Detection of Ovarian Cancer through Exhaled Breath by Electronic Nose: A Prospective Study

Background: Diagnostic methods for the early identification of ovarian cancer (OC) represent an unmet clinical need, as no reliable diagnostic tools are available. Here, we tested the feasibility of electronic nose (e-nose), composed of ten metal oxide semiconductor (MOS) sensors, as a diagnostic tool for OC detection. Methods: Women with suspected ovarian masses and healthy subjects had volatile organic compounds analysis of the exhaled breath using e-nose. Results: E-nose analysis was performed on breath samples collected from 251 women divided into three groups: 86 OC cases, 51 benign masses, and 114 controls. Data collected were analyzed by Principal Component Analysis (PCA) and K-Nearest Neighbors’ algorithm (K-NN). A first 1-K-NN (cases vs. controls) model has been developed to discriminate between OC cases and controls; the model performance tested in the prediction gave 98% of sensitivity and 95% of specificity, when the strict class prediction was applied; a second 1-K-NN (cases vs. controls + benign) model was built by grouping the non-cancer groups (controls + benign), thus considering two classes, cases and controls + benign; the model performance in the prediction was of 89% for sensitivity and 86% for specificity when the strict class prediction was applied. Conclusions: Our preliminary results suggested the potential role of e-nose for the detection of OC. Further studies aiming to test the potential adoption of e-nose in the early diagnosis of OC are needed.

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

Jornal Brasileiro de Pneumologia ◽

10.1590/s1806-37562015000000195 ◽

2016 ◽

Vol 42 (2) ◽

pp. 143-145 ◽

Cited By ~ 8

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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Identification of Multiple Outliers in a Generalized Linear Model with Continuous Variables

Mathematical Problems in Engineering ◽

10.1155/2016/5840523 ◽

2016 ◽

Vol 2016 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Loo Yee Peng ◽

Habshah Midi ◽

Sohel Rana ◽

Anwar Fitrianto

Keyword(s):

Generalized Linear Model ◽

Diagnostic Method ◽

Asymptotic Properties ◽

Diagnostic Methods ◽

Detection Methods ◽

Diagnostic Tools ◽

Continuous Variables ◽

Numerical Examples ◽

Multiple Outliers ◽

Contamination Levels

In the statistical analysis of data, a model might be awfully fitted with the presence of outliers. Besides, it has been well established to use residuals for identification of outliers. The asymptotic properties of residuals can be utilized to contribute diagnostic tools. However, it is now evident that most of the existing diagnostic methods have failed in identifying multiple outliers. Therefore, this paper proposed a diagnostic method for the identification of multiple outliers in GLM, where traditionally used outlier detection methods are effortless as they undergo masking or swamping dilemma. Hence, an investigation was carried out to determine the capability of the proposed GSCPR method. The findings obtained from the numerical examples indicated that the performance of the proposed method was satisfactory for the identification of multiple outliers. Meanwhile, in the simulation study, two scenarios were considered to assess the validity of the proposed method. The proposed method consistently displayed higher percentage of correct detection, as well as lower rates of swamping and masking, regardless of the sample size and the contamination levels.

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Detection of Airway Colonization by Aspergillus fumigatus by Use of Electronic Nose Technology in Patients with Cystic Fibrosis

Journal of Clinical Microbiology ◽

10.1128/jcm.02214-15 ◽

2015 ◽

Vol 54 (3) ◽

pp. 569-575 ◽

Cited By ~ 24

Author(s):

K. de Heer ◽

M. G. M. Kok ◽

N. Fens ◽

E. J. M. Weersink ◽

A. H. Zwinderman ◽

...

Keyword(s):

Cystic Fibrosis ◽

Aspergillus Fumigatus ◽

Electronic Nose ◽

External Validation ◽

Invasive Pulmonary Aspergillosis ◽

Principal Component ◽

Exhaled Breath ◽

Roc Curve Analysis ◽

Noninvasive Test ◽

Content Type

Currently, there is no noninvasive test that can reliably diagnose early invasive pulmonary aspergillosis (IA). An electronic nose (eNose) can discriminate various lung diseases through an analysis of exhaled volatile organic compounds. We recently published a proof-of-principle study showing that patients with prolonged chemotherapy-induced neutropenia and IA have a distinct exhaled breath profile (or breathprint) that can be discriminated with an eNose. An eNose is cheap and noninvasive, and it yields results within minutes. We determined whetherAspergillus fumigatuscolonization may also be detected with an eNose in cystic fibrosis (CF) patients. Exhaled breath samples of 27 CF patients were analyzed with a Cyranose 320. Culture of sputum samples defined theA. fumigatuscolonization status. eNose data were classified using canonical discriminant analysis after principal component reduction. Our primary outcome was cross-validated accuracy, defined as the percentage of correctly classified subjects using the leave-one-out method. ThePvalue was calculated by the generation of 100,000 random alternative classifications. Nine of the 27 subjects were colonized byA. fumigatus. In total, 3 subjects were misclassified, resulting in a cross-validated accuracy of the Cyranose detecting IA of 89% (P= 0.004; sensitivity, 78%; specificity, 94%). Receiver operating characteristic (ROC) curve analysis showed an area under the curve (AUC) of 0.89. The results indicate thatA. fumigatuscolonization leads to a distinctive breathprint in CF patients. The present proof-of-concept data merit external validation and monitoring studies.

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Fast and noninvasive electronic nose for sniffing out COVID-19 based on exhaled breath-print recognition

10.21203/rs.3.rs-750988/v1 ◽

2021 ◽

Author(s):

Dian Kesumapramudya Nurputra ◽

Ahmad Kusumaatmadja ◽

Mohamad Saifudin Hakim ◽

Shidiq Nur Hidayat ◽

Trisna Julian ◽

...

Keyword(s):

Machine Learning ◽

Electronic Nose ◽

Quantitative Polymerase Chain Reaction ◽

Machine Learning Algorithms ◽

Oxide Semiconductor ◽

Support Vector ◽

Detection Accuracy ◽

Exhaled Breath ◽

Laboratory Equipment ◽

Linear Discriminant

Abstract Despite its high accuracy to detect the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the reverse transcription-quantitative polymerase chain reaction (RT-qPCR) approach possesses several limitations (e.g., the lengthy invasive procedure, the reagent availability, and the requirement of specialized laboratory, equipment, and trained staffs). We developed and employed a low-cost, noninvasive method to rapidly sniff out the coronavirus disease 2019 (COVID-19) based on a portable electronic nose (GeNose C19) integrating metal oxide semiconductor gas sensor array, optimized feature extraction, and machine learning models. This approach was evaluated in profiling tests involving a total number of 615 breath samples (i.e., 333 positive and 282 negative COVID-19 confirmed by RT-qPCR) obtained from 83 patients in two hospitals located in the Special Region of Yogyakarta, Indonesia. Four different machine learning algorithms (i.e., linear discriminant analysis (LDA), support vector machine (SVM), stacked multilayer perceptron (MLP), and deep neural network (DNN)) were utilized to identify the top-performing pattern recognition methods and to obtain high system detection accuracy (88–95%), sensitivity (86–94%), specificity (88–95%) levels from the testing datasets. Our results suggest that GeNose C19 can be considered a highly potential breathalyzer for fast COVID-19 screening.

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Development and application of a new low cost electronic nose for the ripeness monitoring of banana using computational techniques (PCA, LDA, SIMCA and SVM)

Czech Journal of Food Sciences ◽

10.17221/113/2014-cjfs ◽

2014 ◽

Vol 32 (No. 6) ◽

pp. 538-548 ◽

Cited By ~ 21

Author(s):

A. Sanaeifar ◽

S.S. Mohtasebi ◽

M. Ghasemi-Varnamkhasti ◽

H. Ahmadi ◽

J. Lozano

Keyword(s):

Electronic Nose ◽

Low Cost ◽

Principal Component ◽

Oxide Semiconductor ◽

Support Vector ◽

Computational Techniques ◽

Laboratory Equipment ◽

Linear Discriminant ◽

Ripening Stages ◽

Non Destructive

Potential application of a metal oxide semiconductor based electronic nose (e-nose) as a non-destructive instrument for monitoring the change in volatile production of banana during the ripening process was studied. The proposed e-nose does not need any advanced or expensive laboratory equipment and proved to be reliable in recording meaningful differences between ripening stages. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Soft Independent Modelling of Class Analogy (SIMCA) and Support Vector Machines (SVM) techniques were used for this purpose. Results showed that the proposed e-nose can distinguish between different ripening stages. The e-nose was able to detect a clear difference in the aroma fingerprint of banana when using SVM analysis compared with PCA and LDA, SIMCA analysis. Using SVM analysis, it was possible to differentiate and to classify the different banana ripening stages, and this method was able to classify 98.66% of the total samples in each respective group. Sensor array capabilities in the classification of ripening stages using loading analysis and SVM and SIMCA were also investigated, which leads to develop the application of a specific e-nose system by applying the most effective sensors or ignoring the redundant sensors.  

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Meat and Fish Freshness Assessment by a Portable and Simplified Electronic Nose System (Mastersense)

Sensors ◽

10.3390/s19143225 ◽

2019 ◽

Vol 19 (14) ◽

pp. 3225 ◽

Cited By ~ 11

Author(s):

Grassi ◽

Benedetti ◽

Opizzio ◽

Nardo ◽

Buratti

Keyword(s):

Electronic Nose ◽

Ad Hoc ◽

Low Cost ◽

Principal Component ◽

Partial Least Square ◽

Least Square ◽

Oxide Semiconductor ◽

Prediction Ability ◽

Nearest Neighbours ◽

Electronic Nose System

The evaluation of meat and fish quality is crucial to ensure that products are safe and meet the consumers’ expectation. The present work aims at developing a new low-cost, portable, and simplified electronic nose system, named Mastersense, to assess meat and fish freshness. Four metal oxide semiconductor sensors were selected by principal component analysis and were inserted in an “ad hoc” designed measuring chamber. The Mastersense system was used to test beef and poultry slices, and plaice and salmon fillets during their shelf life at 4 °C, from the day of packaging and beyond the expiration date. The same samples were tested for Total Viable Count, and the microbial results were used to define freshness classes to develop classification models by the K-Nearest Neighbours’ algorithm and Partial Least Square–Discriminant Analysis. All the obtained models gave global sensitivity and specificity with prediction higher than 83.3% and 84.0%, respectively. Moreover, a McNemar’s test was performed to compare the prediction ability of the two classification algorithms, which resulted in comparable values (p > 0.05). Thus, the Mastersense prototype implemented with the K-Nearest Neighbours’ model is considered the most convenient strategy to assess meat and fish freshness.

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The Electronic Nose Coupled with Chemometric Tools for Discriminating the Quality of Black Tea Samples In Situ

Chemosensors ◽

10.3390/chemosensors7030029 ◽

2019 ◽

Vol 7 (3) ◽

pp. 29 ◽

Cited By ~ 7

Author(s):

Shidiq Nur Hidayat ◽

Kuwat Triyana ◽

Inggrit Fauzan ◽

Trisna Julian ◽

Danang Lelono ◽

...

Keyword(s):

Electronic Nose ◽

External Validation ◽

Principal Component ◽

Black Tea ◽

Oxide Semiconductor ◽

Support Vector ◽

Quality Level ◽

Validation Data ◽

Signal Preprocessing

An electronic nose (E-nose), comprising eight metal oxide semiconductor (MOS) gas sensors, was used in situ for real-time classification of black tea according to its quality level. Principal component analysis (PCA) coupled with signal preprocessing techniques (i.e., time set value preprocessing, F1; area under curve preprocessing, F2; and maximum value preprocessing, F3), allowed grouping the samples from seven brands according to the quality level. The E-nose performance was further checked using multivariate supervised statistical methods, namely, the linear and quadratic discriminant analysis, support vector machine together with linear or radial kernels (SVM-linear and SVM-radial, respectively). For this purpose, the experimental dataset was split into two subsets, one used for model training and internal validation using a repeated K-fold cross-validation procedure (containing the samples collected during the first three days of tea production); and the other, for external validation purpose (i.e., test dataset, containing the samples collected during the 4th and 5th production days). The results pointed out that the E-nose-SVM-linear model together with the F3 signal preprocessing method was the most accurate, allowing 100% of correct predictive classifications (external-validation data subset) of the samples according to their quality levels. So, the E-nose-chemometric approach could be foreseen has a practical and feasible classification tool for assessing the black tea quality level, even when applied in-situ, at the harsh industrial environment, requiring a minimum and simple sample preparation. The proposed approach is a cost-effective and fast, green procedure that could be implemented in the near future by the tea industry.

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Fully integrated ultra-sensitive electronic nose based on organic field-effect transistors

Scientific Reports ◽

10.1038/s41598-021-88569-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Daniil S. Anisimov ◽

Victoria P. Chekusova ◽

Askold A. Trul ◽

Anton A. Abramov ◽

Oleg V. Borshchev ◽

...

Keyword(s):

Electronic Nose ◽

Field Effect ◽

Field Effect Transistors ◽

Limit Of Detection ◽

Low Cost ◽

Meat Product ◽

Principal Component ◽

Exhaled Breath ◽

Organic Field Effect Transistors ◽

Fully Integrated

AbstractModern solid-state gas sensors approaching ppb-level limit of detection open new perspectives for process control, environmental monitoring and exhaled breath analysis. Organic field-effect transistors (OFETs) are especially promising for gas sensing due to their outstanding sensitivities, low cost and small power consumption. However, they suffer of poor selectivity, requiring development of cross-selective arrays to distinguish analytes, and environmental instability, especially in humid air. Here we present the first fully integrated OFET-based electronic nose with the whole sensor array located on a single substrate. It features down to 30 ppb limit of detection provided by monolayer thick active layers and operates in air with up to 95% relative humidity. By means of principal component analysis, it is able to discriminate toxic air pollutants and monitor meat product freshness. The approach presented paves the way for developing affordable air sensing networks for the Internet of Things.

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Dual-Channel Microarray Sensor System for Lung Cancer-Related Volatile Organic Compounds Identification in Exhaled Breath

10.20944/preprints201907.0150.v1 ◽

2019 ◽

Author(s):

Yonghong Xu ◽

Jihong Jiang ◽

Huimin Bu ◽

Pengcheng Zhu ◽

Jiebang Jiang ◽

...

Keyword(s):

Lung Cancer ◽

Screening Method ◽

Principal Component ◽

Lung Cancer Screening ◽

High Specificity ◽

Hierarchical Cluster ◽

Diagnostic Methods ◽

Specific Response ◽

Exhaled Breath ◽

Dual Channel

Lung cancer remains the leading cancer killer worldwide. Early diagnosis can effectively increase the patient cure rate but existing diagnostic methods limit early lung cancer diagnosis. Therefore, development of a simple but efficient lung cancer screening method is important to improvement of both the diagnosis rate and the survival rate of lung cancer patients. In this study, ten photosensitive materials with high sensitivity and high specificity were screened accurately to construct a microarray sensor that can rapidly identify six types of lung cancer biomarkers in exhaled breath. Results from hierarchical cluster analysis (HCA), principal component analysis (PCA) and difference maps showed that the classification of the analytes agreed with structure similarity laws. The detection results from parallel experiments and structurally similar analytes, in turn, cluster into a group; the fingerprints of the different analytes have specific response regions. The well-screened sensor chip fabrication workload and cost were both reduced by approximately two thirds, while the microfluidic device sensitivity and stability increased by approximately 1.3 times their corresponding values before optimization. The dual-channel device also offers real-time contrast detection and synchronous parallel detection functions and has potential application prospects for use in extensive screening of high-risk populations for lung cancer.

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Exhaled Breath Condensate—A Non-Invasive Approach for Diagnostic Methods in Asthma

Journal of Clinical Medicine ◽

10.3390/jcm10122697 ◽

2021 ◽

Vol 10 (12) ◽

pp. 2697

Author(s):

Joanna Połomska ◽

Kamil Bar ◽

Barbara Sozańska

Keyword(s):

Airway Inflammation ◽

Exhaled Breath Condensate ◽

Clinical Manifestations ◽

Diagnostic Methods ◽

Diagnostic Tools ◽

Exhaled Breath ◽

Invasive Approach ◽

Non Invasive ◽

Airway Tone ◽

Breath Condensate

The pathophysiology of asthma has been intensively studied, but its underlying mechanisms such as airway inflammation, control of airway tone, and bronchial reactivity are still not completely explained. There is an urgent need to implement novel, non-invasive diagnostic tools that can help to investigate local airway inflammation and connect the molecular pathways with the broad spectrum of clinical manifestations of asthma. The new biomarkers of different asthma endotypes could be used to confirm diagnosis, predict asthma exacerbations, or evaluate treatment response. In this paper, we briefly describe the characteristics of exhaled breath condensate (EBC) that is considered to be an interesting source of biomarkers of lung disorders. We look at the composition of EBC, some aspects of the collection procedure, the proposed biomarkers for asthma, and its clinical implications. We also indicate the limitations of the method and potential strategies to standardize the procedure of EBC collection and analytical methods.

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