Classification and Recognition of Adulterated Manuka Honey by Multi-Wavelength Laser-Induced Fluorescence

Si-ying CHEN; Yi-wen JIA; Yu-rong JIANG; He CHEN; Wen-hui YANG; Yu-peng LUO; Zhong-shi LI; Yin-chao ZHANG; Pan GUO

doi:10.3964/j.issn.1000-0593(2022)09-2807-06

Spectroscopy and Spectral Analysis, Volume. 42, Issue 9, 2807(2022)

Classification and Recognition of Adulterated Manuka Honey by Multi-Wavelength Laser-Induced Fluorescence

Si-ying CHEN1,*... Yi-wen JIA1,1;, Yu-rong JIANG1,1; *;, He CHEN1,1;, Wen-hui YANG2,2;, Yu-peng LUO1,1;, Zhong-shi LI1,1;, Yin-chao ZHANG1,1; and Pan GUO1,1; |Show fewer author(s)

Author Affiliations

¹1. School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China

²2. Academy of Military Medical Sciences, Academy of Military Sciences, Beijing 100071, China

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Figures & Tables(6)

Fig. 1. Schematic diagram of laser-induced fluorescence experiment system

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Fig. 2. Data processing flowchart

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Fig. 3. Fluorescence spectra after pretreatment===(a), (b), (c): 266 nm excitation; (d), (e), (f): 355 nm excitation; (g), (h), (i): 405 nm excitation; (j), (k), (l): 450 nm excitation;(a), (d), (g), (j): Sample A; (b), (e), (h), (k): Sample B; (c), (f), (i), (l): Sample C (the same below)

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Fig. 4. Dimensionality reduction diagram of training set data

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Table 1. Recognition rates and standard deviations of three kinds of honey under different excitation wavelengths

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Table 1. Recognition rates and standard deviations of three kinds of honey under different excitation wavelengths

激发波长/nm	分类算法	A		B		C
激发波长/nm	分类算法	识别率/%	标准差	识别率/%	标准差	识别率/%	标准差
266	KNN	98.75	0.008 1	100	0	99.97	0.001 6
355	KNN	92.77	0.027 7	93.62	0.018 0	98.20	0.013 8
405	KNN	92.85	0.025 1	98.88	0.009 5	96.58	0.014 6
450	KNN	57.52	0.044 0	65.48	0.029 2	57.42	0.047 8

Table 2. Recognition rates and standard deviations for different excitation wavelengths and classification algorithms

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Table 2. Recognition rates and standard deviations for different excitation wavelengths and classification algorithms

激发波长 /nm	分类算法	A			B			C
激发波长 /nm	分类算法	识别率/%	差值/%	标准差	识别率/%	差值/%	标准差	识别率/%	差值/%	标准差
266	KNN SVM	98.75 97.18	1.57	0.008 1 0.014 4	100 98.87	1.13	0 0.0105	99.97 98.88	1.09	0.001 6 0.009 8
355	KNN SVM	92.77 92.10	0.67	0.027 7 0.034 0	93.62 93.48	0.14	0.018 0 0.018 3	98.20 96.80	1.40	0.013 8 0.016 4
405	KNN SVM	92.85 92.75	0.10	0.025 1 0.023 3	98.88 97.78	1.10	0.009 5 0.018 6	96.58 95.63	0.95	0.014 6 0.017 7

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Si-ying CHEN, Yi-wen JIA, Yu-rong JIANG, He CHEN, Wen-hui YANG, Yu-peng LUO, Zhong-shi LI, Yin-chao ZHANG, Pan GUO. Classification and Recognition of Adulterated Manuka Honey by Multi-Wavelength Laser-Induced Fluorescence[J]. Spectroscopy and Spectral Analysis, 2022, 42(9): 2807

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Paper Information

Category: Research Articles

Received: Aug. 9, 2021

Accepted: Nov. 16, 2021

Published Online: Nov. 17, 2022

The Author Email: CHEN Si-ying (csy@bit.edu.cn)

DOI:10.3964/j.issn.1000-0593(2022)09-2807-06

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Recognition rates and standard deviations of three kinds of honey under different excitation wavelengths

Table 1. Recognition rates and standard deviations of three kinds of honey under different excitation wavelengths

Table 2. Recognition rates and standard deviations for different excitation wavelengths and classification algorithms

Table 2. Recognition rates and standard deviations for different excitation wavelengths and classification algorithms

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