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| 基于UVE-GA变量优选的山茶油可见/近红外光谱掺假鉴别 |
| Adulteration discrimination of oil-tea camellia seed oil by Vis/NIR spectra and UVE-GA method |
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| DOI： |
| 中文关键词: 可见/近红外光谱 UVE-GA 掺假鉴别 山茶油 |
| 英文关键词:visible/near infrared UVE-GA adulteration discrimination oil-tea camellia seed oil |
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| 中文摘要: |
| 利用可见/近红外光谱结合无信息变量消除-遗传算法 （UVE-GA）变量选择方法对山茶油和掺杂低比例菜籽油（1%~10%）的山茶油进行鉴别分类，并应用线性判别分析 （LDA）方法建立分类模型。结果表明：UVE-GA是一种有效的波长变量选择方法，能简化分类模型和提高分类模型精度；UVE-GA-LDA分类模型适用于掺杂2%以上菜籽油的山茶油鉴别分类，其分类正确率为100%；对掺杂1%菜籽油的山茶油鉴别分类正确率有待提高，其分类正确率仅为50%。 |
| 英文摘要: |
| Pure oil-tea camellia seed oil and oil-tea camellia seed oils adulterated with 1%-10% of rapeseed oils were discriminated and classified by visible/near infrared (Vis/NIR) spectra combined with uninformative variable elimination-genetic algorithm (UVE-GA), and classification model was developed by linear discriminant analysis （LDA）. The results indicated that UVE-GA was an efficient wave length variable selection method, and the classification model could be simplified and improved by UVE-GA. UVE-GA-LDA classification model was suitable for discriminating oil-tea camellia seed oil adulterated with more than 2% of rapeseed oil, and the correction rate of classification was 100%, while the correction rate of classification for oil-tea camellia seed oil adulterated with 1% of rapeseed oil was only 50%, which need to be improved. |
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