Wu Xijun, Gao Shibo, Niu Yudong, Zhao Zhilei, Ma Renqi, Xu Baoran, Liu Hailong, Zhang Yungang
Measurement Technology & Instrumentation Key Laboratory of Hebei Province, Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China.
Measurement Technology & Instrumentation Key Laboratory of Hebei Province, Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China.
Food Chem. 2022 Aug 15;385:132655. doi: 10.1016/j.foodchem.2022.132655. Epub 2022 Mar 9.
Blended vegetable oil is a vital product in the vegetable oil market, and quantifying high-value vegetable oil is of great significance to protect the rights and interests of consumers. In this study, we established a one-dimensional convolutional neural network (1D CNN) quantitative identification model based on Raman spectra to identify the amount of olive oil in a corn-olive oil blend. The results show that the 1D CNN model based on 315 extended average Raman spectra can quantitatively identify the content of olive oil, with Rp and RMSEP values of 0.9908 and 0.7183 respectively. Compared with partial least squares regression (PLSR) and support vector regression (SVR), although the index is not optimal, it provides a new analytical method for the quantitative identification of vegetable oil.
调和植物油是植物油市场中的重要产品，对高价值植物油进行定量分析对于保护消费者权益具有重要意义。在本研究中，我们基于拉曼光谱建立了一维卷积神经网络（1D CNN）定量识别模型，以鉴定玉米 - 橄榄油混合物中橄榄油的含量。结果表明，基于315个扩展平均拉曼光谱的1D CNN模型能够定量识别橄榄油含量，Rp和RMSEP值分别为0.9908和0.7183。与偏最小二乘回归（PLSR）和支持向量回归（SVR）相比，尽管指标并非最优，但它为植物油的定量识别提供了一种新的分析方法。
