Technologies and tools for mimicking olfaction: status of the Rome "Tor Vergata" electronic nose

Synergy between nanomaterials and volatile organic compounds for non-invasive medical evaluation

This article is an overview of the present and ongoing developments in the field of nanomaterial-based sensors for enabling fast, relatively inexpensive and minimally (or non-) invasive diagnostics of health conditions with follow-up by detecting volatile organic compounds (VOCs) excreted from one or combination of human body fluids and tissues (e.g., blood, urine, breath, skin). Part of the review provides a didactic examination of the concepts and approaches related to emerging sensing materials and transduction techniques linked with the VOC-based non-invasive medical evaluations. We also present and discuss diverse characteristics of these innovative sensors, such as their mode of operation, sensitivity, selectivity and response time, as well as the major approaches proposed for enhancing their ability as hybrid sensors to afford multidimensional sensing and information-based sensing. The other parts of the review give an updated compilation of the past and currently available VOC-based sensors for disease diagnostics. This compilation summarizes all VOCs identified in relation to sickness and sampling origin that links these data with advanced nanomaterial-based sensing technologies. Both strength and pitfalls are discussed and criticized, particularly from the perspective of the information and communication era. Further ideas regarding improvement of sensors, sensor arrays, sensing devices and the proposed workflow are also included.

Cutting Edge Methods for Non-Invasive Disease Diagnosis Using E-Tongue and E-Nose Devices

Biomimetic cross-reactive sensor arrays (B-CRSAs) have been used to detect and diagnose a wide variety of diseases including metabolic disorders, mental health diseases, and cancer by analyzing both vapor and liquid patient samples. Technological advancements over the past decade have made these systems selective, sensitive, and affordable. To date, devices for non-invasive and accurate disease diagnosis have seen rapid improvement, suggesting a feasible alternative to current standards for medical diagnostics. This review provides an overview of the most recent B-CRSAs for diagnostics (also referred to electronic noses and tongues in the literature) and an outlook for future technological development.

An e-nose made of carbon nanotube based quantum resistive sensors for the detection of eighteen polar/nonpolar VOC biomarkers of lung cancer

A room temperature operating electronic nose (e-nose) has been developed by the assembly of conductive polymer nanocomposite (CPC) quantum resistive sensors (QRS). The fabrication of QRS by spray layer by layer (sLbL) of CPC solutions allowed us to obtain transducers with reproducible initial properties that could be easily tailored by adjusting either the number of sprayed layers and/or the solution composition. The selectivity of QRS was varied by changing the chemical nature of the polymer matrix in which carbon nanotubes (CNTs) were dispersed in solution, i.e., poly(carbonate) (PC), poly(caprolactone) (PCL), poly(lactic acid) (PLA), poly(styrene) (PS), and poly(methyl methacrylate) (PMMA). The e-nose was then successfully used to detect several volatile organic compounds (VOCs) selected among lung cancer biomarkers: a first set of seven polar vapours (water, ethanol, methanol, acetone, propanol, isopropanol, and 2-butanone), and another set of eleven less and nonpolar vapours (chloroform, toluene, benzene, styrene, cyclohexane, o-xylene, n-propane, n-decane, 1,2,4-trimethyl benzene, isoprene, and 1-hexene). The discrimination ability of the e-nose evaluated after a 3D principal component analysis (PCA) pattern recognition treatment was proved to be very good. Moreover, the quantitativity of the transducers' chemo-resistive responses was well fitted with the Langmuir-Henry-Clustering (LHC) model for both acetone and toluene vapours in a wide range of concentrations. The QRS developed in this study appear to be very good candidates to design low cost e-noses for the anticipated diagnosis of lung cancer by VOC analysis in breath, with ppm level sensitivity (tested down to 2.5 parts per million), short response time (a couple of seconds), low consumption, and a large signal to noise ratio (SNR ≥ 10).

Piezoelectric sensors for dioxins: a biomimetic approach

The aim of this work was to design a fast, cheap and easy to use analytical system for dioxins. Piezoelectric sensors coupled with the pentapeptides as biomimetic traps (the receptors), selective for the dioxins, were used for the realisation of this analytical system. A methodology to select specific receptors among all possible pentapeptides randomly generated was represented by the use of molecular modelling software. Three peptides called later on A, B and C (A:[N]Asn-Phe-Gln-Gly-Ile[C]; B:[N]Asn-Phe-Gln-Gly-Gln[C]; C:[N]Asn-Phe-Gln-Gly-Phe[C]), were selected and evaluated for their potential usage as artificial receptors in solid-gas analysis by using a quartz crystal microbalance (QCM) sensors array. The peptide sequences were functionalised by two terminal cysteine residues in order to achieve a covalent interaction with the QCM gold surface. A manganese-porphyrin complex and two other pentapeptides, a pentaglutamine (pentapeptide D) and a pentalysine (pentapeptide E), were used as negative control sensors. The QCM sensors (A, B and C) gave a good linearity against different sample concentrations of the 2,3,7,8-tetrachlorinated dibenzo-p-dioxin (TCDD) and a mixture of dioxins. In particular, the selectivity against 2,3,7,8-TCDD was nicely correlated to the estimated binding energy of the receptors calculated by computational modelling. The cross-reactivity of the system was quantified using commercial polychlorinated biphenyls (PCBs) mixtures (dioxin-like compounds).

Lung cancer identification by the analysis of breath by means of an array of non-selective gas sensors

Previous finding shown that the composition of the breath of patients with lung cancer contains information that could be used to detect the disease. These volatiles are mainly alkanes and aromatic compounds. Sensor arrays technology (electronic nose) proved to be useful to screen samples characterised by different headspace composition. Here we investigated the possibility of using an electronic nose to check whether volatile compounds present in expired air may diagnose lung cancer. Breath samples were collected and immediately analysed by an electronic nose. A total of 60 individuals were involved in the experiment. 35 of them were affected by lung cancer, 18 individuals were measured as reference and nine were measured after the surgical therapy. Two individuals were measured twice, before and after the surgical therapy, for a total of 62 measurements. An electronic nose, composed by eight quartz microbalance (QMB) gas sensors, coated with different metalloporphyrins, was used. These sensors show a good sensitivity towards those compounds previously indicated as possible lung cancer markers in breath. The application of a 'partial least squares-discriminant analysis' (PLS-DA) found out a 100% of classification of lung cancer affected patients, 94% of reference was correctly classified. The class of post-surgery patients were correctly individuated in 44% of the cases, while the other samples were classified as healthy references. The alteration of breath composition induced by the presence of lung cancer was enough to allow a complete identification of the sample of diseased individuals. Extended studies are necessary to evaluate the resolution of the method, namely the stage at which the disease may be identified in order to use this instrument for early diagnosis.

Electronic nose analysis of urine samples containing blood

In this paper the possible application of an electronic nose to the analysis of urine is presented. In contrast with the conventional applications of sensors and biosensors operating in liquid, the approach discussed here makes use of gas sensors performing an analysis of the headspace. The application deals with urine samples from patients affected by kidney diseases; some of the samples contained traces of blood. Results show the possibility of distinguishing the samples containing blood from the others, and a linear correlation between the first three principal components and the blood content was found. Furthermore, the electronic nose matched with a suitable neural network showed good performance in measuring the pH and the specific weight of the samples.