A novel method for rapid discrimination of bulbus of Fritillaria by using electronic nose and electronic tongue technology

2015 ◽  
Vol 7 (3) ◽  
pp. 943-952 ◽  
Author(s):  
Shilong Yang ◽  
Shaopeng Xie ◽  
Min Xu ◽  
Chao Zhang ◽  
Na Wu ◽  
...  
Keyword(s):  
Electronic Nose ◽  
Electronic Tongue ◽  
Novel Method ◽  
Rapid Discrimination

E-nose and E-tongue coupled with the chemometrics were employed to discriminate the bulbus of fritillaria in the form of powder.

scholarly journals A Novel Method for the Discrimination of Semen Arecae and Its Processed Products by Using Computer Vision, Electronic Nose, and Electronic Tongue

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Min Xu ◽  
Shi-Long Yang ◽  
Wei Peng ◽  
Yu-Jie Liu ◽  
Da-Shuai Xie ◽  
...  
Keyword(s):  
Computer Vision ◽  
Electronic Nose ◽  
Electronic Tongue ◽  
Principal Component ◽  
Accurate Method ◽  
Color Parameters ◽  
Hydroxymethyl Furfural ◽  
Novel Method ◽  
Sensory Responses ◽  
Processed Products

Areca nut, commonly known locally as Semen Arecae (SA) in China, has been used as an important Chinese herbal medicine for thousands of years. The raw SA (RAW) is commonly processed by stir-baking to yellow (SBY), stir-baking to dark brown (SBD), and stir-baking to carbon dark (SBC) for different clinical uses. In our present investigation, intelligent sensory technologies consisting of computer vision (CV), electronic nose (E-nose), and electronic tongue (E-tongue) were employed in order to develop a novel and accurate method for discrimination of SA and its processed products. Firstly, the color parameters and electronic sensory responses of E-nose and E-tongue of the samples were determined, respectively. Then, indicative components including 5-hydroxymethyl furfural (5-HMF) and arecoline (ARE) were determined by HPLC. Finally, principal component analysis (PCA) and discriminant factor analysis (DFA) were performed. The results demonstrated that these three instruments can effectively discriminate SA and its processed products. 5-HMF and ARE can reflect the stir-baking degree of SA. Interestingly, the two components showed close correlations to the color parameters and sensory responses of E-nose and E-tongue. In conclusion, this novel method based on CV, E-nose, and E-tongue can be successfully used to discriminate SA and its processed products.

Electronic Nose and Electronic Tongue

2009 ◽  
pp. 105-126 ◽  
Author(s):  
Corrado Di Natale ◽  
Gudrun lafsdttir
Keyword(s):  
Electronic Nose ◽  
Electronic Tongue

DIMENSIONALITY REDUCTION FOR SENSORY DATASETS BASED ON MASTER–SLAVE SYNCHRONIZATION OF LORENZ SYSTEM

2013 ◽  
Vol 23 (05) ◽  
pp. 1330013 ◽  
Author(s):  
REZA GHAFFARI ◽  
IOAN GROSU ◽  
DACIANA ILIESCU ◽  
EVOR HINES ◽  
MARK LEESON
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Electronic Nose ◽  
Lorenz System ◽  
Nearest Neighbor ◽  
Performance Testing ◽  
K Nearest Neighbor ◽  
Classification Rate ◽  
Benchmark Datasets ◽  
Novel Method

In this study, we propose a novel method for reducing the attributes of sensory datasets using Master–Slave Synchronization of chaotic Lorenz Systems (DPSMS). As part of the performance testing, three benchmark datasets and one Electronic Nose (EN) sensory dataset with 3 to 13 attributes were presented to our algorithm to be projected into two attributes. The DPSMS-processed datasets were then used as input vector to four artificial intelligence classifiers, namely Feed-Forward Artificial Neural Networks (FFANN), Multilayer Perceptron (MLP), Decision Tree (DT) and K-Nearest Neighbor (KNN). The performance of the classifiers was then evaluated using the original and reduced datasets. Classification rate of 94.5%, 89%, 94.5% and 82% were achieved when reduced Fishers iris, crab gender, breast cancer and electronic nose test datasets were presented to the above classifiers.

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