Volatile Organic Compounds (VOCs) Feature Selection for Human Odor Classification

Sabri, Ahmed; Alfred, Rayner

doi:10.1007/978-981-10-8276-4_24

Cited by 6 publications

(6 citation statements)

References 11 publications

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“…Most of the investigations employed the direct or indirect collection of a sample of human body odors [17] where direct collection involves the interactions of cotton/gauze pads with the skin (from a prime source of their body odor, armpit) [18] after continuous monitoring of volunteers throughout 5 to 7 consecutive days. During this treatment, they are instructed to control their ordinary life activities such as the use of cosmetics, sexual activities to obtain the qualitative form of sample.…”

Section: Experimental Discussionmentioning

confidence: 99%

“…The consequent extraction and identification of compounds can be done through the Solid Phase Micro Extraction (SPME) method that uses either DVB/CAR or PDMS fibers [19]. The thermal sorbent Tenax/carbonograph tube is employed in another study [18] to extract VOCs which is collected through cotton pads by heating the sample up to 90 o C. A Nalophan sampling bag is employed for the collection of samples of headspace SPME, contact SPME, liquid-liquid extraction and the other dynamic sorbent methods-Dynamic headspace sorbent tube sampling (DHS). Out of all these styles, SPME has a potent position in providing fast and efficient sample extraction.…”

Section: Experimental Discussionmentioning

confidence: 99%

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Human Identification with their VOC distribution through CMS – SEN Model

Kanakam

Chakravarthy

2021

Soft Comput

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Smell printing or odor printing is a novel morphological characteristic that an object can be defined by its odor. Human body odor is one such biological trait that yields less error rate of 15% among other biometrics. The human odor printing or smell printing possesses significance against the world towards screening of security checkpoint, searching for survivals under rubbles, investigating criminals, and many more. Cogno-monitoring system (CMS) is a specific prototype to furnish two essential processes -odor analysis and odor encoding through the Sensing-Encoding-Notifying (SEN) model to give the sensitivity and specificity score among the individuals. Human body odor can be interpreted as the alliance of various volatile organic compounds (VOCs) and they are recognized, classified in the encoding process. This article exhibits a detailed analysis of the traditional detection methods including bioanalysis concerning the human body human body odor experimented with 6 people. By applying principal component analysis along with random forest classifier, the VOCs distribution of the individuals is measured. This work calculates that 18.7% of VOCs are having a match with all the individuals which become the plinth for the identification of humans.

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Section: Experimental Discussionmentioning

confidence: 99%

Section: Experimental Discussionmentioning

confidence: 99%

Human Identification with their VOC distribution through CMS – SEN Model

Kanakam

Chakravarthy

2021

Soft Comput

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“…The X axis signifies the number of minutes needed for the sample in the stated VOC detection device, whereas Y axis represents the detected VOC concentration. Table 1 summaries a list of 15 VOCs designated out of 198 gas that was emitted from study subject and will be used as an input data to the artificial neural network NN for the stated human identification target [16].…”

Section: A Gas Chromatograph Mass Spectrometry Test (Gc/ms) and Sweamentioning

confidence: 99%

Odour Based Human Identification and Classification using Neural Networks

2019

IJRTE

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Biometrics permits an individual to be authenticated and identified by computer systems following on a set of verifiable and identifiable data that are precise and unique in nature. This mechanism constitutes a cutting-edge method of identifying an individual since it precisely establishes more explicit and direct connection with humans than mere passwords since biometrics tend to use measurable behavioral and physiological characteristics of human. In this paper, a framework for human identification is proposed distinctively based on specific human odour features. 15 samples of female and male human odour are collected from different age groups, only 15 effective Volatile Organic Compounds (VOCs) are chosen. In this paper, several diverse functions of neural network activation are tested such as Levenberg-Marquardt backpropagation, Gradient descent backpropagation, and Resilient backpropagation. Besides, numerous neural network topologies are tested by means of variety hidden layers and different number of neurons and. Different energy functions were tested TAN- Sigmoid transfer, Linear transfer, and LOG- Sigmoid transfer. Considering the obtained results, employing two hidden layers with more neurons in the hidden layers- to be specific: 15 neurons in every layer- has yielded better accuracy in performance with an accuracy rate of 100%. The unsurpassed framework for algorithm learning to be used for human identification can be backpropagation learning algorithm named the Levenberg-Marquardt. The best function for activation established in this paper is the function of TANSigmoid transfer. The performance accuracy consistency in recognizing human can be enhanced using a big number of study samples.

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“…The X axis signifies time measurement (minutes) needed for tube to stated VOC recognition machine, whereas Y axis refer to detected VOC condensation. Table 1 summaries set of 15 VOCs designated out of 198 gas that was emitted from study subject for using as input data to the classifier for the stated human identification aim [18]. In Table 1 below only 15 gases are picked for human detection and identification procedure based on multiple feature selection algorithms applied [18].…”

Section: A Sampling Procedure: Gc/ms (Gas Chromatograph Mass Spectromentioning

confidence: 99%

“…Table 1 summaries set of 15 VOCs designated out of 198 gas that was emitted from study subject for using as input data to the classifier for the stated human identification aim [18]. In Table 1 below only 15 gases are picked for human detection and identification procedure based on multiple feature selection algorithms applied [18]. Probabilistic methods are a group of classification techniques marked by the fact that their output is a probabilistic measure of similarity between the object under test and some hypothesized class or classes.…”

Section: A Sampling Procedure: Gc/ms (Gas Chromatograph Mass Spectromentioning

confidence: 99%

Effects of Handling Missing Values of VOCS Gases Emitted From Human for Human Detection

Sabri¹,

Alfred²

2019

IJRTE

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The main objective of this paper is to investigate the effects of replacing missing values of Volatile Organic Compounds (VOC) that represent gases emitted from human on the accuracy of classifying individual person. These missing values will be replaced with three possibilities, which include 0, 1, and Random number between 0 and 1. The effects of using these predefined values on the classification accuracy are investigated by conducting experiments that involve applying a list of classification methods to classify 15 humans using human odour. Each person is characterized by their own pre-selected 15 gases emitted from their sweats. In this paper, we also study and determine the minimum number of gases that is required to produce acceptable results to correctly classify an individual person based on the gases emitted from their bodies. Based on the results obtained from the conducted experiments, the maximum and minimum allowable numbers of missing gases in human odour samples in reference to human emitted gases are 4 and 3. The best accuracy result when missing values are introduced in the odour dataset is the ensemble Bagged Trees.

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Volatile Organic Compounds (VOCs) Feature Selection for Human Odor Classification

Cited by 6 publications

References 11 publications

Human Identification with their VOC distribution through CMS – SEN Model

Human Identification with their VOC distribution through CMS – SEN Model

Odour Based Human Identification and Classification using Neural Networks

Effects of Handling Missing Values of VOCS Gases Emitted From Human for Human Detection

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