Identification of soy sauce using high-field asymmetric waveform ion mobility spectrometry combined with machine learning

doi:10.1016/j.snb.2022.131966

	Identification of soy sauce using high-field asymmetric waveform ion mobility spectrometry combined with machine learning
	Jin, Jiao 1,3; Liu, Youjiang 3; Li, Shan 1,3; Hu, Jun 1,3; Liu, Shaomin 2,3; Chen, Chilai 3
刊名	SENSORS AND ACTUATORS B-CHEMICAL
	2022-08-15
卷号	365
关键词	Soy sauce FAIMS Volatile organic compounds Classification Machine learning
DOI	10.1016/j.snb.2022.131966
通讯作者	Chen, Chilai(chlchen@iim.ac.cn)
英文摘要	Soy sauce, an important condiment, varies greatly in the brand, geographical distribution, and production processes. We investigated the potential of volatile organic compounds (VOCs) serving as an indicator of soy sauce quality to detect three regions and two production technologies of Chinese soy sauce. An analytical method named high-field asymmetric waveform ion mobility spectrometry (FAIMS) was utilized for acquiring sample data. Wavelet packet decomposition (WPD) and principal component analysis (PCA) were used to extract the features of FAIMS data. 4 machine learning models were trained using these features, and the optimal parameters were obtained by a grid search. The scatter plots of the optimal two features we selected showed that the different regions and production technologies of soy sauce had obvious clustering trends. For the identification of different regions and production technologies, the training score, test score, and average cross-validation score of the optimal model were all 100%. Furthermore, the learning curves indicated that the optimal model obtained good performance and had low prediction errors. It was concluded that FAIMS combined with a suitable machine learning algorithm can successfully classify different regions and production technologies of Chinese soy sauce.
资助项目	National Natural Science Foundation of China[61871367] ; Youth Innovation Promotion Association of the Chinese Academy of Sciences[2013213] ; National Natural Science Foundation for Young Scientists of China[41805017]
WOS关键词	VOLATILE COMPOUNDS ; GEOGRAPHIC REGION ; UV-FAIMS ; CLASSIFICATION ; FERMENTATION
WOS研究方向	Chemistry ; Electrochemistry ; Instruments & Instrumentation
语种	英语
出版者	ELSEVIER SCIENCE SA
WOS记录号	WOS:000798279900002
资助机构	National Natural Science Foundation of China ; Youth Innovation Promotion Association of the Chinese Academy of Sciences ; National Natural Science Foundation for Young Scientists of China
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/131077]
专题	中国科学院合肥物质科学研究院
通讯作者	Chen, Chilai
作者单位	1.Univ Sci & Technol China, Hefei 230026, Peoples R China 2.Guangxi Univ, Sch Elect Engn, Guangxi Key Lab Intelligent Control & Maintenance, Nanning 530004, Peoples R China 3.Chinese Acad Sci, Hefei Inst Phys Sci, Hefei 230031, Peoples R China
推荐引用方式 GB/T 7714	Jin, Jiao,Liu, Youjiang,Li, Shan,et al. Identification of soy sauce using high-field asymmetric waveform ion mobility spectrometry combined with machine learning[J]. SENSORS AND ACTUATORS B-CHEMICAL,2022,365.
APA	Jin, Jiao,Liu, Youjiang,Li, Shan,Hu, Jun,Liu, Shaomin,&Chen, Chilai.(2022).Identification of soy sauce using high-field asymmetric waveform ion mobility spectrometry combined with machine learning.SENSORS AND ACTUATORS B-CHEMICAL,365.
MLA	Jin, Jiao,et al."Identification of soy sauce using high-field asymmetric waveform ion mobility spectrometry combined with machine learning".SENSORS AND ACTUATORS B-CHEMICAL 365(2022).