scholarly journals Selective Detection of Target Volatile Organic Compounds in Contaminated Humid Air Using a Sensor Array with Principal Component Analysis

Sensors ◽  
2017 ◽  
Vol 17 (7) ◽  
pp. 1662 ◽  
Author(s):  
Toshio Itoh Takafumi Akamatsu ◽  
Akihiro Tsuruta ◽  
Woosuck Shin
Keyword(s):  
Principal Component Analysis ◽  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Sensor Array ◽  
Principal Component ◽  
Component Analysis ◽  
Selective Detection ◽  
Humid Air ◽  
Volatile Organic

scholarly journals Effect of Spices on the Formation of VOCs in Roasted Mutton Based on GC-MS and Principal Component Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Jiapei Xi ◽  
Ping Zhan ◽  
Honglei Tian ◽  
Peng Wang
Keyword(s):  
Principal Component Analysis ◽  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Principal Component ◽  
Component Analysis ◽  
Volatile Components ◽  
Volatile Profile ◽  
Asian Countries ◽  
Volatile Organic ◽  
Chinese Prickly Ash

Peppertree prickly ash, Amomum tsao-ko, cumin, and ginger have long been used in Asian countries to modify the flavor and to partially neutralize any unpleasant odors present in roast lamb. The purpose of this study was to evaluate the change in the amount of volatile components present in roast lamb compared to meat added with peppertree prickly ash, Amomum tsao-ko, cumin, and ginger. Principal component analysis was carried out on the 27 initially selected from 88 volatile substances, and 15 substances with a projection of more than 0.25 in the load matrix were used as indicators to study the different contents in roasted mutton and lamb prepared by adding peppertree prickly ash, Amomum tsao-ko, cumin, and ginger. The types of VOCs (volatile organic compounds) detected in roast meat without adding spices were the least. Roast meat with the addition of cumin leads to the strongest content of aldehydes, followed by the addition of Amomum tsao-ko. Additionally, roast meat with the addition of Chinese prickly ash leads to the strongest content of terpenes, followed by the addition of ginger. Moreover, with the addition of spices, the content of volatiles responsible for the presence of a mutton odor (such as hexanal, heptanal, pentanal, (z)-4-decenal, benzaldehyde, p-propyl-anisole, and dimethyl ether) was not significantly decreased, and in fact some volatiles increased in amount such as pentanal, hexanal, octanal, and (z)-4-decenal. In conclusion, the effect of addition of spices on the volatile profile of roasted mutton and lamb can be attributed to the generation of flavor volatiles mainly derived from raw spices’ hot action, with few additional volatiles formed during boiling.

scholarly journals Selective Detection of Target Volatile Organic Compounds in Contaminated Air Using Sensor Array with Machine Learning: Aging Notes and Mold Smells in Simulated Automobile Interior Contaminant Gases

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2687
Author(s):  
Toshio Itoh ◽  
Yutaro Koyama ◽  
Woosuck Shin ◽  
Takafumi Akamatsu ◽  
Akihiro Tsuruta ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Sensor Array ◽  
Selective Detection ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Age Related ◽  
Volatile Organic

We investigated the selective detection of target volatile organic compounds (VOCs) which are age-related body odors (namely, 2-nonenal, pelargonic acid, and diacetyl) and a fungal odor (namely, acetic acid) in the presence of interference VOCs from car interiors (namely, n-decane, and butyl acetate). We used eight semiconductive gas sensors as a sensor array; analyzing their signals using machine learning; principal-component analysis (PCA), and linear-discriminant analysis (LDA) as dimensionality-reduction methods; k-nearest-neighbor (kNN) classification to evaluate the accuracy of target-gas determination; and random forest and ReliefF feature selections to choose appropriate sensors from our sensor array. PCA and LDA scores from the sensor responses to each target gas with contaminant gases were generally within the area of each target gas; hence; discrimination between each target gas was nearly achieved. Random forest and ReliefF efficiently reduced the required number of sensors, and kNN verified the quality of target-gas discrimination by each sensor set.

scholarly journals Influence of age and gender on the profile of exhaled volatile organic compounds analyzed by an electronic nose

2016 ◽  
Vol 42 (2) ◽  
pp. 143-145 ◽  
Author(s):  
Silvano Dragonieri ◽  
Vitaliano Nicola Quaranta ◽  
Pierluigi Carratu ◽  
Teresa Ranieri ◽  
Onofrio Resta
Keyword(s):  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Electronic Nose ◽  
Principal Component ◽  
Exhaled Breath ◽  
Age And Gender ◽  
Volatile Organic ◽  
Older Subjects ◽  
Never Smokers ◽  
And Gender

We aimed to investigate the effects of age and gender on the profile of exhaled volatile organic compounds. We evaluated 68 healthy adult never-smokers, comparing them by age and by gender. Exhaled breath samples were analyzed by an electronic nose (e-nose), resulting in "breathprints". Principal component analysis and canonical discriminant analysis showed that older subjects (≥ 50 years of age) could not be distinguished from younger subjects on the basis of their breathprints, as well as that the breathprints of males could not distinguished from those of females (cross-validated accuracy, 60.3% and 57.4%, respectively).Therefore, age and gender do not seem to affect the overall profile of exhaled volatile organic compounds measured by an e-nose.

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