variables in the mixture is 99.30%, illustrating that the selective lack of rank is insignificant. Obviously, the most intuitive distribution images are constructed by ALSI among four imaging methods.