Odor Discrimination by Similarity Measures of Abstract Odor Factor Maps from Electronic Noses

Guo, Wei; Haohui, Kong; Jia, Wu; Gan, Feng

doi:10.3390/s18082658

Cited by 2 publications

(4 citation statements)

References 40 publications

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“…In this study, we employed the Statistical Quality Control (SQC) [36] and the Soft Independent Modeling of Class Analogy (SIMCA) algorithms [9,37] to analyze the stability of data within sample groups and between sample groups. We conducted an intra-group stability discriminant analysis for data from stationary 30 min cigarette packaging paper samples (sample A) and finished cigarette packs (sample B).…”

Section: Box Packaging Paper Quality Stability Discriminationmentioning

confidence: 99%

“…This technology excels in analyzing sample odors that have complex media and components, offering advantages such as rapid detection, cost efficiency, a broad detection range, and the elimination of the need for chemical reagents. In odor analysis applications, E-nose technology enables comprehensive, objective, accurate, and swift odor evaluations [7][8][9]. In their research, Luo Dehan et al [10] utilized portable E-nose technology to collect and classify aroma signals from various cigarette brands.…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Detection and Odor Recognition of Cigarette Packaging Paper Boxes Based on a Homemade Electronic Nose

Wang,

Li,

Wang

et al. 2024

Micromachines

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The printing process of box packaging paper can generate volatile organic compounds, resulting in odors that impact product quality and health. An efficient, objective, and cost-effective detection method is urgently needed. We utilized a self-developed electronic nose system to test four different cigarette packaging paper samples. Employing multivariate statistical methods like Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Statistical Quality Control (SQC), and Similarity-based Independent Modeling of Class Analogy (SIMCA), we analyzed and processed the collected data. Comprehensive evaluation and quality control models were constructed to assess sample stability and distinguish odors. Results indicate that our electronic nose system rapidly detects odors and effectively performs quality control. By establishing models for quality stability control, we successfully identified samples with acceptable quality and those with odors. To further validate the system’s performance and extend its applications, we collected two types of cigarette packaging paper samples with odor data. Using data augmentation techniques, we expanded the dataset and achieved an accuracy rate of 0.9938 through classification and discrimination. This highlights the significant potential of our self-developed electronic nose system in recognizing cigarette packaging paper odors and odorous samples.

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Section: Box Packaging Paper Quality Stability Discriminationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent Detection and Odor Recognition of Cigarette Packaging Paper Boxes Based on a Homemade Electronic Nose

Wang,

Li,

Wang

et al. 2024

Micromachines

View full text Add to dashboard Cite

show abstract

“…Many researchers have discussed e-nose for various problems, from detecting food quality [1], health interests like diabetes detection [2], detecting adulteration and quality control of olive oil [3], to detecting halal food by utilising pork and beef data [4]. The electronic nose has been used widely for many purposes because of its neutrality, stability, and durability than traditional odour analysis [5]. Overall, the electronic nose is a well-built system to perceive odour because of its sensitivity and quick data process.…”

Section: Introductionmentioning

confidence: 99%

“…It has crosssensitivity [6] and low selectivity [7]. Its weakness makes the electronic nose have a limitation to discrimination [5]. Besides discrimination objects, the electronic nose has a limitation to distinguish similar odours.…”

Section: Introductionmentioning

confidence: 99%

Electronic Nose Signals for Analysing Similarity of Male and Female Axillary Odour to Food Material Aroma

2022

IJIES

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Human axillary odour is a unique discussion because it stores various useful information, including health, food quality analysis, and the similarity to the smell of the food eaten which can be done using an electronic nose (enose). However, rarely for research use an e-nose to determine the similarity of human axillary odour to food material odours. This research aimed to determine the similarity between male and female odours based on food material odours, such as garlic, shallot, and red chilli using an e-nose. We propose a method using an electronic nose with 8 Taguchi gas sensor (TGS) and 1 digital & humidity (DHT) sensor, then processing the resulting data using the fast fourier transform (FFT) smoothing method. Sensor data that have anomalies are removed by the quantile method. The standardization process is carried out to the signal data and feature extraction is processed with mean, standard deviation, and minimum value. This study found that the proposed method can produce a fairly good cluster of aroma food materials, faithfully getting the highest accuracy of 96.67 % using support vector classifier (SVC) and k-nearest neighbour (KNN) with each best parameter. We also found that the smell of human armpit sweat generally has a lot of similarity with food materials, specifically that male has the highest similarity to the shallot, while female has the highest level of similarity to garlic. In contrast, red chilli has the lowest level of similarity with human armpit odour.

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Odor Discrimination by Similarity Measures of Abstract Odor Factor Maps from Electronic Noses

Cited by 2 publications

References 40 publications

Intelligent Detection and Odor Recognition of Cigarette Packaging Paper Boxes Based on a Homemade Electronic Nose

Intelligent Detection and Odor Recognition of Cigarette Packaging Paper Boxes Based on a Homemade Electronic Nose

Electronic Nose Signals for Analysing Similarity of Male and Female Axillary Odour to Food Material Aroma

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