Identification and Evaluation of Composition in Food Powder Using Point-Scan Raman Spectral Imaging

This study used Raman spectral imaging coupled with self-modeling mixture analysis (SMA) for identification of three components mixed into a complex food powder mixture. Vanillin, melamine, and sugar were mixed together at 10 different concentration level (1% to 10%, w/w) into powdered non-dairy cre...

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Bibliographic Details
Published inApplied sciences Vol. 7; no. 1; p. 1
Main Authors Dhakal, Sagar, Chao, Kuanglin, Qin, Jianwei, Kim, Moon, Peng, Yankun, Chan, Diane
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.01.2017
Subjects
Baby foods
Concentration (composition)
Correlation coefficient
Correlation coefficients
Descriptive labeling
Food composition
food powder
Food safety
food safety and quality
Foods
Image detection
Imaging
Inspection
Lasers
Melamine
Methods
Milk
Milk free
Photonics
Powder
Raman spectral imaging
self modeling mixture analysis
Shape memory alloys
Spectra
spectral information divergence
Vanillin
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Summary:This study used Raman spectral imaging coupled with self-modeling mixture analysis (SMA) for identification of three components mixed into a complex food powder mixture. Vanillin, melamine, and sugar were mixed together at 10 different concentration level (1% to 10%, w/w) into powdered non-dairy creamer. SMA was used to decompose the complex multi-component spectra and extract the pure component spectra and corresponding contribution images. Spectral information divergence (SID) values of the extracted pure component spectra and reference component spectra were computed to identify the components corresponding to the extracted spectra. The contribution images obtained via SMA were used to create Raman chemical images of the mixtures samples, to which threshold values were applied to obtain binary detection images of the components at all concentration levels. The detected numbers of pixels of each component in the binary images was found to be strongly correlated with the actual sample concentrations (correlation coefficient of 0.99 for all components). The results show that this method can be used for simultaneous identification of different components and estimation of their concentrations for authentication or quantitative inspection purposes.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app7010001