Acta Photonica Sinica ›› 2019, Vol. 48 ›› Issue (6): 630003-0630003.doi: 10.3788/gzxb20194806.0630003

• Spectroscopy • Previous Articles    

Discrimination of Adulterated Sesame Oil Using Fusion of Near-mid Infrared Correlation Spectra

ZHANG Jing, SHAN Hui-yong, YANG Ren-jie, JIN Hao, WU Hai-yun, YU Ya-ping   

  1. College of Engineering and Technology, Tianjin Agricultural University, Tianjin 300384, China
  • Received:2019-02-14 Online:2019-06-25 Published:2019-03-28
  • Supported by:

    Tianjin Natural Science Foundation (Nos.18JCYBJC96400, 16JCQNJC08200),Tianjin Science Technology Project (No.17ZXYENC00080),the National Natural Science Foundation of China(Nos.41771357, 31201359, 21607114, 81471698)

Abstract:

To extract the characteristic information of complex adulterated edible oils, a method was established to distinguish adulterated sesame oil. The conventional one-dimensional near-infrared transmission spectra and mid-infrared attenuated total reflectance spectra of 40 pure and adulterated sesame oil samples were collected. The technology of two-trace two-dimensional correlation spectroscopy was used to obtain the synchronous and asynchronous two-dimensional near-infrared and two-dimensional mid-infrared correlation spectra of each sample. The corresponding synchronous-asynchronous two-dimensional near-infrared and two-dimensional mid-infrared correlation spectra were obtained by pretreatment. The multi-way principal component analysis was applied to extract feature information, and the score matrices were fused. The partial least squares discrimination analysis models of pure and adulterated sesame oil were established respectively using the fusion score matrix, the score matrix of synchronous-asynchronous near-infrared correlation spectra, and the score matrix of synchronous-asynchronous mid-infrared correlation spectra. The discriminant accuracies of the three models are 100%, 96.2% and 96.2% respectively. The results show that the proposed method can extract more feature information and provide better analysis results.

Key words: Information fusion, Synchronous-asynchronous two-dimensional correlation spectroscopy, Partial least squares discrimination analysis, Sesame oil, Food safety detection

CLC Number: