|
| Rapid adulteration detection of peanut oil using three-dimensional fluorescence spectroscopy |
| |
| DOI： |
| KeyWord:peanut oil three-dimensional fluorescence spectroscopy Zernike moment Xgboost algorithm generalized regression neural network algorithm |
| FundProject:西北农林科技大学大学生创新创业训练项目（S201910712199） |
|
| Hits: 1794 |
| Download times: 719 |
| Abstract: |
| The adulteration detection method of peanut oil was established based on three-dimensional fluorescence spectroscopy. Using pure peanut oil and peanut oil aduterated with 4 kinds of common edible vegetable oils as the research objects, their three dimensional fluorescence spectra were processed into grayscale, and the feature information of the three dimensional spectrum grayscale was extracted directly using Zernike moment image, and then these feature information data was used to establish qualitative and quantitative discriminant models by Xgboost algorithm and generalized regression neural network (GRNN) algorithm respectively, and the models were verified. The results showed that Xgboost algorithm could identify adulterated peanut oil effectively and analyze its adulterated components accurately. GRNN algorithm could quantitatively predict the adulteration content of peanut oil, and the limits of detection were respectively 0.2% for soybean oil, 1.5% for rapeseed oil, 1.0% for corn oil and 0.5% for sunflower seed oil. In conclusion, the proposed method could realize the qualitative and quantitative analysis of adulteration of peanut oil, and had the advantages of fastness, simpleness and high sensitivity. |
| View full text View/Add Comment Download reader |
| Close |
|
|
|
