Recognition of edible oil by using BP neural network and laser induced fluorescence spectrum

Tao-tao Mu; Si-ying Chen; Yin-chao Zhang; Pan Guo; He Chen; Hong-yan Zhang; Xiao-hua Liu; Yuan Wang; Zhi-chao Bu

doi:10.1117/12.2034972

19 September 2013 Recognition of edible oil by using BP neural network and laser induced fluorescence spectrum

Tao-tao Mu, Si-ying Chen, Yin-chao Zhang, Pan Guo, He Chen, Hong-yan Zhang, Xiao-hua Liu, Yuan Wang, Zhi-chao Bu

Proceedings Volume 8905, International Symposium on Photoelectronic Detection and Imaging 2013: Laser Sensing and Imaging and Applications; 89052H (2013) https://doi.org/10.1117/12.2034972
Event: ISPDI 2013 - Fifth International Symposium on Photoelectronic Detection and Imaging, 2013, Beijing, China

Abstract

In order to accomplish recognition of the different edible oil we set up a laser induced fluorescence spectrum system in the laboratory based on Laser induced fluorescence spectrum technology, and then collect the fluorescence spectrum of different edible oil by using that system. Based on this, we set up a fluorescence spectrum database of different cooking oil. It is clear that there are three main peak position of different edible oil from fluorescence spectrum chart. Although the peak positions of all cooking oil were almost the same, the relative intensity of different edible oils was totally different. So it could easily accomplish that oil recognition could take advantage of the difference of relative intensity. Feature invariants were extracted from the spectrum data, which were chosen from the fluorescence spectrum database randomly, before distinguishing different cooking oil. Then back propagation (BP) neural network was established and trained by the chosen data from the spectrum database. On that basis real experiment data was identified by BP neural network. It was found that the overall recognition rate could reach as high as 83.2%. Experiments showed that the laser induced fluorescence spectrum of different cooking oil was very different from each other, which could be used to accomplish the oil recognition. Laser induced fluorescence spectrum technology, combined BP neural network，was fast, high sensitivity, non-contact, and high recognition rate. It could become a new technique to accomplish the edible oil recognition and quality detection.

Citation Download Citation

Tao-tao Mu, Si-ying Chen, Yin-chao Zhang, Pan Guo, He Chen, Hong-yan Zhang, Xiao-hua Liu, Yuan Wang, and Zhi-chao Bu "Recognition of edible oil by using BP neural network and laser induced fluorescence spectrum", Proc. SPIE 8905, International Symposium on Photoelectronic Detection and Imaging 2013: Laser Sensing and Imaging and Applications, 89052H (19 September 2013); https://doi.org/10.1117/12.2034972

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KEYWORDS

Luminescence

Laser induced fluorescence

Neural networks

Principal component analysis

Databases

Fluorescence spectroscopy

Signal to noise ratio

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