Application of principal-component analysis on near-infrared spectroscopic data of vegetable oils for their classification |
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Authors: | Tetsuo Sato |
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Affiliation: | (1) Department of Crop Breeding, Kyushu National Agricultural Experiment Station, Ministry of Agriculture, Forestry and Fisheries (MAFF), 861-11 Nishigoshi, Kumamoto-ken, Japan |
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Abstract: | In the near-infrared (NIR) spectra of oil, information about fatty acid composition is concentrated in the range of 1600–2200
nm. Principal-component analysis (PCA) was applied on the standardized full NIR spectral data of this region for vegetable
oils to totally capture the NIR spectral pattern. Nine varieties of vegetable oils (soybean, corn, cottonseed, olive, rice
bran, peanut, rapeseed, sesame and coconut oil) could be successfully classified from their PCA scores. Examining the contribution
of wavelengths to PCA scores showed that wavelengths with a high loading weight were assigned to characteristic absorption
regions that correspond to specific fatty acid moieties. This classification is related to the fatty acid composition of an
oil, and it can be carried out rapidly and easily after eigenvectors were obtained. |
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Keywords: | Classification fatty acid composition near-infrared spectra principal-component analysis spectroscopy vegetable oil |
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