Wen Zhen-cai, Sun Tong, Geng Xiang, Liu Mu-hua
Qinghai Entry-Exit Inspection and Quarantine Bureau, Xining 810000, China.
Optics-Electronics Application of Biomaterials Lab, Jiangxi Agricultural University, Nanchang 330045, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2013 Sep;33(9):2354-8.
Camellia oil is a special and high quality edible oil in China, and quality of pressed camellia oils is superior to extracted camellia oils. The objective of the present research was to discriminate pressed and extracted camellia oils by visible/near infrared (Vis/NIR) spectroscopy. The transmission spectra of pressed and extracted camellia oil samples were acquired using a QualitySpec spectrometer in the wavelength range of 350-1800 nm. Uninformative variable elimination (UVE) was used to select informative wavelength variables, and eliminate uninformative wavelength variables, then partial least squares combined with linear discriminant analysis (PLS-LDA) was used to develop classification model. At last, the classification model was used to discriminate 26 samples in the prediction set. The results indicate that UVE-PLS-LDA is an efficient discrimination and classification method, pressed and extracted camellia oils can be discriminated well by the classification model developed by UVE-PLS-LDA, the accurate rate is 100% for both samples in the calibration and prediction sets. So, Vis/NIR spectra combined with UVE-PLS-LDA is an effective method for discriminating pressed and extracted camellia oils.
山茶油是中国一种特殊的优质食用油,压榨山茶油的品质优于浸出山茶油。本研究的目的是利用可见/近红外(Vis/NIR)光谱法鉴别压榨和浸出山茶油。使用QualitySpec光谱仪在350 - 1800 nm波长范围内采集压榨和浸出山茶油样品的透射光谱。采用无信息变量消除法(UVE)选择有信息的波长变量,消除无信息的波长变量,然后使用偏最小二乘法结合线性判别分析(PLS - LDA)建立分类模型。最后,用该分类模型对预测集中的26个样品进行鉴别。结果表明,UVE - PLS - LDA是一种有效的鉴别和分类方法,利用UVE - PLS - LDA建立的分类模型能够很好地鉴别压榨和浸出山茶油,校正集和预测集样品的准确率均为100%。因此,Vis/NIR光谱结合UVE - PLS - LDA是鉴别压榨和浸出山茶油的有效方法。
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