Determination of Rice Storage Time with Colorimetric Sensor Array
| Autor: | Jiewen Zhao, Hong-juan Jin, Hao Lin, Binbin Guan |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Analytical chemistry
Colorimetric sensor array 01 natural sciences Applied Microbiology and Biotechnology Hexanal Analytical Chemistry chemistry.chemical_compound 0404 agricultural biotechnology Sensor array Safety Risk Reliability and Quality Difference map Aroma Chromatography biology Chemistry 010401 analytical chemistry food and beverages 04 agricultural and veterinary sciences biology.organism_classification Linear discriminant analysis 040401 food science 0104 chemical sciences Personal computer Gas chromatography–mass spectrometry Safety Research Food Science |
| Zdroj: | Food Analytical Methods. 10:1054-1062 |
| ISSN: | 1936-976X 1936-9751 |
| Popis: | Aroma of rice greatly affects palatability as well as consumer acceptability and is one of the main factors of rice quality. In this work, gas chromatography mass spectrometry (GC-MS) was used to investigate the volatile organic compounds (VOC) of rice samples with different storage time. It was found that the compounds of benzaldehyde, 2-fluoro-5-methylaniline increased remarkably during storage, while hexanal, octaethylene glycol and monododecyl ether continuously decreased. And then, colorimetric sensor array composed of 12 sensitive dyes were used for rice sample discrimination. A CCD camera was employed to capture the images of colorimetric sensor array, and the difference map of sensor array before and after exposure VOC was obtained. The red (R), green (G), and blue (B) components images were extracted from the difference map. Moreover, the images were further processed and turned into digital data for subsequent analysis. Recently harvested rice and that stored for 6 and 12 months samples were distinguished with three dimensional spaces personal computer assistant cluster trend. Linear discriminant analysis (LDA) model based on PCA scores was used to discriminate the storage time of rice samples, and the result indicates 85 % of samples were correctly identified. |
| Databáze: | OpenAIRE |
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