Breath sensors for lung cancer diagnosis

Public Preferences for Lung Cancer Screening Policies

BACKGROUND

Because early detection of lung cancer can substantially improve survival, there is increasing attention for lung cancer screening.

OBJECTIVES

To estimate public preferences for lung cancer screening and to identify subgroups in preferences.

METHODS

Seven important attributes were selected using the literature, interviews, and a panel session. Preferences were elicited using a swing weighting questionnaire. The resulting attribute weights indicate the relative importance of swings from the worst to the best level between attributes. Hierarchical clustering was used to identify subgroups with different attribute weights.

RESULTS

One thousand thirty-four respondents from a representative Dutch panel aged between 40 and 80 years were included. The identified attributes were location of screening (weight = 0.18 ± 0.16), mode of screening (weight = 0.17 ± 0.14), sensitivity (weight = 0.16 ± 0.13) and specificity (weight = 0.13 ± 0.12) of the screening modality, waiting time until results (weight = 0.13 ± 0.12), radiation burden (weight = 0.13 ± 0.12), and duration of screening procedure (weight = 0.10 ± 0.09). Most respondents preferred breath analysis (45%) to giving blood samples (31%) or going through a scanner (24%) as screening modality; 59% preferred screening at the general practitioner's office instead of at the hospital. There was a significant difference in education between the five identified preference subgroups (P < 0.01).

CONCLUSIONS

There is considerable variation in how people value attributes of lung cancer screening. Different screening policies and implementation strategies may be appropriate for particular preference subgroups. Our results indicate that people prefer breath analysis and that they are more likely to attend screening modalities that can be used at a primary care facility.

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification.

Multi-Frequency Measurement of Volatile Organic Compounds With a Radio-Frequency Interferometer

We present a radio-frequency (RF) sensor and its measurement results of three volatile organic compounds (VOCs) at multiple frequency points from ∼ 2 to ∼ 11 GHz, which is a convenient range in our examination. The sensor is based on a simple RF interferometer and uses two coplanar waveguides (CPWs), A and B of 5 and 25 mm length, respectively, as VOC sensing electrodes. Approximately 70-nm-thick poly copolymer films are coated on CPW surfaces for VOC adsorption and concentration. It is shown that ethanol, acetone, and isopropyl (IPA) induce frequency-dependent RF responses, which are also VOC-dependent. Thus, the frequency-dependent properties provide a possible new approach for better VOC sensing selectivity. With CPW A, the limit-of-detections (LODs) are ∼ 600 ppm for ethanol, ∼ 270 ppm for acetone, and ∼ 330 ppm for IPA at 9.29 GHz. With CPW B, the LODs are roughly four times better. These LODs are also better than most of other RF VOC sensor results. In the future work, it is promising to further improve RF sensitivity and selectivity significantly.

Identification of Chinese Herbal Medicines with Electronic Nose Technology: Applications and Challenges

This paper provides a review of the most recent works in machine olfaction as applied to the identification of Chinese Herbal Medicines (CHMs). Due to the wide variety of CHMs, the complexity of growing sources and the diverse specifications of herb components, the quality control of CHMs is a challenging issue. Much research has demonstrated that an electronic nose (E-nose) as an advanced machine olfaction system, can overcome this challenge through identification of the complex odors of CHMs. E-nose technology, with better usability, high sensitivity, real-time detection and non-destructive features has shown better performance in comparison with other analytical techniques such as gas chromatography-mass spectrometry (GC-MS). Although there has been immense development of E-nose techniques in other applications, there are limited reports on the application of E-noses for the quality control of CHMs. The aim of current study is to review practical implementation and advantages of E-noses for robust and effective odor identification of CHMs. It covers the use of E-nose technology to study the effects of growing regions, identification methods, production procedures and storage time on CHMs. Moreover, the challenges and applications of E-nose for CHM identification are investigated. Based on the advancement in E-nose technology, odor may become a new quantitative index for quality control of CHMs and drug discovery. It was also found that more research could be done in the area of odor standardization and odor reproduction for remote sensing.

Fluorometric Biosniffer Camera "Sniff-Cam" for Direct Imaging of Gaseous Ethanol in Breath and Transdermal Vapor

Various volatile organic compounds can be found in human transpiration, breath and body odor. In this paper, a novel two-dimensional fluorometric imaging system, known as a "sniffer-cam" for ethanol vapor released from human breath and palm skin was constructed and validated. This imaging system measures ethanol vapor concentrations as intensities of fluorescence through an enzymatic reaction induced by alcohol dehydrogenase (ADH). The imaging system consisted of multiple ultraviolet light emitting diode (UV-LED) excitation sheet, an ADH enzyme immobilized mesh substrate and a high-sensitive CCD camera. This imaging system uses ADH for recognition of ethanol vapor. It measures ethanol vapor by measuring fluorescence of nicotinamide adenine dinucleotide (NADH), which is produced by an enzymatic reaction on the mesh. This NADH fluorometric imaging system achieved the two-dimensional real-time imaging of ethanol vapor distribution (0.5-200 ppm). The system showed rapid and accurate responses and a visible measurement, which could lead to an analysis of metabolism function at real time in the near future.

A quantitative immunoassay for lung cancer biomarker CIZ1b in patient plasma

OBJECTIVES

Non-invasive tests for early detection of lung cancer are an important unmet clinical need. CIZ1b plasma biomarker can discriminate stage 1 lung cancer from within high-risk groups with clinically useful accuracy, with ROC AUCs in excess of 0.9 for two independent retrospective cohorts, and could therefore meet this need. Our aim was to characterise the native state of the biomarker and develop a quantitative immunoassay.

DESIGN AND METHODS

Selective denaturation, preparative electrophoresis and mass spectrometry of human plasma were used to characterise the biomarker and interaction partners. A sandwich ELISA was generated, and specificity for CIZ1b biomarker tested on lung cancer patient plasma.

RESULTS

CIZ1b biomarker is a denaturation-resistant complex between a C-terminal fragment of CIZ1 bearing the CIZ1b epitope specified by alternative splicing of exon14, and fibrinogen alpha chain. Reconstitution of the biomarker epitope with purified fibrinogen and CIZ1b, but not CIZ1a (non-alternatively spliced exon 14) confirmed the specificity of the results. The endogenous complex is highly stable in lung cancer plasma and can be quantified by pairing of a CIZ1b exon-junction specific antibody with detection of fibrinogen. Application of this sandwich ELISA to a prospectively collected development set of plasmas reveals the same level of accuracy as the western blot used to validate the discriminatory capability of the biomarker.

CONCLUSIONS

Unexpected and unusual molecular structure of CIZ1b in native plasma has complicated immunoassay design, and delayed translation of this promising biomarker. However, CIZ1b can now be measured using a high-throughput, hospital-friendly sandwich ELISA format, overcoming an important barrier to further clinical development and application of this blood test for early stage lung cancer.

A disposable breath sensing tube with on-tube single-nanowire sensor array for on-site detection of exhaled breath biomarkers

This paper presents a novel disposable breath-sensing tube with an on-tube single-nanowire (NW) sensor array for noninvasive, simple, and on-site detection of exhaled breath biomarkers. Although various noninvasive detection methods for lung cancer biomarkers in breath samples exist, they are unsuitable for self-diagnostics and immediate detection because they entail complicated handling procedures and are time intensive. In this study, we simulated, fabricated, and characterized disposable nanosensors by using single TiO₂ and Ag NWs in flexible plastic tubes. The proposed sensors simultaneously detect 2-propyl-1-pentanol (2-PP) lung cancer biomarkers and changes in the humidity, flow rate, and temperature of the flowing gas. The optimal dimension of the tubes was determined and verified through dynamic simulations and experiments. The current tube design decreases sensing variation and moisture interference by 43.28% and 78.77%, respectively, compared with previous designs. In the future, the proposed breath sensor, which has a response time of less than 10 s, can be used in a tube for simple and quick screening of lung cancer patients with the 2-PP concentration exceeding 100 ppb. The developed breath-sensing platform can be applied in noninvasive on-site disease screening.

Chocolate Classification by an Electronic Nose with Pressure Controlled Generated Stimulation

In this work, we will analyze the response of a Metal Oxide Gas Sensor (MOGS) array to a flow controlled stimulus generated in a pressure controlled canister produced by a homemade olfactometer to build an E-nose. The built E-nose is capable of chocolate identification between the 26 analyzed chocolate bar samples and four features recognition (chocolate type, extra ingredient, sweetener and expiration date status). The data analysis tools used were Principal Components Analysis (PCA) and Artificial Neural Networks (ANNs). The chocolate identification E-nose average classification rate was of 81.3% with 0.99 accuracy (Acc), 0.86 precision (Prc), 0.84 sensitivity (Sen) and 0.99 specificity (Spe) for test. The chocolate feature recognition E-nose gives a classification rate of 85.36% with 0.96 Acc, 0.86 Prc, 0.85 Sen and 0.96 Spe. In addition, a preliminary sample aging analysis was made. The results prove the pressure controlled generated stimulus is reliable for this type of studies.

Detection of cancer through exhaled breath: a systematic review

Background

Timely diagnosis of cancer represents a challenging task; in particular, there is a need for reliable non-invasive screening tools that could achieve high levels of adherence at virtually no risk in population-based screening. In this review, we summarize the current evidence of exhaled breath analysis for cancer detection using standard analysis techniques and electronic nose.

Methods

Relevant studies were identified searching Pubmed and Web of Science databases until April 30, 2015. Information on breath test performance, such as sensitivity and specificity, was extracted together with volatile compounds that were used to discriminate cancer patients from controls. Performance of different breath analysis techniques is provided for various cancers together with information on methodological issues, such as breath sampling protocol and validation of the results.

Results

Overall, 73 studies were included, where two-thirds of the studies were conducted on lung cancer. Good discrimination usually required a combination of multiple biomarkers, and area under the receiver operating characteristic curve or accuracy reached levels of 0.9 or higher in multiple studies. In 25% of the reported studies, classification models were built and validated on the same datasets. Huge variability was seen in different aspects among the studies.

Conclusions

Analyses of exhaled breath yielded promising results, although standardization of breath collection, sample storage and data handling remain critical issues. In order to foster breath analysis implementation into practice, larger studies should be implemented in true screening settings, paying particular attention to standardization in breath collection, consideration of covariates, and validation in independent population samples.

Fusing Sensor Paradigms to Acquire Chemical Information: An Integrative Role for Smart Biopolymeric Hydrogels

The Information Age transformed our lives but it has had surprisingly little impact on the way chemical information (e.g., from our biological world) is acquired, analyzed and communicated. Sensor systems are poised to change this situation by providing rapid access to chemical information. This access will be enabled by technological advances from various fields: biology enables the synthesis, design and discovery of molecular recognition elements as well as the generation of cell-based signal processors; physics and chemistry are providing nano-components that facilitate the transmission and transduction of signals rich with chemical information; microfabrication is yielding sensors capable of receiving these signals through various modalities; and signal processing analysis enhances the extraction of chemical information. The authors contend that integral to the development of functional sensor systems will be materials that (i) enable the integrative and hierarchical assembly of various sensing components (for chemical recognition and signal transduction) and (ii) facilitate meaningful communication across modalities. It is suggested that stimuli-responsive self-assembling biopolymers can perform such integrative functions, and redox provides modality-spanning communication capabilities. Recent progress toward the development of electrochemical sensors to manage schizophrenia is used to illustrate the opportunities and challenges for enlisting sensors for chemical information processing.

Artificial Nose Technology: Status and Prospects in Diagnostics

Biomimetic crossreactive sensor arrays have been used to detect and analyze a wide variety of vapor and liquid components in applications such as food science, public health and safety, and diagnostics. As technology has advanced over the past three decades, these systems have become selective, sensitive, and affordable. Currently, the need for noninvasive and accurate devices for early disease diagnosis remains a challenge. This Opinion article provides an overview of the various types of biomimetic crossreactive sensor arrays (also referred to as electronic noses or tongues in the literature), their current use and future directions, and an outlook for future technological development.

Biophysical and biological perspective in biosemiotics

The cell and its basic constituents are introduced here through a biophysical and information communication theoretic approach in biology and biosemiotics. With this purpose, the requirements of primordial cellular structures, single binding events, and signalling cascades are first mentioned stepwise, in relation to the model of the cellular sensing mechanism. This is followed by the concepts of cross reactions in sensing and pattern recognitions, wherein an information theoretic approach is addressed and the features of multicellularity are discussed along. Multicellularity is introduced as the path that leads to the loss of the direct causal relations. The loss of true causal relation is considered as a form of translation that enables meaning-encoded communication over the informative processes. In this sense, semiosis may not be exclusive. Synthetic biology is exemplified as a form of artificial selection mechanisms for the generation of 'self-reproducing' systems with information coding and processing machineries. These discussions are summarised at the end.

Headspace-programmed temperature vaporizer-mass spectrometry and pattern recognition techniques for the analysis of volatiles in saliva samples

A rapid method for the analysis of volatiles in saliva samples is proposed. The method is based on direct coupling of three components: a headspace sampler (HS), a programmable temperature vaporizer (PTV) and a quadrupole mass spectrometer (qMS). Several applications in the biomedical field have been proposed with electronic noses based on different sensors. However, few contributions have been developed using a mass spectrometry-based electronic nose in this field up to date. Samples of 23 patients with some type of cancer and 32 healthy volunteers were analyzed with HS-PTV-MS and the profile signals obtained were subjected to pattern recognition techniques with the aim of studying the possibilities of the methodology to differentiate patients with cancer from healthy controls. An initial inspection of the contained information in the data by means of principal components analysis (PCA) revealed a complex situation were an overlapped distribution of samples in the score plot was visualized instead of two groups of separated samples. Models using K-nearest neighbors (KNN) and Soft Independent Modeling of Class Analogy (SIMCA) showed poor discrimination, specially using SIMCA where a small distance between classes was obtained and no satisfactory results in the classification of the external validation samples were achieved. Good results were obtained when Mahalanobis discriminant analysis (DA) and support vector machines (SVM) were used obtaining 2 (false positives) and 0 samples misclassified in the external validation set, respectively. No false negatives were found using these techniques.

Headspace-programmed temperature vaporization-mass spectrometry for the rapid determination of possible volatile biomarkers of lung cancer in urine

We propose a new method for the rapid determination of five volatile compounds described in the literature as possible biomarkers of lung cancer in urine samples. The method is based on the coupling of a headspace sampler, a programmed temperature vaporizer in solvent-vent injection mode, and a mass spectrometer (HS-PTV-MS). This configuration is known as an electronic nose based on mass spectrometry. Once the method was developed, it was used for the analysis of urine samples from lung cancer patients and healthy individuals. Multivariate calibration models were employed to quantify the biomarker concentrations in the samples. The detection limits ranged between 0.16 and 21 μg/L. For the assignment of the samples to the patient group or the healthy individuals, the Wilcoxon signed-rank test was used, comparing the concentrations obtained with the median of a reference set of healthy individuals. To date, this is the first time that multivariate calibration and non-parametric methods have been combined to classify biological samples from profile signals obtained with an electronic nose. When significant differences in the concentration of one or more biomarkers were found with respect to the reference set, the sample is considered as a positive one and a new analysis was performed using a chromatographic method (HS-PTV-GC/MS) to confirm the result. The main advantage of the proposed HS-PTV-MS methodology is that no prior chromatographic separation and no sample manipulation are required, which allows an increase of the number of samples analyzed per hour and restricts the use of time-consuming techniques to only when necessary. Graphical abstract Schematic diagram of the developed methodology.

Carbon Nanotube-Based Chemical Sensors

The need to sense gases and vapors arises in numerous scenarios in industrial, environmental, security and medical applications. Traditionally, this activity has utilized bulky instruments to obtain both qualitative and quantitative information on the constituents of the gas mixture. It is ideal to use sensors for this purpose since they are smaller in size and less expensive; however, their performance in the field must match that of established analytical instruments in order to gain acceptance. In this regard, nanomaterials as sensing media offer advantages in sensitivity, preparation of chip-based sensors and construction of electronic nose for selective detection of analytes of interest. This article provides a review of the use of carbon nanotubes in gas and vapor sensing.

Enhancing Electronic Nose Performance Based on a Novel QPSO-KELM Model

A novel multi-class classification method for bacteria detection termed quantum-behaved particle swarm optimization-based kernel extreme learning machine (QPSO-KELM) based on an electronic nose (E-nose) technology is proposed in this paper. Time and frequency domain features are extracted from E-nose signals used for detecting four different classes of wounds (uninfected and infected with Staphylococcu aureus, Escherichia coli and Pseudomonas aeruginosa) in this experiment. In addition, KELM is compared with five existing classification methods: Linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), extreme learning machine (ELM), k-nearest neighbor (KNN) and support vector machine (SVM). Meanwhile, three traditional optimization methods including particle swarm optimization algorithm (PSO), genetic algorithm (GA) and grid search algorithm (GS) and four kernel functions (Gaussian kernel, linear kernel, polynomial kernel and wavelet kernel) for KELM are discussed in this experiment. Finally, the QPSO-KELM model is also used to deal with another two experimental E-nose datasets in the previous experiments. The experimental results demonstrate the superiority of QPSO-KELM in various E-nose applications.

Electrospun nanofibers for cancer diagnosis and therapy

The advent of nanotechnology has provided unprecedented opportunities for nanomedicine. Electrospun nanofibers have some astounding features such as high loading capacity, extremely large surface area and porosity, high encapsulation efficiency, ease of modification, combination of diverse therapies, low cost and great benefits. These remarkable structure-dependent properties have far reaching application potential in cancer diagnosis and therapy such as ultra-sensitive sensing systems for point-of-care cancer detection, targeted cancer cell capture, and functional and smart anticancer drug delivery systems. This review summarizes the principal mechanism of electrospun nanofibers and a variety of modified electrospun nanofibers, illustrates their application in biosensors for cancer detection, and enumerates their application in implantable drug delivery for cancer therapy.

Detection of Volatile Organic Compounds by Self-assembled Monolayer Coated Sensor Array with Concentration-independent Fingerprints

In this paper, we have modeled and analyzed affinities and kinetics of volatile organic compounds (VOCs) adsorption (and desorption) on various surface chemical groups using multiple self-assembled monolayers (SAMs) functionalized film bulk acoustic resonator (FBAR) array. The high-frequency and micro-scale resonator provides improved sensitivity in the detections of VOCs at trace levels. With the study of affinities and kinetics, three concentration-independent intrinsic parameters (monolayer adsorption capacity, adsorption energy constant and desorption rate) of gas-surface interactions are obtained to contribute to a multi-parameter fingerprint library of VOC analytes. Effects of functional group's properties on gas-surface interactions are also discussed. The proposed sensor array with concentration-independent fingerprint library shows potential as a portable electronic nose (e-nose) system for VOCs discrimination and gas-sensitive materials selections.

Blinded Validation of Breath Biomarkers of Lung Cancer, a Potential Ancillary to Chest CT Screening

Background

Breath volatile organic compounds (VOCs) have been reported as biomarkers of lung cancer, but it is not known if biomarkers identified in one group can identify disease in a separate independent cohort. Also, it is not known if combining breath biomarkers with chest CT has the potential to improve the sensitivity and specificity of lung cancer screening.

Methods

Model-building phase (unblinded): Breath VOCs were analyzed with gas chromatography mass spectrometry in 82 asymptomatic smokers having screening chest CT, 84 symptomatic high-risk subjects with a tissue diagnosis, 100 without a tissue diagnosis, and 35 healthy subjects. Multiple Monte Carlo simulations identified breath VOC mass ions with greater than random diagnostic accuracy for lung cancer, and these were combined in a multivariate predictive algorithm. Model-testing phase (blinded validation): We analyzed breath VOCs in an independent cohort of similar subjects (n = 70, 51, 75 and 19 respectively). The algorithm predicted discriminant function (DF) values in blinded replicate breath VOC samples analyzed independently at two laboratories (A and B). Outcome modeling: We modeled the expected effects of combining breath biomarkers with chest CT on the sensitivity and specificity of lung cancer screening.

Results

Unblinded model-building phase. The algorithm identified lung cancer with sensitivity 74.0%, specificity 70.7% and C-statistic 0.78. Blinded model-testing phase: The algorithm identified lung cancer at Laboratory A with sensitivity 68.0%, specificity 68.4%, C-statistic 0.71; and at Laboratory B with sensitivity 70.1%, specificity 68.0%, C-statistic 0.70, with linear correlation between replicates (r = 0.88). In a projected outcome model, breath biomarkers increased the sensitivity, specificity, and positive and negative predictive values of chest CT for lung cancer when the tests were combined in series or parallel.

Conclusions

Breath VOC mass ion biomarkers identified lung cancer in a separate independent cohort, in a blinded replicated study. Combining breath biomarkers with chest CT could potentially improve the sensitivity and specificity of lung cancer screening.

Trial Registration

ClinicalTrials.gov NCT00639067

Electronic Nose Feature Extraction Methods: A Review

Many research groups in academia and industry are focusing on the performance improvement of electronic nose (E-nose) systems mainly involving three optimizations, which are sensitive material selection and sensor array optimization, enhanced feature extraction methods and pattern recognition method selection. For a specific application, the feature extraction method is a basic part of these three optimizations and a key point in E-nose system performance improvement. The aim of a feature extraction method is to extract robust information from the sensor response with less redundancy to ensure the effectiveness of the subsequent pattern recognition algorithm. Many kinds of feature extraction methods have been used in E-nose applications, such as extraction from the original response curves, curve fitting parameters, transform domains, phase space (PS) and dynamic moments (DM), parallel factor analysis (PARAFAC), energy vector (EV), power density spectrum (PSD), window time slicing (WTS) and moving window time slicing (MWTS), moving window function capture (MWFC), etc. The object of this review is to provide a summary of the various feature extraction methods used in E-noses in recent years, as well as to give some suggestions and new inspiration to propose more effective feature extraction methods for the development of E-nose technology.

Detection of Volatile Organic Compounds Using Microfabricated Resonator Array Functionalized with Supramolecular Monolayers

This paper describes the detection of volatile organic compounds (VOCs) using an e-nose type integrated microfabricated sensor array, in which each resonator is coated with different supramolecular monolayers: p-tert-butyl calix[8]arene (Calix[8]arene), 2,3,7,8,12,13,17,18-octaethyl-21H,23H-porphine (Porphyrin), β-cyclodextrin (β-CD), and cucurbit[8]uril (CB[8]). Supramolecular monolayers fabricated by Langmuir-Blodgett techniques work as specific sensing interface for different VOCs recognition which increase the sensor selectivity. Microfabricated ultrahigh working frequency film bulk acoustic resonator (FBAR) transducers (4.4 GHz) enable their high sensitivity toward monolayer gas sensing which facilitate the analyses of VOCs adsorption isotherms and kinetics. Two affinity constants (K1, K2) are obtained for each VOC, which indicate the gas molecule adsorption happen inside and outside of the supramolecular cavities. Additional kinetic information on adsorption and desorption rate constants (ka, kd) are obtained as well from exponential fitting results. The five parameters, one from the conventional frequency shift signals of mass transducers and the other four from the indirect analyses of monolayer adsorption behaviors, thus enrich the sensing matrix (Δf, K1, K2, ka, kd) which can be used as multiparameter fingerprint patterns for highly selective detection and discrimination of VOCs.

A Novel Feature Extraction Approach Using Window Function Capturing and QPSO-SVM for Enhancing Electronic Nose Performance

In this paper, a novel feature extraction approach which can be referred to as moving window function capturing (MWFC) has been proposed to analyze signals of an electronic nose (E-nose) used for detecting types of infectious pathogens in rat wounds. Meanwhile, a quantum-behaved particle swarm optimization (QPSO) algorithm is implemented in conjunction with support vector machine (SVM) for realizing a synchronization optimization of the sensor array and SVM model parameters. The results prove the efficacy of the proposed method for E-nose feature extraction, which can lead to a higher classification accuracy rate compared to other established techniques. Meanwhile it is interesting to note that different classification results can be obtained by changing the types, widths or positions of windows. By selecting the optimum window function for the sensor response, the performance of an E-nose can be enhanced.

Current Challenges in Volatile Organic Compounds Analysis as Potential Biomarkers of Cancer

An early diagnosis and appropriate treatment are crucial in reducing mortality among people suffering from cancer. There is a lack of characteristic early clinical symptoms in most forms of cancer, which highlights the importance of investigating new methods for its early detection. One of the most promising methods is the analysis of volatile organic compounds (VOCs). VOCs are a diverse group of carbon-based chemicals that are present in exhaled breath and biofluids and may be collected from the headspace of these matrices. Different patterns of VOCs have been correlated with various diseases, cancer among them. Studies have also shown that cancer cells in vitro produce or consume specific VOCs that can serve as potential biomarkers that differentiate them from noncancerous cells. This review identifies the current challenges in the investigation of VOCs as potential cancer biomarkers, by the critical evaluation of available matrices for the in vivo and in vitro approaches in this field and by comparison of the main extraction and detection techniques that have been applied to date in this area of study. It also summarises complementary in vivo, ex vivo, and in vitro studies conducted to date in order to try to identify volatile biomarkers of cancer.