基于橄榄油的近红外光谱数据, 用判别分析(Discriminant analysis)方法把20个样品成功地分为特级初榨橄榄油和普通橄榄油两类, 正确率为100％。 同时测定了纯橄榄油中分别掺入菜籽油、 玉米油、 花生油、 山茶油、 葵花籽油、 罂粟油的混合油的近红外光谱, 掺杂油体积百分数范围为0～100%。 选择最佳的光谱波段组合用偏最小二乘(PLS)法分别建立定量分析模型, 预测相对误差范围在－5.67%～5.61%之间。 研究结果表明, 基于化学计量学方法和近红外光谱数据可为橄榄油的品质鉴定和掺杂量检测提供了一种简便、 快捷、 准确的方法。

Discriminant analysis was used to classify 20 olive oil samples based on their near-infrared (NIR) spectra. The samples were successfully classified into two categories which are consistent with extra virgin olive oil and ordinary olive oil defined in the products. The NIR spectra of olive-oil mixtures containing colza oil, corn oil, peanut oil, camellia oil, sunflower oil, and poppy seed oil were collected, respectively. The volume percent of adulterants ranged from 0 to 100%. The best spectrum bands for analysis were selected before developing partial least-squares (PLS) calibration models. The relative errors of prediction ranged from －5.67% to 5.61%. Results showed that the method combined with chemometrics methods and near-infrared spectrometry is simple, fast and credible for qualitative and quantitative analyses of olive oil samples.