Authors

Abstract

Flaxseed oil is rich in α-linolenic acid and other nutrients, and the adulteration happens frequently because of its high price. To detect the adulteration of flaxseed oil quickly and accurately, a method was proposed based on weighted reconstructed two-dimensional correlation near-infrared(NIR) spectra. The near-infrared spectra of 79 adulterated flaxseed oil samples (adulterated by rapeseed oil with the doping volume ratio 1%-40%) were measured, and the traditional two-dimensional correlation synchronous spectra were calculated. The two-dimensional correlation synchronous spectra of all samples were decomposed into multiple components of different scales by the bi-dimensional empirical mode decomposition algorithm (BEMD). According to the root mean square error(RMSE) values of the adulteration detection sub-models established by each component, the weights of the corresponding components were calculated, and then the two-dimensional correlation spectra of all samples were reconstructed by accumulating the weighted components. A quantitative analysis model of flaxseed oil adulteration was established based on the weighted reconstructed two-dimensional correlation spectra combined with the N-way partial least square(N-PLS)algorithm. Compared with the traditional two-dimensional correlation spectroscopy, the model built by the weighted reconstructed two-dimensional correlation spectra had better performance with the calibration determination coefficient increased by 6.05%, and the prediction determination coefficient increased by 7.5%. The proposed method could effectively enhance the spectral feature information, reduce the spectral noise interference, and hence provide a new idea for the detection of edible oil adulteration.

Abstract in English

亚麻籽油富含α-亚麻酸等营养成分，市场价格较高，掺假现象频出。为快速、准确检测亚麻籽油掺杂，本文提出一种基于加权重构二维相关近红外光谱的亚麻籽油掺杂检测方法。采集了79个掺杂亚麻籽油样本（以菜籽油为掺杂油,掺杂体积比例1%-40%）的近红外光谱，计算各样本的常规二维相关同步光谱；利用二维经验模态分解算法将各样本的二维相关光谱分解为不同尺度的多个分量，以各分量所建立的掺杂检测子模型的均方根误差值为依据，计算相应尺度分量的加权值，然后通过加权分量叠加重构各样本的二维相关光谱；基于加权重构的二维相关光谱结合多维偏最小二乘方法建立了亚麻籽油掺杂的定量分析模型。结果表明：相比于常规二维相关光谱，加权重构二维相关光谱建立的掺杂检测模型性能更佳，模型的校正决定系数提高了6.05%，预测决定系数提高了7.5%，该方法可有效增强光谱特征信息，降低光谱噪声干扰，为食用油掺假检测提供了一种新的思路。