Rapid identification of rice species by laser-induced breakdown spectroscopy combined with pattern recognition

Ziyi Luo; Linying Zhang; Tianbing Chen; Muhua Liu; Jinyin Chen; Huamao Zhou; Mingyin Yao

doi:10.1364/AO.58.001631

Applied Optics
Vol. 58,
Issue 7,
pp. 1631-1638
(2019)
•https://doi.org/10.1364/AO.58.001631

Rapid identification of rice species by laser-induced breakdown spectroscopy combined with pattern recognition

Ziyi Luo, Linying Zhang, Tianbing Chen, Muhua Liu, Jinyin Chen, Huamao Zhou, and Mingyin Yao

Not Accessible

Your library or personal account may give you access

Get PDF
Email
Share
Get Citation
Copy Citation Text
Ziyi Luo, Linying Zhang, Tianbing Chen, Muhua Liu, Jinyin Chen, Huamao Zhou, and Mingyin Yao, "Rapid identification of rice species by laser-induced breakdown spectroscopy combined with pattern recognition," Appl. Opt. 58, 1631-1638 (2019)

Export Citation
- BibTex
- Endnote (RIS)
- HTML
- Plain Text
Citation alert
Save article

Check for updates

Related Topics
Table of Contents Category
- Lasers, Optical Amplifiers, and Laser Optics
Optics & Photonics Topics
?

The topics in this list come from the Optics and Photonics Topics applied to this article.

About this Article
History
- Original Manuscript: October 22, 2018
- Revised Manuscript: January 9, 2019
- Manuscript Accepted: January 19, 2019
- Published: February 22, 2019

Abstract

Laser-induced breakdown spectroscopy (LIBS) combined with pattern recognition was proposed to discriminate rice species. LIBS spectra in the range of 210–480 nm wavelength from 11 different rice species were collected and preprocessed. Principal component analysis was applied to extract the characteristic variables from LIBS spectral data. Three pattern recognition methods, discriminant analysis, radial basis function neural network, and multi-layer perceptron neural network (MLP) were performed to compare the precision in identifying rice species. The results showed that the performance of the MLP model was better. The average identification rate of rice species reached 100% and 97.9% in the training and test sets, respectively, with MLP. The highest and lowest percentages for correct identification were 100% for early indica rice, Huai rice 5, Yan japonica 6, Lian japonica 8, Xuhan 1, Lvhan 1, Sheng rice 16, Yang japonica 687, and Fenghan 30, and 77.8% for Wuyu japonica rice in test sets. The overall results demonstrate that LIBS combined with MLP could be utilized to rapidly discriminate rice species.

Full Article | PDF Article

More Like This

Laser-induced breakdown spectroscopy assisted chemometric methods for rice geographic origin classification

Ping Yang, Ran Zhou, Wen Zhang, Shisong Tang, Zhongqi Hao, Xiangyou Li, Yongfeng Lu, and Xiaoyan Zeng
Appl. Opt. 57(28) 8297-8302 (2018)

Meat species identification accuracy improvement using sample set portioning based on joint x–y distance and laser-induced breakdown spectroscopy

Lianbo Guo, Weinan Zheng, Feng Chen, Weiliang Wang, Deng Zhang, Zhenlin Hu, and Yanwu Chu
Appl. Opt. 60(20) 5826-5831 (2021)

Discrimination of hazardous bacteria with combination laser-induced breakdown spectroscopy and statistical methods

Yu Zhao, Qianqian Wang, Xutai Cui, Geer Teng, Kai Wei, and Haida Liu
Appl. Opt. 59(5) 1329-1337 (2020)

Previous Article Next Article

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Figures (9)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Tables (2)

You do not have subscription access to this journal. Article tables are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Equations (8)

You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Sample No.	Species	Authorized No.	Species type
1	Early indica rice 788	Wan dao 2008001	Conventional indica rice
2	Wu yu japonica rice 33	E shen dao 2015020	Conventional japonica rice
3	Huai rice 5	Su zhong shen zi no. 358	Conventional japonica rice
4	Yan japonica 6	Su zhong shen zi no. 355	Conventional japonica rice
5	Lian japonica 7	Su shen dao 201008	Conventional japonica rice
6	Xu han 1	Guo shen dao 2007051	Conventional japonica rice
7	Zhen rice 99	Su shen dao 200106	Conventional japonica rice
8	Lv han 1	Guo shen dao 2005053	Conventional indica rice
9	Sheng rice 16	Guo shen dao 2010048	Conventional japonica rice
10	Yang japonica 687	Su zhong shen zi no. 357	Conventional japonica rice
11	Feng zao 30	Su zhong shen zi no. 44	Conventional japonica rice

	RB		DA		MLP
Sample No.	Calibration set/%	Prediction set/%	Original group/%	Cross validation/%	Calibration set/%	Prediction set/%
1	93.1	90.9	92.5	90.0	100.0	100.0
2	86.7	80.0	100.0	100.0	100.0	77.8
3	86.2	100.0	95.0	95.0	100.0	100.0
4	79.3	63.6	97.5	95.0	100.0	100.0
5	56.0	53.3	97.5	97.5	100.0	100.0
6	64.5	66.7	92.5	82.5	100.0	100.0
7	76.7	60.0	95.0	92.5	100.0	91.7
8	95.7	88.2	100.0	100.0	100.0	100.0
9	92.3	100.0	67.5	65.0	100.0	100.0
10	100.0	100.0	90.0	90.0	100.0	100.0
11	59.4	62.5	85.0	85.0	100.0	100.0
average	80.4	79.8	92.0	90.2	100.0	97.9

Sample No.	Species	Authorized No.	Species type
1	Early indica rice 788	Wan dao 2008001	Conventional indica rice
2	Wu yu japonica rice 33	E shen dao 2015020	Conventional japonica rice
3	Huai rice 5	Su zhong shen zi no. 358	Conventional japonica rice
4	Yan japonica 6	Su zhong shen zi no. 355	Conventional japonica rice
5	Lian japonica 7	Su shen dao 201008	Conventional japonica rice
6	Xu han 1	Guo shen dao 2007051	Conventional japonica rice
7	Zhen rice 99	Su shen dao 200106	Conventional japonica rice
8	Lv han 1	Guo shen dao 2005053	Conventional indica rice
9	Sheng rice 16	Guo shen dao 2010048	Conventional japonica rice
10	Yang japonica 687	Su zhong shen zi no. 357	Conventional japonica rice
11	Feng zao 30	Su zhong shen zi no. 44	Conventional japonica rice

	RB		DA		MLP
Sample No.	Calibration set/%	Prediction set/%	Original group/%	Cross validation/%	Calibration set/%	Prediction set/%
1	93.1	90.9	92.5	90.0	100.0	100.0
2	86.7	80.0	100.0	100.0	100.0	77.8
3	86.2	100.0	95.0	95.0	100.0	100.0
4	79.3	63.6	97.5	95.0	100.0	100.0
5	56.0	53.3	97.5	97.5	100.0	100.0
6	64.5	66.7	92.5	82.5	100.0	100.0
7	76.7	60.0	95.0	92.5	100.0	91.7
8	95.7	88.2	100.0	100.0	100.0	100.0
9	92.3	100.0	67.5	65.0	100.0	100.0
10	100.0	100.0	90.0	90.0	100.0	100.0
11	59.4	62.5	85.0	85.0	100.0	100.0
average	80.4	79.8	92.0	90.2	100.0	97.9

Abstract

Cited By

Figures (9)

Tables (2)

Equations (8)

Applied Optics