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| Classification of edible vegetable oil based on laser near-infrared spectroscopy combined with extraction of characteristic wavelength |
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| KeyWord:laser near-infrared spectroscopy edible vegetable oil characteristic wavelength extraction support vector machine classification adulteration |
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| Abstract: |
| To classify edible vegetable oil quickly， the method of laser near-infrared(NIR) spectroscopy combined with extration of characteristic wavelength by heavy competitive adaptive weighted sampling (CARS)， successive projections algorithm(SPA) and CARS-SPA was proposed. Spectral data of 127 edible vegetable oil samples were collected by laser NIR spectrometer，and pretreated by standard normal variate transformation(SNV)，standard normal variate transformation and de-trending(SNV-DT)，then the characteristic wavelength was extracted using CARS， SPA and CARS-SPA, and the support vector machine classification (SVC) method was applied to establish qualitative classification correction model of edible vegetable oil. In the end， the model parameter combination (C， g) were optimized by mesh search algorithm. The results showed that the accuracy rates of prediction set of CARS-SVC， SPA-SVC and CARS-SPA-SVC models all reached 96.77% and the prediction effect was satisfying， in which the prediction effect of SNV-DT-SPA-SVC model was the best with the accuracy rate of prediction set 100%. NIR spectroscopy combined with extraction of characteristic wavelength could identify edible vegetable oil accurately and quickly， and they could provide theoretical basis for develop ment of portable field testing equipment. |
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