食品科学 ›› 2021, Vol. 42 ›› Issue (16): 328-332.doi: 10.7506/spkx1002-6630-20200729-372

• 安全检测 • 上一篇    下一篇

拉曼光谱技术快速检测专用煎炸油极性组分

李睿雯,孙晓荣,刘翠玲,郭泽翰,田密   

  1. (北京工商大学人工智能学院,食品安全大数据技术北京市重点实验室,北京 100048)
  • 发布日期:2021-08-27
  • 基金资助:
    北京市教委科技计划一般项目(KM201810011006);北京市自然科学基金项目(4182017); 全国大学生科学研究与创业行动计划项目(201810011090)

Rapid Detection of Polar Components of Used Frying Oils by Raman Spectroscopy

LI Ruiwen, SUN Xiaorong, LIU Cuiling, GUO Zehan, TIAN Mi   

  1. (Beijing Key Laboratory of Food Safety Big Data Technology, Artificial Intelligence Academy, Beijing Technology and Business University, Beijing 100048, China)
  • Published:2021-08-27

摘要: 为能够快速、无损检测专用煎炸油的极性组分含量,采集不同煎炸时间下煎炸油样本的拉曼光谱图。为建立稳定性高、误差小、精度高的模型,研究不同预处理方法对模型效果的影响,建立相应的偏最小二乘回归模型以选择最优的光谱预处理方法。结果表明:标准正态变换处理后的偏最小二乘模型最优,预测均方根误差(root mean square error of prediction,RMSEP)为1.18,决定系数R2为0.940?4。其次,将标准正态变换处理后的光谱数据分别建立误差反向传播(error back propagation,BP)算法和径向基函数算法神经网络模型,通过比较稳定性以及误差大小,得出采集到的拉曼光谱经过标准正态变换处理后采用BP神经网络建立的模型效果最好,RMSEP为0.032?6,R2为0.972。该方法可以用作专用煎炸油极性组分的快速分析。

关键词: 煎炸油极性组分;拉曼光谱;预处理;偏最小二乘;误差反向传播算法;径向基函数算法

Abstract: In order to detect the content of polar components in used frying oils quickly and non-destructively, we acquired Raman spectra of used frying oil samples collected at different frying times. To create a predictive model for determining the content of polar components in used frying oils with high stability, small errors and high precision, the influence of different spectral preprocessing methods on the performance of predictive models was evaluated, and partial least squares regression analysis regression (PLSR) modelling was carried out to select the optimal spectral preprocessing method. The experimental results showed that the optimal PLSR model was obtained using standard normal transformation with root mean square error of prediction (RMSEP) of 1.18 and correlation coefficient of 0.940 4. Subsequently, the spectral data preprocessed by the standard normal transformation were used to establish a neural network model by error back propagation (BP) and radial basis function training (RBF), separately. The BP neural network model had the best prediction performance with RMSEP of 0.032 6 and correlation coefficient 0.972. The above findings demonstrate that this method can be used for the rapid analysis of polar components of used frying oils.

Key words: polar components of used frying oil; Raman spectroscopy; pretreatment; partial least squares; error back propagation; radial basis function

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