为打击山茶油掺假，保障消费者的合法利益，利用近红外光谱和子窗口重排分析(SPA)对山茶油的复杂掺假(掺入大豆油、菜籽油、花生油及混合油)进行检测。采集85 个纯山茶油和315 个掺假山茶油样本的近红外光谱，利用SPA 变量选择方法对样本光谱的波长变量进行筛选，再由偏最小二乘-线性判别分析(PLS-LDA)建立山茶油掺假检测模型，并与竞争性自适应重加权算法(CARS)和无信息变量消除(UVE)变量选择方法的结果进行比较。研究结果表明，近红外光谱联合SPA 方法可以用于山茶油的复杂掺假检测，预测集样本的分类错误率、灵敏度及特异性分别为0、1和1。SPA 方法优于UVE 方法，与CARS方法相当，是一种有效的变量选择方法，能简化模型并提高模型的预测精度和稳定性。

In order to crack down on camellia oil adulteration and protect the legitimate interests of consumers, near infrared (NIR) spectroscopy and subwindow permutation analysis (SPA) are used to detect complex adulteration of camellia oil (adulterated with soybean oil, colza oil, peanut oil and mixed oil). The NIR spectra of 85 pure camellia oil samples and 315 adulterated camellia oil samples are acquired, and the SPA method is used to select important wavelength variables. After that, partial least squares-linear discriminant analysis (PLS-LDA) is used to develop calibration models for adulteration detection of camellia oil, and the result is compared to the models developed by PLS-LDA with other selection methods such as competitive adaptive reweighted sampling (CARS) and uninformative variable elimination (UVE). The results indicate that NIR spectroscopy combined with SPA method is feasible for complex adulteration detection of camellia oil, the error rate, sensitivity and specificity of classification in prediction set are 0, 1 and 1, respectively. SPA method is superior to UVE method, and is comparable with CARS method. It is an effective variable selection method, and can simplify model, improve model prediction precision and stability.