Detection of Umami Substances and Umami Intensity Based on Near-Infrared Spectroscopy

Jian Hu; Yaoze Feng; Yijian Wang; Jie Huang; Guifeng Jia; Ming Zhu

doi:10.3788/AOS202242.0130002

Acta Optica Sinica, Volume. 42, Issue 1, 0130002(2022)

Detection of Umami Substances and Umami Intensity Based on Near-Infrared Spectroscopy

Jian Hu¹, Yaoze Feng^1,2,3、*, Yijian Wang¹, Jie Huang¹, Guifeng Jia^1,2, and Ming Zhu^1,2

Author Affiliations

¹College of Engineering, Huazhong Agricultural University, Wuhan, Hubei 430070, China

²Key Laboratory of Agricultural Equipment in Mid-Lower Yangtze River, Ministry of Agriculture and Rural Affairs, Wuhan, Hubei 430070, China

³Interdisciplinary Sciences Research Institute, Huazhong Agricultural University, Wuhan, Hubei 430070, China

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Figures & Tables(8)

Fig. 1. Flowchart of CMIC algorithm

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Fig. 2. Original spectrogram of sample

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Fig. 3. Performance analysis results of MSG concentration extracted by CMIC algorithm. (a) Variation trend under different window lengths; (b) variation trend of combined bands; (c) variation trend of number of resevered sub-intervals; (d) feature variables extracted by CMIC algorithm

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Fig. 4. Performance analysis results of IMP concentration extracted by CMIC algorithm. (a) Scatter diagram for verification of IMP concentration detection in mixed solution; (b) feature variables extracted by CMIC algorithm

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Fig. 5. Performance analysis results of extracting EUC values by CMIC algorithm. (a) Scatter diagram for verification of EUC value in mixed solution; (b) feature variables extracted by CMIC algorithm

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Table 1. Comparison of different MSG concentration detection models

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Table 1. Comparison of different MSG concentration detection models

Algorithm	Number of variables	PC	$R_{c}^{2}$	$R_{p}^{2}$	RMSEc /(g·dL^-1)	RMSEp /(g·dL^-1)
Original	3112	20	0.7396	0.6113	0.0173	0.0204
iPLS	183	7	0.8608	0.8050	0.0127	0.0145
UVE	1767	20	0.6885	0.5271	0.0190	0.0225
CARS	73	14	0.9180	0.8793	0.0097	0.0114
CMIC	414	14	0.9096	0.8886	0.0102	0.0109

Table 2. Comparison of different IMP concentration detection models

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Table 2. Comparison of different IMP concentration detection models

Algorithm	Number of variables	PC	$R_{c}^{2}$	$R_{p}^{2}$	RMSEc /(g·dL^-1)	RMSEp /(g·dL^-1)
Original	3112	16	0.7834	0.7272	0.1087	0.1302
iPLS	195	7	0.9006	0.8788	0.0737	0.0868
UVE	1368	19	0.9398	0.9087	0.0573	0.0753
CARS	85	20	0.9446	0.9103	0.0550	0.0747
CMIC	602	13	0.9239	0.9182	0.0644	0.0713

Table 3. Comparison of different EUC value detection models

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Table 3. Comparison of different EUC value detection models

Algorithm	Number of variables	PC	$R_{c}^{2}$	$R_{p}^{2}$	RMSEc /(g·dL^-1)	RMSEp /(g·dL^-1)
Original	3112	12	0.7534	0.7596	3.4955	3.6533
iPLS	283	10	0.8382	0.7840	2.8319	3.4625
UVE	1476	14	0.7948	0.7930	3.1889	3.3899
CARS	73	14	0.8585	0.8022	2.6476	3.3137
CMIC	417	14	0.8393	0.8097	2.8221	3.2506

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Jian Hu, Yaoze Feng, Yijian Wang, Jie Huang, Guifeng Jia, Ming Zhu. Detection of Umami Substances and Umami Intensity Based on Near-Infrared Spectroscopy[J]. Acta Optica Sinica, 2022, 42(1): 0130002

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Paper Information

Category: Spectroscopy

Received: May. 27, 2021

Accepted: Jul. 19, 2021

Published Online: Dec. 22, 2021

The Author Email: Feng Yaoze (yaoze.feng@mail.hzau.edu.cn)

DOI:10.3788/AOS202242.0130002

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology