分子光谱分析技术结合化学计量学已成为一种非常活跃的食用油鉴别方法。 然而, 当不同类型的样本之间的光谱差异极其微小时, 利用传统的分类技术也很难将其分开。 为了完成相似品种食用油的快速识别和分类, 收集了包括芝麻油、 玉米油、 油菜籽油、 调和油、 葵花油、 花生油、 橄榄油七种食用油的衰减全反射红外光谱, 在此基础上, 采用图像识别的方法对七种食用油进行快速分类。 在所提出的图像识别方法中, 首先, 将通过多元散射校正预处理后的红外光谱吸光度矩阵进行自相关运算, 利用等高线原理根据吸光度强度值的不同生成光谱图像, 以扩大的光谱差异并提高光谱可视化。 然后, 根据图像膨胀的原理找到光谱图像的局部特征点, 将其作为图像特征。 最后, 使用BP神经网络对特征点进行训练和分类预测。 为了对比所提出的方法, PCA-BP和KL-BP的方法被用于与图像识别的方法进行比较, 实验结果表明, 图像识别方法的正确识别率为94.4%, 高于PCA-BP的66.7%和KL-BP的83.3%。 所提方法为实现食用油的快速识别和检测提供了一条新的有效途径。

Molecular spectra analysis combined with the chemometrics is becoming a popular method for rapid classification of edible oil. However, when the molecular spectral differences among the different types of samples are tiny, it is usually difficult to identify them with the traditional classification techniques. In this work, a method of molecular spectra analysis based on image recognition for rapid classification of edible oil is proposed. In order to accomplish recognition of different types of edible oil, the attenuated total reflectance infrared spectra of seven types of edible oil are scanned on ATR-FTIR. To enhance the spectral differences among different types of samples and visualize the identification process, the pretreated IR spectra are transformed into two-dimensional spectral image with auto correlation operation. Then, the local extrema are extracted with the method of image expansion and are used as the classification features. The back propagation (BP) neural network is chosen as the classifier to identify the extracted local extrema of the two-dimensional spectral image. Comparative experiments to identify the same samples with the proposed method, PCA-BP and KL-BP have also been done. Comparative experiment results have verified that the classification results with the proposed method (correct classification rate is 94.4%) are obviously better than those with PCA-BP (correct classification rate is 66.7%) and with KL-BP (correct classification rate is 83.3%). The proposed method has provided a new way to classify the edible oil rapidly based on molecular spectra analysis.