近红外光谱-BP神经网络-PLS法用于橄榄油掺杂分析

橄榄油兼有食用和保健的作用，价值与价格远远高于其他食用油，所以橄榄油中以劣充好的现象十分普遍。可采用近红外光谱法测定初榨橄榄油中掺杂芝麻油、大豆油和葵花籽油的光谱数据，运用改进的BP算法——Levenberg-Marquardt方法，建立PCA-BP人工神经网络方法对其进行定性判别。同时采用偏最小二乘法(PLS)建立了初榨橄榄油中芝麻油、大豆油、葵花籽油含量的近红外光谱定标模型，用交互验证法进行验证。结果表明，BP人工神经网络有很好的定性鉴别能力，PLS建立的芝麻油、大豆油、葵花籽油定标模型的相关系数分别为98.77，99.37，99.44，交叉验证的均方根误差分别为1.3，1.1，1.04。该方法无损、快速、简便，为橄榄油掺杂的检测提供了一种新的途径。

Discriminating and Quantifying Potential Adulteration in Virgin Olive Oil by Near Infrared Spectroscopy with BP-ANN and PLS

In the present paper,the use of near infrared spectroscopy(NIR)as a rapid and cost-effective classification and quantification techniques for the authentication of virgin olive oil were preliminarily investigated.NIR spectra in the range of 12 000-3 700 cm-1 were recorded for pure virgin olive oil and virgin olive oil samples adulterated with varying concentrations of sesame oil,soybean oil and sunflower oil(5%-50% adulterations in the weight of virgin olive oil).The spectral range from 12 000 to 5 390 cm-1...