Rapid discrimination of slimming capsules based on illegal additives by electronic nose and flash gas chromatography

2015 ◽  
Vol 38 (4) ◽  
pp. 621-625 ◽  
Author(s):  
Zhenzhen Xia ◽  
Wensheng Cai ◽  
Xueguang Shao
Keyword(s):  
Gas Chromatography ◽  
Electronic Nose ◽  
Rapid Discrimination

scholarly journals The Verification of the Usefulness of Electronic Nose Based on Ultra-Fast Gas Chromatography and Four Different Chemometric Methods for Rapid Analysis of Spirit Beverages

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Paulina Wiśniewska ◽  
Magdalena Śliwińska ◽  
Jacek Namieśnik ◽  
Waldemar Wardencki ◽  
Tomasz Dymerski
Keyword(s):  
Gas Chromatography ◽  
Data Analysis ◽  
Statistical Methods ◽  
Electronic Nose ◽  
Fast Gas Chromatography ◽  
Analysis Methods ◽  
Selection Of Variables ◽  
Fast Gc ◽  
Rapid Discrimination ◽  
Data Analysis Methods

Spirit beverages are a diverse group of foodstuffs. They are very often counterfeited which cause the appearance of low quality products or wrongly labelled products on the market. It is important to find a proper quality control and botanical origin method enabling the same time preliminary check of the composition of investigated samples, which was the main goal of this work. For this purpose, the usefulness of electronic nose based on ultra-fast gas chromatography (fast GC e-nose) was verified. A set of 24 samples of raw spirits, 33 samples of vodkas, and 8 samples of whisky were analysed by fast GC e-nose. Four data analysis methods were used. The PCA was applied for the visualization of dataset, observation of the variation inside groups of samples, and selection of variables for the other three statistical methods. The SQC method was utilized to compare the quality of the samples. Both the DFA and SIMCA data analysis methods were used for discrimination of vodka, whisky, and spirits samples. The fast GC e-nose combined with four statistical methods can be used for rapid discrimination of raw spirits, vodkas, and whisky and in the same for preliminary determination of the composition of investigated samples.

Rapid Measuring Flavor Quality Changes of Frying Rapeseed Oils using a Flash Gas Chromatography Electronic Nose

2019 ◽  
Vol 121 (5) ◽  
pp. 1800260 ◽  
Author(s):  
Lirong Xu ◽  
Xu Li ◽  
Jianhua Huang ◽  
Pan Gao ◽  
Qingzhe Jin ◽  
...  
Keyword(s):  
Gas Chromatography ◽  
Electronic Nose ◽  
Quality Changes ◽  
Flavor Quality

scholarly journals Detecting Pulmonary Oxygen Toxicity Using eNose Technology and Associations between Electronic Nose and Gas Chromatography–Mass Spectrometry Data

Metabolites ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 286
Author(s):  
Thijs T. Wingelaar ◽  
Paul Brinkman ◽  
Rianne de Vries ◽  
Pieter-Jan A.M. van Ooij ◽  
Rigo Hoencamp ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Gas Chromatography ◽  
Electronic Nose ◽  
Oxygen Toxicity ◽  
Partial Least Square ◽  
Least Square ◽  
Sensor Data ◽  
Gas Chromatography Mass Spectrometry ◽  
Exhaled Breath ◽  
Chromatography Mass Spectrometry

Exposure to oxygen under increased atmospheric pressures can induce pulmonary oxygen toxicity (POT). Exhaled breath analysis using gas chromatography–mass spectrometry (GC–MS) has revealed that volatile organic compounds (VOCs) are associated with inflammation and lipoperoxidation after hyperbaric–hyperoxic exposure. Electronic nose (eNose) technology would be more suited for the detection of POT, since it is less time and resource consuming. However, it is unknown whether eNose technology can detect POT and whether eNose sensor data can be associated with VOCs of interest. In this randomized cross-over trial, the exhaled breath from divers who had made two dives of 1 h to 192.5 kPa (a depth of 9 m) with either 100% oxygen or compressed air was analyzed, at several time points, using GC–MS and eNose. We used a partial least square discriminant analysis, eNose discriminated oxygen and air dives at 30 min post dive with an area under the receiver operating characteristics curve of 79.9% (95%CI: 61.1–98.6; p = 0.003). A two-way orthogonal partial least square regression (O2PLS) model analysis revealed an R² of 0.50 between targeted VOCs obtained by GC–MS and eNose sensor data. The contribution of each sensor to the detection of targeted VOCs was also assessed using O2PLS. When all GC–MS fragments were included in the O2PLS model, this resulted in an R² of 0.08. Thus, eNose could detect POT 30 min post dive, and the correlation between targeted VOCs and eNose data could be assessed using O2PLS.

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