Rapid discrimination of Apiaceae plants by electronic nose coupled with multivariate statistical analyses

Lin, Hui; Yan, Yong-Hong; Zhao, Ting; Peng, Liqing; Zou, Hui-Qin; Li, Jian; Xia, Yang; Xiong, Yin; Wang, Meng; H, Wu

doi:10.1016/j.jpba.2013.05.027

Cited by 31 publications

(20 citation statements)

References 14 publications

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“…The acquisition time and the time between injections were 200 s and 400 s, respectively. The response value of each of the 12 sensors for every sample was recorded, and response curves were generated (Lin et al, 2013).…”

Section: Methodsmentioning

confidence: 99%

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Rapid assessment of quality of deer antler slices by using an electronic nose coupled with chemometric analysis

Xu¹,

Liao²,

Ren³

et al. 2014

Revista Brasileira de Farmacognosia

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Electronic noseDeer antler Lu-Rong Quality A B S T R A C T Deer antler is a precious animal-sourced traditional Chinese medicine. We aimed to rapidly assess the quality of deer antler slices by electronic nose so that we can ensure medical safety. In this study, response intensity of the electronic nose was favorably optimized, and samples were well assessed by using an electronic nose based on LDA model. The results obtained herein suggested that electronic nose could be an effective method to rapidly assess the quality of deer antler slices, and could also be an important tool for categorization of complex aroma mixtures for the control of quality of drugs or food.

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Section: Methodsmentioning

confidence: 99%

“…sets of the samples were analyzed using SPSS 19.0 based on cluster analysis (CA), principal component analysis (PCA), linear discriminate analysis (LDA) and artificial neural network (ANN) of radial basis function (Lin et al, 2013).…”

Section: Statistical Processingmentioning

confidence: 99%

Rapid assessment of quality of deer antler slices by using an electronic nose coupled with chemometric analysis

Xu¹,

Liao²,

Ren³

et al. 2014

Revista Brasileira de Farmacognosia

Self Cite

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show abstract

“…Lin [93] studied Apiaceae plants using an E-nose system composed of a MOS array and identified different kinds of Apiaceae plants using multivariate statistical analyses. The results showed that the response values were positively related to the different kinds of Apiaceae plants.…”

Section: E-nose Applications In Identification Of Chmsmentioning

confidence: 99%

Identification of Chinese Herbal Medicines with Electronic Nose Technology: Applications and Challenges

Zhou

Luo

GholamHosseini

et al. 2017

Sensors

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This paper provides a review of the most recent works in machine olfaction as applied to the identification of Chinese Herbal Medicines (CHMs). Due to the wide variety of CHMs, the complexity of growing sources and the diverse specifications of herb components, the quality control of CHMs is a challenging issue. Much research has demonstrated that an electronic nose (E-nose) as an advanced machine olfaction system, can overcome this challenge through identification of the complex odors of CHMs. E-nose technology, with better usability, high sensitivity, real-time detection and non-destructive features has shown better performance in comparison with other analytical techniques such as gas chromatography-mass spectrometry (GC-MS). Although there has been immense development of E-nose techniques in other applications, there are limited reports on the application of E-noses for the quality control of CHMs. The aim of current study is to review practical implementation and advantages of E-noses for robust and effective odor identification of CHMs. It covers the use of E-nose technology to study the effects of growing regions, identification methods, production procedures and storage time on CHMs. Moreover, the challenges and applications of E-nose for CHM identification are investigated. Based on the advancement in E-nose technology, odor may become a new quantitative index for quality control of CHMs and drug discovery. It was also found that more research could be done in the area of odor standardization and odor reproduction for remote sensing.

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“…A multivariate analysis of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are a pattern recognition system which used for the purpose of classification or discrimination [21][22]. PCA is a non-supervised pattern recognition system [23][24][25], while LDA is a supervised pattern recognition system [17,[26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Detection of Taste Change of Bovine and Goat Milk in Room Ambient Using Electronic Tongue

et al. 2017

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Rapid discrimination of Apiaceae plants by electronic nose coupled with multivariate statistical analyses

Cited by 31 publications

References 14 publications

Rapid assessment of quality of deer antler slices by using an electronic nose coupled with chemometric analysis

Rapid assessment of quality of deer antler slices by using an electronic nose coupled with chemometric analysis

Identification of Chinese Herbal Medicines with Electronic Nose Technology: Applications and Challenges

Detection of Taste Change of Bovine and Goat Milk in Room Ambient Using Electronic Tongue

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