Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China.
Intelligent Agriculture Engineering Laboratory of Anhui Province, Hefei 230031, China.
Biosensors (Basel). 2021 Dec 2;11(12):492. doi: 10.3390/bios11120492.
The visible and near-infrared (Vis-NIR) reflectance spectroscopy was utilized for the rapid and nondestructive discrimination of edible oil adulteration. In total, 110 samples of sesame oil and rapeseed oil adulterated with soybean oil in different levels were produced to obtain the reflectance spectra of 350-2500 nm. A set of multivariant methods was applied to identify adulteration types and adulteration rates. In the qualitative analysis of adulteration type, the support vector machine (SVM) method yielded high overall accuracy with multiple spectra pretreatments. In the quantitative analysis of adulteration rate, the random forest (RF) combined with multivariate scattering correction (MSC) achieved the highest identification accuracy of adulteration rate with the full wavelengths of Vis-NIR spectra. The effective wavelengths of the Vis-NIR spectra were screened to improve the robustness of the multivariant methods. The analysis results suggested that the competitive adaptive reweighted sampling (CARS) was helpful for removing the redundant information from the spectral data and improving the prediction accuracy. The PLSR + MSC + CARS model achieved the best prediction performance in the two adulteration cases of sesame oil and rapeseed oil. The coefficient of determination (RPcv2) and the root mean square error (RMSEPcv) of the prediction set were 0.99656 and 0.01832 in sesame oil adulterated with soybean oil, and the RPcv2 and RMSEPcv were 0.99675 and 0.01685 in rapeseed oil adulterated with soybean oil, respectively. The Vis-NIR reflectance spectroscopy with the assistance of multivariant analysis can effectively discriminate the different adulteration rates of edible oils.
可见近红外（Vis-NIR）反射光谱法用于快速无损鉴别食用油掺假。共制备了 110 份不同掺比的芝麻油和菜籽油掺大豆油样品，获得了 350-2500nm 的反射光谱。采用多变量方法对掺假类型和掺假率进行识别。在定性掺假类型分析中，支持向量机（SVM）方法在多种光谱预处理下具有较高的总体准确率。在定量掺假率分析中，随机森林（RF）结合多元散射校正（MSC）在全波长 Vis-NIR 光谱下实现了最高的掺假率识别准确率。筛选 Vis-NIR 光谱的有效波长，以提高多变量方法的稳健性。分析结果表明，竞争自适应重加权采样（CARS）有助于从光谱数据中去除冗余信息并提高预测精度。PLSR+MSC+CARS 模型在芝麻油和菜籽油两种掺假情况下均取得了最佳预测性能。芝麻油掺大豆油的预测集决定系数（RPcv2）和均方根误差（RMSEPcv）分别为 0.99656 和 0.01832，菜籽油掺大豆油的 RPcv2 和 RMSEPcv 分别为 0.99675 和 0.01685。多变量分析辅助的 Vis-NIR 反射光谱可有效鉴别不同掺假率的食用油。
