scholarly journals Identification of Four Wood Species by an Electronic Nose and by LIBS

2012 ◽  
Vol 2012 ◽  
pp. 1-5 ◽  
Author(s):  
Juliana R. Cordeiro ◽  
Maria I. V. Martinez ◽  
Rosamaria W. C. Li ◽  
Anderson P. Cardoso ◽  
Lidiane C. Nunes ◽  
...  
Keyword(s):  
Electronic Nose ◽  
Gas Sensors ◽  
Wood Species ◽  
Chemical Elements ◽  
Conductive Polymer ◽  
Principal Component ◽  
Volatile Organic ◽  
Elemental Determination ◽  
Laser Induced Breakdown ◽  
Leave One Out

This paper presents two complementary methods capable of identifying four wood species (Cedrela fissilis, Ocotea porosa, Hymenolobium petraeum,andAspidosperma subincanum) both by their volatile organic compounds and by the presence of 10 chemical elements: Al, B, Ca, Mg, Zn, Cu, Mn, Fe, Na, and Si. The volatile compounds were detected by an electronic nose formed by an array of three different conductive polymer gas sensors. The elemental determination was made by laser-induced breakdown spectrometry (LIBS). The emissions measured were treated by principal component analysis (PCA). Leave-one-out analysis showed a rate of hits of 100%.

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.

scholarly journals Discrimination of Chinese Liquors Based on Electronic Nose and Fuzzy Discriminant Principal Component Analysis

Foods ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 38 ◽  
Author(s):  
Xiaohong Wu ◽  
Jin Zhu ◽  
Bin Wu ◽  
Chao Zhao ◽  
Jun Sun ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Electronic Nose ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Component Analysis ◽  
K Nearest Neighbor ◽  
Knn Classifier ◽  
Extraction Algorithm ◽  
Leave One Out

The detection of liquor quality is an important process in the liquor industry, and the quality of Chinese liquors is partly determined by the aromas of the liquors. The electronic nose (e-nose) refers to an artificial olfactory technology. The e-nose system can quickly detect different types of Chinese liquors according to their aromas. In this study, an e-nose system was designed to identify six types of Chinese liquors, and a novel feature extraction algorithm, called fuzzy discriminant principal component analysis (FDPCA), was developed for feature extraction from e-nose signals by combining discriminant principal component analysis (DPCA) and fuzzy set theory. In addition, principal component analysis (PCA), DPCA, K-nearest neighbor (KNN) classifier, leave-one-out (LOO) strategy and k-fold cross-validation (k = 5, 10, 20, 25) were employed in the e-nose system. The maximum classification accuracy of feature extraction for Chinese liquors was 98.378% using FDPCA, showing this algorithm to be extremely effective. The experimental results indicate that an e-nose system coupled with FDPCA is a feasible method for classifying Chinese liquors.

scholarly journals Electronic nose with polymer-composite sensors for monitoring fungal deterioration of stored rapeseed

2017 ◽  
Vol 31 (3) ◽  
pp. 317-325 ◽  
Author(s):  
Marek Gancarz ◽  
Jolanta Wawrzyniak ◽  
Marzena Gawrysiak-Witulska ◽  
Dariusz Wiącek ◽  
Agnieszka Nawrocka ◽  
...  
Keyword(s):  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Electronic Nose ◽  
Principal Component ◽  
Colony Forming Units ◽  
Volatile Organic ◽  
Association Time ◽  
Infected Material ◽  
Fungal Deterioration

AbstractInvestigations were performed to examine the possibility of using an electronic nose to monitor development of fungal microflora during the first eighteen days of rapeseed storage. The Cyranose 320 device manufactured by Sensigent was used to analyse volatile organic compounds. Each sample of infected material was divided into three parts and the degree of spoilage was measured in three ways: analysis of colony forming units, determination of ergosterol content, and measurement of volatile organic compounds with the e-nose. Principal component analysis was performed on the generated patterns of signals and six groups of different spoilage levels were isolated. An analysis of sensorgrams for a few sensors with a strong signal for each group of rapeseed spoilage was performed. The ratio of the association time to the steady state was calculated. This ratio was different for the low level and the highest level of ergosterol and colony forming units. The results have shown that the e-nose can be a useful tool for quick estimation of the degree of rapeseed spoilage.

Quality Control of Orange Juice Using Electronic Nose

2015 ◽  
Vol 1131 ◽  
pp. 242-245
Author(s):  
Rungroj Maolanon ◽  
Winadda Wongwiriyapan ◽  
Sirapat Pratontep
Keyword(s):  
Quality Control ◽  
Electronic Nose ◽  
Gas Sensors ◽  
Orange Juice ◽  
Fermentation Process ◽  
Control Process ◽  
Principal Component ◽  
Alcohol Concentration ◽  
Ethanol Level ◽  
Quality Control Process

Applications of electronic noses to classify the freshness of food and beverages by mimicking the olfactory perception are becoming widely recognized in food industries. For pasteurized orange juice, packaging and shelf-life are key factors for the quality control, which are generally inspected by the sensory stability and quality (odor, color, texture and taste) of the orange juice. An electronic nose based on five different commercial metal oxide gas sensors, a temperature sensor and a humidity sensor has been designed and constructed to examine the quality of orange juice as subjected to the fermentation process. The duration for a single measurement from an orange juice sample was approximately two minutes. The data acquisition of the voltage responses of the gas sensors were achieved via a microcontroller unit. The data classification was statistically analyzed by the “Principal Component Analysis (PCA)”. The Euclidean distance between two PCA groups was used as an indicator of ethanol concentration. The orange juice was laced with various concentrations of ethanol from 0.1 to 1.0% ethanol to simulate fermented orange juice at different stages. The objective was to characterize the freshness of orange juice by means of the ethanol level from the fermentation process. The results show a distinctive classification of the orange juice for an alcohol concentration lower than 0.1%. Thus the electronic nose offers a rapid, highly sensitive alternative for the quality control process.

scholarly journals Freshness assessment of tilapia fish in traditional market based on an electronic nose

2021 ◽  
Vol 10 (5) ◽  
pp. 2466-2476
Author(s):  
Radi Radi ◽  
Eka Wahyudi ◽  
Muhammad Danu Adhityamurti ◽  
Joko Purwo Leksono Yuroto Putro ◽  
Barokah Barokah ◽  
...  
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Electronic Nose ◽  
Gas Sensors ◽  
Principal Component ◽  
Data Recording ◽  
System A ◽  
Traditional Market ◽  
Accuracy Level ◽  
Three Stages

This study evaluates an e-nose based on gas sensors to measure the freshness of tilapia. The device consists of a series of semiconductor sensors as detector, a combination of valve-vial-oxygen as sample delivery system, a microcontroller as interface and controller, and a computer for data recording and processing. The e-nose was firstly used to classify the fresh and non-fresh tilapia. A total of 48 samples of fresh tilapia and 50 samples of non-fresh tilapia were prepared and measured using the e-nose through three stages, namely: flushing, collecting, and purging. The sensor responses were processed into aroma patterns, then classified by two pattern classification softwares of principal component analysis (PCA) and neural network (NN). There were four methods for aroma patterns formation being evaluated: absolute data, normalized absolute data, relative data, normalized relative data. The results showed that the normalized absolute data method provides the best classification with the accuracy level of 93.88%. With this method, the trained NN was used to predict the freshness of 15 tilapia samples collected from a traditional market. The result showed that 60.0% of the samples are classified into fresh category, 33.3% are in the non-fresh category, and 6.7% are not included in both categories.

Rapid Discrimination of Indonesian Herbal Medicines by Using Electronic Nose Based on Array of Commercial Gas Sensors

2015 ◽  
Vol 771 ◽  
pp. 209-212 ◽  
Author(s):  
Fajar Hardoyono ◽  
Kuwat Triyana ◽  
Bambang Heru Iswanto
Keyword(s):  
Electronic Nose ◽  
Gas Sensors ◽  
Herbal Medicines ◽  
Principal Component ◽  
Statistical Analyses ◽  
Local Market ◽  
Multivariate Statistical ◽  
Good Repeatability ◽  
And Cluster Analysis ◽  
Rapid Discrimination

The aim of this study is to discriminate herbal medicines (here after referred to as herbals) by an electronic nose (e-nose) based on an array of eight commercially gas sensors and multivariate statistical analyses. Seven kinds of herbal essential oils purchased from local market in Yogyakarta Indonesia, including zingiberofficinale (ZO), kaempferiagalanga (KG), curcuma longa (CL), curcuma zedoaria (CZ), languasgalanga (LG), pogostemoncablin (PO), and curcuma xanthorrizharoxb (CX) were measured by using this e-nose consecutively. Due to the use of dynamic headspace in this e-nose, data for one cycle (sampling and purging) were recorded every five second for 10 cycles. Each kind of herbals was analyzed for five replications and relative amplitude of the responses was extracted as a feature. The statistical analyses of principal component analysis (PCA) and cluster analysis (CA) were used for discriminating samples. The PCA score plot shows that these 35 essential oil samples were separated into 7 groups based on similarity of patterns. The first two components, PC1 and PC2, capture 96.2% of data variance. Meanwhile, by using 80% similarity, the CA clusters 7 herbals into 3 classes. In this case, the first class consists of ZO and CZ and the second class consists of KG, CL, LG and CX, while the PO sample is clustered in the third class. These classes need to be validated using a standard analytical instrument such as GC/MS. The technique shows some advantages including easy in operation because of without any sample preparation, rapid detection, and good repeatability.

scholarly journals Ionogels Based on a Single Ionic Liquid for Electronic Nose Application

Chemosensors ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 201
Author(s):  
Wellington B. Gonçalves ◽  
Evelyn P. Cervantes ◽  
Ana C. C. S. Pádua ◽  
Gonçalo Santos ◽  
Susana I. C. J. Palma ◽  
...  
Keyword(s):  
Ionic Liquid ◽  
Electronic Nose ◽  
Electronic Devices ◽  
Principal Component ◽  
Solid Matrix ◽  
Imidazolium Ionic Liquid ◽  
Electronic Noses ◽  
Custom Made ◽  
Volatile Organic ◽  
Supervised Classification Methods

Ionogel are versatile materials, as they present the electrical properties of ionic liquids and also dimensional stability, since they are trapped in a solid matrix, allowing application in electronic devices such as gas sensors and electronic noses. In this work, ionogels were designed to act as a sensitive layer for the detection of volatiles in a custom-made electronic nose. Ionogels composed of gelatin and a single imidazolium ionic liquid were doped with bare and functionalized iron oxide nanoparticles, producing ionogels with adjustable target selectivity. After exposing an array of four ionogels to 12 distinct volatile organic compounds, the collected signals were analyzed by principal component analysis (PCA) and by several supervised classification methods, in order to assess the ability of the electronic nose to distinguish different volatiles, which showed accuracy above 98%.

scholarly journals Detection of Water Stress in Khasi Mandarin Orange Plants from Volatile Organic Compound Emission Profile Implementing Electronic Nose

2019 ◽  
Vol 9 (1) ◽  
pp. 133-137 ◽  
Keyword(s):  
Water Stress ◽  
Electronic Nose ◽  
Principal Component ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Gene Expressions ◽  
Volatile Organic Compound Emission ◽  
Linear Discriminant ◽  
Volatile Organic ◽  
Mandarin Orange

Plants in the absence of an innate immune system like animals and being immobile are regularly exposed to a host of stresses, ranging from biotic to abiotic stresses. In response to these, plants have developed a complicated response system like reprogramming gene expressions and emission of secondary metabolites as volatile organic compounds (VOCs) by its various tissues like roots, stems, leaves etc. These VOCs can be used as biomarkers for inspecting plants’ in situ health status. This paper address the usefulness of electronic nose (e-nose) system to sense the VOCs emitted by plants’ leaves to detect the stresses in it. Standard commercial electronic nose (e-nose) system Alfa Mos Fox 3000 has been used here to identify the stressed and non-stressed plants. Fifteen Mandarin orange plants were considered for the study and were subdivided into three categories. Each one was subjected to a different level of water stress. Leaf samples were collected for e-nose analyses from each plant of all three categories on the 15th day and 30th day of induction of water stresses. Dimensionality reduction techniques like kernel Principal Component Analysis (kPCA), Linear Discriminant Analysis (LDA) and classification algorithms like Support Vector Machines (SVC) and Multi-Layer Perceptron Classifier (MLPC) have been used to classify the three categories of plants. The scores obtained from these analyses reveals the feasibility of using an e nose system in discriminating plants based on the status of water stress in them. This paper analyses the applicability of e nose system in stress diagnosis of agricultural and horticultural crops, which would significantly help in controlling the irrigation regime.

Ultrafast Elemental Mapping of Platinum Group Elements and Mineral Identification in Platinum-Palladium Ore Using Laser Induced Breakdown Spectroscopy

Minerals ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 207 ◽  
Author(s):  
Kheireddine Rifai ◽  
Lütfü-Çelebi Özcan ◽  
François R. Doucet ◽  
Kyle Rhoderick ◽  
François Vidal
Keyword(s):  
Chemical Elements ◽  
Principal Component ◽  
Scanning Speed ◽  
Laser Induced Breakdown Spectroscopy ◽  
Chemical Mapping ◽  
Breakdown Spectroscopy ◽  
Nickel Copper ◽  
Laser Induced Breakdown ◽  
Elemental Maps ◽  
Mineral Identification

This paper demonstrates the capability of performing an ultrafast chemical mapping of drill cores collected from a platinum/palladium mine using laser-induced breakdown spectroscopy (LIBS). A scan of 40 mm × 30 mm was performed, using a commercial LIBS analyzer, onto the flat surface of a drill core with a scanning speed of 1000 Hz, and a spatial resolution of 50 µm, in about 8 min. Maps of the scanned areas for seven chemical elements (platinum, palladium, nickel, copper, iron, silicon, and magnesium), as well as a single map including the seven elements altogether, were then generated using the proprietary software integrated into the LIBS analyzer. Based on the latter image, seven minerals were identified using the principal component analysis (PCA) and correlations with the elemental maps.

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