Nondestructive Testing of Volatile Oil of Zanthoxylum Bungeanum Based on Hyperspectral Technique and IRIV-FOA-ELM Algorithm

Ranshi Ji; Xiaoyan Chen; Suzhen Liu; Libo Rao; Zhen Wang

doi:10.3788/LOP57.203002

Journals >Laser & Optoelectronics Progress >Volume 57 >Issue 20 >Page 203002 > Article

Laser & Optoelectronics Progress
Vol. 57, Issue 20, 203002 (2020)

Nondestructive Testing of Volatile Oil of Zanthoxylum Bungeanum Based on Hyperspectral Technique and IRIV-FOA-ELM Algorithm

Ranshi Ji¹, Xiaoyan Chen^2、*, Suzhen Liu¹, Libo Rao¹, and Zhen Wang²

Author Affiliations

¹College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, Sichuan 625014, China

²College of Information and Engineering, Sichuan Agricultural University, Yaan, Sichuan 625014, China

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DOI: 10.3788/LOP57.203002 Cite this Article Set citation alerts

Ranshi Ji, Xiaoyan Chen, Suzhen Liu, Libo Rao, Zhen Wang. Nondestructive Testing of Volatile Oil of Zanthoxylum Bungeanum Based on Hyperspectral Technique and IRIV-FOA-ELM Algorithm[J]. Laser & Optoelectronics Progress, 2020, 57(20): 203002 Copy Citation Text

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Fig. 1. Fruit fly optimization algorithm optimized extreme learning machine flowchart

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Fig. 2. Spectra before and after SNVT processing. (a) Before processing (raw spectra); (b) after processing

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Fig. 3. Fitness curve of FOA

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Fig. 4. Comparison of test sets before and after optimization. (a) Before optimization; (b) after optimaization

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Sample	Number ofsamples	Pepper oil content /(mL·g^-1)
Sample	Number ofsamples	Minimum	Maximum	Mean	Standard deviation
Total	150	0.03	0.08	0.056353	0.011984
Training set	120	0.03	0.08	0.056108	0.012253
Prediction set	30	0.031	0.075	0.057333	0.010978

Table 1. Statistics of pepper oil content in training set and prediction set

Model	Calibration set		Prediction set
Model	$R_{C}^{2}$	RMSEC	$R_{P}^{2}$	RMSEP
Primitive variables-PLS	0.5714	0.6922	0.4933	0.7565
All variables-PLS	0.6157	0.6702	0.5614	0.7477
IRIV-PLS	0.6732	0.5944	0.6362	0.6524
All variables-ELM(17)	0.7409	0.7219	0.6952	0.8057
IRIV-ELM(17)	0.8547	0.4311	0.6557	0.6059
All variables-ELM(18)	0.8312	0.5872	0.7522	0.6700
IRIV-ELM(18)	0.8522	0.3475	0.8365	0.5737
All variables-ELM(19)	0.8425	0.4840	0.6862	0.5014
IRIV-ELM(19)	0.8446	0.4121	0.6952	0.6688
All variables-ELM(20)	0.8392	0.6126	0.7494	0.6965
IRIV-ELM(20)	0.8546	0.4673	0.7609	0.5994