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The aim of this research was to assess the techniques and the materials used to realize the canvas painting Santa Chiara, from the Santa Caterina d’Alessandria Monastery and to verify the state of preservation, the possible decay processes and previous restorations. Consequently, an integrated analytical approach based on the use of non-invasive (diffused, indirect and ultraviolet light investigations) and micro-invasive techniques (optical microscopy, OM, scanning electron microscopy, SEM-EDX, Fourier- transform infrared spectroscopy FT-IR spectroscopy) were used. The oil painting on canvas dated XVI century: data suggests traditional painting technique, characterized by two priming preparatory layers (ammannitura) on a linen canvas and a traditional pigment’s palette. The painting was probably restored at the XIX century as revealed by the insertions of painted canvas in the upper areas and by the replacement of the original framework.

The incremented uptake provided by time-lapse microscopy in Organ-on-a-Chip (OoC) devices allowed increased attention to the dynamics of the co-cultured systems. However, the amount of information stored in long-time experiments may constitute a serious bottleneck of the experimental pipeline. Forward long-term prediction of cell trajectories may reduce the spatial–temporal burden of video sequences storage. Cell trajectory prediction becomes crucial especially to increase the trustworthiness in software tools designed to conduct a massive analysis of cell behavior under chemical stimuli. To address this task, we transpose here the exploitation of the presence of “social forces” from the human to the cellular level for motion prediction at microscale by adapting the potential of Social Generative Adversarial Network predictors to cell motility. To demonstrate the effectiveness of the approach, we consider here two case studies: one related to PC-3 prostate cancer cells cultured in 2D Petri dishes under control and treated conditions and one related to an OoC experiment of tumor-immune interaction in fibrosarcoma cells. The goodness of the proposed strategy has been verified by successfully comparing the distributions of common descriptors (kinematic descriptors and mean interaction time for the two scenarios respectively) from the trajectories obtained by video analysis and the predicted counterparts.

Organs On a Chip (OOCs) represent a sophisticated approach for exploring biological mechanisms and developing therapeutic agents. In conjunction with high-quality time-lapse microscopy (TLM), OOCs allow for the visualization of reconstituted complex biological processes, such as multi-cell-type migration and cell–cell interactions. In this context, increasing the frame rate is desirable to reconstruct accurately cell-interaction dynamics. However, a trade-off between high resolution and carried information content is required to reduce the overall data volume. Moreover, high frame rates increase photobleaching and phototoxicity. As a possible solution for these problems, we report a new hybrid-imaging paradigm based on the integration of OOC/TLMs with a Multi-scale Generative Adversarial Network (GAN) predicting interleaved video frames with the aim to provide high-throughput videos. We tested the performance of the predictive capability of GAN on synthetic videos, as well as on real OOC experiments dealing with tumor–immune cell interactions. The proposed approach offers the possibility to acquire a reduced number of high-quality TLM images without any major loss of information on the phenomena under investigation.

We describe a novel method to achieve a universal, massive, and fully automated analysis of cell motility behaviours, starting from time-lapse microscopy images. The approach was inspired by the recent successes in application of machine learning for style recognition in paintings and artistic style transfer. The originality of the method relies i) on the generation of atlas from the collection of single-cell trajectories in order to visually encode the multiple descriptors of cell motility, and ii) on the application of pre-trained Deep Learning Convolutional Neural Network architecture in order to extract relevant features to be used for classification tasks from this visual atlas. Validation tests were conducted on two different cell motility scenarios: 1) a 3D biomimetic gels of immune cells, co-cultured with breast cancer cells in organ-on-chip devices, upon treatment with an immunotherapy drug; 2) Petri dishes of clustered prostate cancer cells, upon treatment with a chemotherapy drug. For each scenario, single-cell trajectories are very accurately classified according to the presence or not of the drugs. This original approach demonstrates the existence of universal features in cell motility (a so called “motility style”) which are identified by the DL approach in the rationale of discovering the unknown message in cell trajectories.

High-throughput phenotyping is becoming increasingly available thanks to analytical and bioinformatics approaches that enable the use of very high-dimensional data and to the availability of dynamic models that link phenomena across levels: from genes to cells, from cells to organs, and through the whole organism. The combination of phenomics, deep learning, and machine learning represents a strong potential for the phenotypical investigation, leading the way to a more embracing approach, called machine learning phenomics (MLP). In particular, in this work we present a novel MLP platform for phenomics investigation of cancer-cells response to therapy, exploiting and combining the potential of time-lapse microscopy for cell behavior data acquisition and robust deep learning software architectures for the latent phenotypes extraction. A two-step proof of concepts is designed. First, we demonstrate a strict correlation among gene expression and cell phenotype with the aim to identify new biomarkers and targets for tailored therapy in human colorectal cancer onset and progression. Experiments were conducted on human colorectal adenocarcinoma cells (DLD-1) and their profile was compared with an isogenic line in which the expression of LOX-1 transcript was knocked down. In addition, we also evaluate the phenotypic impact of the administration of different doses of an antineoplastic drug over DLD-1 cells. Under the omics paradigm, proteomics results are used to confirm the findings of the experiments.

Cell-cell interactions are an observable manifestation of underlying complex biological processes occurring in response to diversified biochemical stimuli. Recent experiments with microfluidic devices and live cell imaging show that it is possible to characterize cell kinematics via computerized algorithms and unravel the effects of targeted therapies. We study the influence of spatial and temporal resolutions of time-lapse videos on motility and interaction descriptors with computational models that mimic the interaction dynamics among cells. We show that the experimental set-up of time-lapse microscopy has a direct impact on the cell tracking algorithm and on the derived numerical descriptors. We also show that, when comparing kinematic descriptors in two diverse experimental conditions, too low resolutions may alter the descriptors’ discriminative power, and so the statistical significance of the difference between the two compared distributions. The conclusions derived from the computational models were experimentally confirmed by a series of video-microscopy acquisitions of co-cultures of unlabelled human cancer and immune cells embedded in 3D collagen gels within microfluidic devices. We argue that the experimental protocol of acquisition should be adapted to the specific kind of analysis involved and to the chosen descriptors in order to derive reliable conclusions and avoid biasing the interpretation of results.

Localization of malignant sites of asymmetry in mammograms.Combination of Tabar masking procedures.Use of correlation-based structural similarity descriptors.Database-independent validation strategy. Background and objectivesThe analysis of patterns of asymmetry between the left and right mammograms of a patient can provide meaningful insights into the presence of an underlying tumor in its early stage. However, the identification of breast cancer by investigating bilateral asymmetry is difficult to perform due to the indistinct and borderline nature of the asymmetric signs as they appear on mammograms. MethodsIn this study, to increase the positive-predictive value of asymmetry in mammographic screening, a novel computerized approach for the automatic localization of malignant sites of asymmetry in mammograms is proposed. The sites of anatomical correspondence between the right and left regions of each radiographic projection were extracted by means of two bilateral masking procedures, inspired by radiologists criteria in interpreting mammograms and based on the use of detected landmarking structures. Relative variations of spatial patterns of intensity values and of orientations of directional components within each site were quantified by combining multidirectional Gabor filters and indices of structural similarity. The localization of the sites of malignant asymmetry was performed by coupling two quadratic discriminant analysis classifiers, one for each masking procedure, that assigned the likelihood of malignancy to each site of correspondence. ResultsThe performance of the proposed method was assessed on 94 mammographic images from two publicly available databases and containing at least one asymmetric site. Sensitivity, specificity and balanced accuracy levels of 0.83 (0.09), 0.75 (0.06), and 0.79 (0.04), respectively were obtained in the classification of malignant asymmetric sites vs benign/normal sites using cross-validation. In addition, a further blind test on a dataset of Full Field Digital Mammograms achieved levels of sensitivity, specificity, and balanced accuracy of 0.86, 0.65, and 0.75, respectively. ConclusionsThe achieved performance indicates that the proposed system is effective in localizing sites of malignant asymmetry and it is expected to improve computer-aided diagnosis of breast cancer.

This paper illustrates that an electronic nose applied to the measure of the headspace cultures of Salmonella serotypes can discriminate among different strains. Salmonella is one of the main bacteria causing food borne illnesses, this there is a great interest for fast identification methods. Standard detection methods for Salmonella food contamination have limited performance in terms of costs and rapidity. Electronic noses are among the technologies that supposedly can improve the management of Salmonella infections. Preliminary experiments are discussed in this paper; results confirm that electronic noses offer a viable technology for rapid identification of bacteria.

This work is aimed to detect degradation of different food matrices through the use of a miniaturized (USB-stick sized) electronic nose. This electronic nose is based on metal oxide gas sensors and works according to temperature profile protocols. The tested foods were fish or chicken kept at room temperature. The obtained results show that this device is able to discriminate the degradation of both foods at the safety threshold.

Electronic nose (EN) can represent a useful tool in agrifood applications, thanks to its capability to identify meaningful parameters based on food-odor.This work is focused on the set up of a measurement protocol to track microbial contamination development in fish under environmental conditions by means of a miniaturized, fast, low-power consumption, EN based on two micro machined metal oxide gas sensors integrated into a USB controlled device. The sensors worked with custom temperature profile protocols and provided real time information.

This paper presents an equivalent electric circuit model that describes adsorption-desorption processes occurring on bio and chemical sensor surfaces under the Langmuir hypothesis and considers the following practical case: the pressure or concentration of the particles in the test chamber is not perturbed by these processes and keeps its initial value, as in the cases of relatively high pressure or concentration values with zero molecular flow, or in the presence of a molecular flow at any pressure or concentration value. It is also pointed out that the equivalent circuit for Langmuir adsorption is similar to the circuit proposed for enzymatic reactions. Even if this work essentially covers theoretic aspects, a way is suggested for the possible experimental determination of both adsorption-desorption parameters and adsorption-desorption site density.

Two phosphorous(V) corrole ligands, [5,10,15-pentafluorophenyl corrole] phosphorous (V), (PFCorr), and [10-(4-trimhetylsilyphenyl)-5,15-dimesityl-corrole] phosphorous (V) (PCorr) were synthesized and tested as nitrite-sensitive ionophore. Fluorimetry studies on ligand sensitivity towards anions were carried out first in solution and then inside polymeric membrane optodes. The selective ligand fluorescence quenching was registered upon addition of growing amount of NO 2 −-ions. The influence on the PFCorr optode response of the lipophilic sites functionalization was investigated. The sensor will be tested in natural (fresh and seawater) and fish farm waters in order to detect the effectiveness of nitrite assessment at recommended concentrations levels (less than 0.3 mg/L).

Real-time and comfortable monitoring of the human breathing could allow identifying anomalies in the rhythm and waveform to be correlated with several pathologic disorders of respiratory and cardiovascular systems. A wireless sensor based on a flexible Kapton substrate, suitable to be stuck over the face skin like a plaster and provided with a graphene-oxide (GO) electrode, is here proposed for application to the monitoring of the moisture emitted during inhalations and exhalations. The GO-based electrode increases its dc resistance when exposed to humidity with a sensitivity of $60~\Omega $ /RH. The device is compatible with the radiofrequency identification (RFID) standard in the UHF band. When used in battery-less mode, it can be read up to 60 cm. The RFID sensor has been successfully experimented in a measurement campaign involving 10 volunteers asked to reproduce a set of predefined normal and pathological breaths. The resulting resistance traces permit to well clusterize the breath patterns with respect to the respiration rate (extracted by a fast Fourier transform) and to the average peak variation of the sensor’s resistance with an accuracy close to 90%.

Thin film deposition of porphyrinoids is a critical step for the development of reliable chemical sensors. The binding properties, in liquid phase, of these compounds should be in fact preserved as much as possible in the solid state, to allow an efficient recognition of target analytes. However, the solid-state arrangements of these macrocycles could offer additional binding mechanisms that exceed those possible to the single molecules allowing to even enhance the superb binding versatility of porphyrinoids. In this manuscript, we have reviewed mostly our approach for the thin film deposition of porphyrinoids, going from the simple, noncontrolled techniques to the preparation of composite inorganic/organic materials. All these opportunities drive the aggregation of porphyrinoids by supramolecular interactions and the possibility to merge their properties with those of inorganic materials open the way to an almost unlimited possibility to prepare optimized sensing layers.

An opto-electronic tongue, prepared using porphyrins, pH indicators, and their mixtures, has been tested for the analysis of toxic compounds in potable water. The color changes of sensitive dyes immersed in a water solution containing the target analytes were measured with an optical platform made by four LEDs (as light sources) and a digital camera (detector). We demonstrate that blends of dyes might be endowed with sensing properties wider than those of the single constituents, enabling the identification of a range of toxic compounds at concentrations smaller than 10(-6) mol/L. Furthermore, the use of the reporters in a sensor array configuration allows for the identification of the compounds disregarding their concentration. (C) Published by Elsevier Ltd.

In this work, we illustrate an autonomous and real-time reconfigurable classifier. The algorithm starts from a non-adaptive classifier and evolves during the routine operation of sensors providing a dynamic optimization of the feature selection and refinement of classes’ distribution. The model has been tested on an experimental dataset and the results show that the algorithm may improve the resilience of classifiers in case of drifting and/or faulty sensors. The outcome of this studied case suggests that the algorithm might be able to enhance long-term performance almost independently from which classification model is considered.

Breath analysis as well as the detection of abnormal respiratory rate can provide important feedback on people's health status. In this contribution we propose a wearable radiofrequency identification (RFID) epidermal device consisting of a flexible open loop antenna, that enables an easy adhesion on the user's skin, coupled with a graphene oxide-based sensor sensitive to the water vapour. The graphene oxide sensor was characterised in reference conditions, while the communication performance of the antenna was estimated by electromagnetic simulations over a human head phantom as well as by means of measurements on a volunteer. Finally, the whole system was experimentally demonstrated to be capable of comfortably detecting the inhalation/exhalation cycles and abnormal patterns of respiration like the apnea and tachypnea conditions by measuring the changes in graphene oxide resistance.

Background Synovial fluid (SF) receives protein contribution from the tissue around: cartilages, synovial membranes and bones. The presence of inflammation and oxidative stress alters the its chemical composition. In particular, inflammation modulates the release of volatile organic compounds (VOCs) that are the product of reactive oxygen species and free radicals excreted by mithocondria during oxydative stress (1). Articular inflammation plays a major role both in Osteoarthritis (OA) and Rheumatoid arthritis (RA), thus, the identification of specifics VOCs associated with inflammation in the SF may represent a suitable procedure to facilitate a diagnosis and a better characterization of these diseases. E-noses are versatile instruments based on arrays of partially selective gas sensors system that do not provide specific information about the individual molecules but can detect a large spectrum of VOCs to provide a discrimination among samples classified according to their chemical composition (VOC pattern) (2). Objectives Aim of this study was to prospectively investigate whether analysis of volatile compounds (VOCs) emitted from knee synovial fluid can identify differences between osteoarthritis (OA) and rheumatoid arthritis (RA). Methods VOCs Profile emitted by knee synovial fluid of 10 OA patients was compared with that of 25 RA patients using gas chromatography and mass spectrometry (GC-MS) and a gas sensor array (electronic nose) made by an ensemble of metalloporphyrins coated quartz microbalances. Patients9 data are summarized in Table 1. Data were analyzed by principal component analysis (PCA), partial least squares discriminant analysis (PLSDA). Permutation analysis and area under curve (AUROC) of receiver operating characteristics (ROC) curves were used to characterize the classifier performance. Results GC-MS analysis identified 55 VOCs in the headspace of synovial fluids. The ANOVA analysis of the relative abundance indicated five VOCs significantly different between OA and RA. The abundance of five compounds allowed to identify OA with respect to RA with an accuracy of 82% (sensitivity: 0.90, specificity: 0.80, AUROC=0.92, 99.7% CI). The signals of the electronic nose sensors allowed to classify the studied subjects in OA or RA. In particular, OA patients could be distinguished from that of RA patients with an accuracy of 100% (sensitivity: 1, specificity: 1, AUROC=1, 99.9% CI).) (Figure 1). However, no single VOC was specific for OA or RA. Conclusions This study shows that OA and RA patients exhibit qualitative and quantitative differences in the chemical compositions of knee synovial fluid. These differences may be attributed to five volatile compounds and can be detected by an electronic nose which may represent a suitable diagnostic tool for diagnosis and characterization of OA vs. RA. References Hakim M, Broza YY, Barash O, Peled N, Phillips M, Amann A, et al. Volatile organic compounds of lung cancer and possible biochemical pathways. Chem Rev. 2012;112:5949–66. Stitzel S, Arnecke M, Walt D. Artificial noses. Ann Rev Biomed Eng. 2011;13:1–25. Disclosure of Interest None declared

Atomic force microscopy is gaining interest as a technique to quantitatively study biological samples in native environment. However, the measuring principles behind may cause the presence of different sources of artifacts and image degradations. In this work, we present an AFM image analysis tool able to recognize morphological alterations in human leukemia cells after 3 h incubation with the antioxidant cerium oxide nanoparticles. To demonstrate the robustness of the approach to a particular artifact called contrast reversal (CR) effect, consisting in an unexpected switching between repulsive and attractive tip regime during scanning, we present a technique to artificially inject the artefact on the image and then apply the tool. Maximum area under the ROC (AUC) curve results of 0.91 (0.11) in the discrimination between exposed and unexposed cells confirm the validity of the approach and its applicability in AFM-based cell studies.

The potential of an electronic nose to discriminate olive oil samples based on their sensory profiles is proposed. The e-nose was constituted by an array of seven quartz crystal microbalance sensors modified with Gold Nanoparticles (GNPs) conjugated to short peptides. Forty olive oil samples headspaces were characterised by headspace solid-phase microextraction gas chromatography-mass spectrometry analysis to evaluate chemical composition; in parallel, they were chemically and sensory evaluated according to European Regulation EEC 2568/91 and amendments and EU Regulation 640/2008. The steady state sensor responses obtained with the e-nose setup were used to evaluate the discrimination properties of the system by principal component analysis and partial least square method. The results of this study provided a promising perspectives for the use of the electronic nose as a low-cost, easy to use and rapid system for the quality control of extra virgin, virgin and lampante (non-edible) olive oil. This system ...

Photoconductivity and gas sensitivity cooperate in porphyrins coated ZnO nanostructures. However, in organic coated semiconductors the former is regulated by a number of mechanisms, involving the charge transfer in the organic layer. Since organic layers are poor conductors these processes are quite slow and the sensor may exhibits a long time drift before to be operative as gas sensor. In this paper we show that under light modulation, the carrier frequency component of the signal is free of drift and it can readily indicate the interaction with volatile compounds.

Sensor networks prompt a great deal of research interest within the computer and analytical sciences. To this regard, one of the most important issues is concerned with the interpretation of data that are collected by different sensors. Due to sensors non-reproducibility, this problem may also persist even when many replicas of the same sensors are considered. In this case additional calibrations may be required to use a common knowledge database. Noteworthy, the same problem arises in case of sensors replacement. In this paper we demonstrate that in case of optical chemical sensors drawing inspiration from the connectivity strategy of the olfactory bulb, this problem can find a straightforward solution when an image sensor is used to measure the optical properties of an extended sensing layer. If the sensing layer is formed by a number of spots of different indicators, it is demonstrated that a common data processing can be applied to any replica of the sensing layer even if the indicators are spotted with different geometries and in different quantities.

none 9 no A systematic study of a series of diaza-18-crown-6 8-hydroxyquinoline (DCHQ) chemosensors, devoted to Mg(II) ion detection, was performed. Functionalization of DCHQ by peripheral substituents allowed the development of novel all-solid-state optodes via inclusion inside PVC-based polymeric films. The influence on the DCHQ-based optode response of the lipophilic sites functionalization and of the nature of the plasticizer, was investigated. Fluorimetric studies on optodes sensitivity towards a number of different metal cations (Ca2+, Na+, K+, Li+, Co2+, Cd2+, Pb2+, Cu2+, Hg2+, Zn2+) and NH4+were carried out. The results demonstrated the suitability of the DCHQ optodes to perform fast monitoring (

Nucleophosmin (NPM1) is a multifunctional nucleolar protein implicated in ribogenesis, centrosome duplication, cell cycle control, regulation of DNA repair and apoptotic response to stress stimuli. The majority of these functions are played through the interactions with a variety of protein partners. NPM1 is frequently overexpressed in solid tumors of different histological origin. Furthermore NPM1 is the most frequently mutated protein in acute myeloid leukemia (AML) patients. Mutations map to the C-terminal domain and lead to the aberrant and stable localization of the protein in the cytoplasm of leukemic blasts. Among NPM1 protein partners, a pivotal role is played by the tumor suppressor Fbw7γ, an E3-ubiquitin ligase that degrades oncoproteins like c-MYC, cyclin E, Notch and c-jun. In AML with NPM1 mutations, Fbw7γ is degraded following its abnormal cytosolic delocalization by mutated NPM1. This mechanism also applies to other tumor suppressors and it has been suggested that it may play a key role in leukemogenesis. Here we analyse the interaction between NPM1 and Fbw7γ, by identifying the protein surfaces implicated in recognition and key aminoacids involved. Based on the results of computational methods, we propose a structural model for the interaction, which is substantiated by experimental findings on several site-directed mutants. We also extend the analysis to two other NPM1 partners (HIV Tat and CENP-W) and conclude that NPM1 uses the same molecular surface as a platform for recognizing different protein partners. We suggest that this region of NPM1 may be targeted for cancer treatment.

Nowadays Toxic Industrial Components (TICs) and Toxic Industrial Materials (TIMs) are one of the most dangerous and diffuse vehicle of contamination in urban and industrial areas. The academic world together with the industrial and military one are working on innovative solutions to monitor the diffusion in atmosphere of such pollutants. In this phase the most common commercial sensors are based on "point detection" technology but it is clear that such instruments cannot satisfy the needs of the smart cities. The new challenge is developing stand-off systems to continuously monitor the atmosphere. Quantum Electronics and Plasma Physics (QEP) research group has a long experience in laser system development and has built two demonstrators based on DIAL (Differential Absorption of Light) technology could be able to identify chemical agents in atmosphere. In this work the authors will present one of those DIAL system, the miniaturized one, together with the preliminary results of an experimental campaign conducted on TICs and TIMs simulants in cell with aim of use the absorption database for the further atmospheric an analysis using the same DIAL system. The experimental results are analysed with standard multivariate data analysis technique as Principal Component Analysis (PCA) to develop a classification model aimed at identifying organic chemical compound in atmosphere. The preliminary results of absorption coefficients of some chemical compound are shown together pre PCA analysis.

The monitoring of the breathing dynamic characteristics, including the presence of biomarkers in exhaled breath, is of growing interest in noninvasive diagnosis of diseases. We describe a wearable radiofrequency identification (RFID) device hosting a flexible antenna suitable for integration into a facemask and a sensor made of graphene oxide sensitive to the humidity variations. The so obtained wearable wireless sensor was characterized in reference conditions and was then experimentally demonstrated to be capable of detecting the inhalation/exhalation cycles and abnormal patterns of respiration like the apnea by measuring the changes in graphene oxide resistance.

Lung cancer diagnosis via breath analysis has to overcome some issues that can be summarized by two crucial points: (1) further developments for more performant breath sampling technologies; (2) discovering more differentiated volatile fingerprints to be ascribed to specific altered biological mechanisms. The present work merges these two aspects in a pilot study, where a breath volume, sampled via endoscopic probe, is analyzed by an array of non-selective gas sensors. Even if the original non-invasive methods of breath analysis has been laid in favour of the endoscopic means, the innovative technique here proposed allows the analysis of the volatile mixtures directly sampled near the tumor mass. This strategy could open the way for a better understanding of the already obtained discrimination among positive and negative cancer cases. The results obtained so far confirm the established discrimination capacity. This allows to discriminate the different subtypes of lung cancer with 75% of correct classification between adenocarcinoma and squamous cell carcinoma. This result suggests that a ‘zoom-in’ on the cancer settled inside the human body can increase the resolution power of key-volatiles detection, allowing the discrimination among different cancer fingerprints. We report this novel technique as a robust support for a better comprehension of the promising results obtained so far and present in literature; it is not to be intended as a replacement for non-invasive breath sampling procedure with the endoscope.

In this paper we study the sensitivity to volatile organic vapors of the surface potential of metalloporphyrins coated ZnO nanorods. The hybrid material has been growth with an original method based on the direct addition of metalloporphyrins to the hydrothermal precursor solution. The resulting film shows a complete coating of the ZnO surface expected to improve the photovoltaic characteristics. The chemical sensitivity of the surface potential has been measured in dark and in white light illumination. For sake of comparison, the hybrid material has been compared with those obtained from a casting coated porphyrin functionalized ZnO nanorods. Results show that the material preparation strongly influences the sensor properties, and in case of the hybrid material the illumination with visible light elicits an increase of the sensor response that is proportional to the ability of the volatile compound to donate electrons.

The observations of natural olfaction led to the evidence that the processing of olfactory receptor signals contributes to properties such as performance stability, concentration invariance, and background suppression. Then, the study of olfactory processing has been regarded as a valuable source of inspiration for sensor arrays. In this work, a neural network architecture, introduced in the past as a model of the olfactory bulb, is used as a pre-processing step to analyse the data of an array of chemical sensors. Surprisingly, besides to enforce gas recognition, the network shows an interesting property of drift rejection. The potentialities of the network are demonstrated with data collected in a long-time experiment. The results provide strong evidence that bio-inspired processing of chemical sensors data actively improve the overall performance.

Gas sensor arrays can be very sensitive to the properties of individual sensors, then even the fault of one of the array element can greatly jeopardize the array performance. Sensor replacement easily achieves the pristine conditions but non reproducibility between sensors of the same series may also require a new calibration. In this paper, a processing strategy aimed at counteracting the necessity of new calibration is introduced. It is based on an ensemble of adaptive classifiers each built with single sensors data. The array prediction is then obtained by a majority voting decision rule based on the outputs of the ensemble of models. The classifier has been tested with an experiment aimed at detecting gases with a drifting gas sensors array. The classifier performance has been compared with a standard classifier.

Fluorimetric and Computer Screen Photoassisted Technique (CSPT) studies on the binding properties of three coumarin pendant arm derivatives of mixed aza-thioether macrocyclic ligands (L1-L3), incorporated into PVC plasticized polymeric membranes, towards a number of different metal cations were carried out. An enhanced selectivity of the ligands towards Hg2+ion was detected. In particular, the DOS plasticized membrane containing L3 and tetrakis(p-chlorophenyl)borate, TpClPB-, as lipophilic anionic additive, displayed a strong fluorescence activation by Hg2+over the wide concentration range of 10-7to 10-3M. The influence of the ratio among ligand and lipophilic additive on the membrane performance was tested. In addition, the CSPT measurements showed that L3 possesses a unique selectivity towards Hg2+ion with respect to several common diverse cations. Moreover CSPT technique permits to perform the fast and low cost monitoring of mercury (II) ions, by application of familiar devices and a chemometric approach.

The analysis of the volatile organic compounds (VOCs) released by bio-systems represents a promising approach for the diagnosis of human diseases including cancers. Experimental results suggested the possibility to detect tumors in vivo through the analysis of released VOCs from different body compartments, such as breath and skin. This paper illustrates the results obtained measuring VOCs mixtures released by well-characterized tumor cells derived from human malignancies by using an array of broadly selective chemical sensors. The patterns of VOCs emitted by two different melanoma tumorigenic cell lines transplanted ad hoc in a mice model were investigated. The recorded sensors signals are compatible with the existence of a distinguishable tumor specific pattern of VOCs evolving during the exponential phase of tumor growth.

Networked films of carbon nanotubes (CNTs) have been grown by CVD technology onto low-cost miniaturized alumina substrates. The sidewalls of the CNTs films have been modified by spray-coating with two different metalloporphyrins (MPPs) consisting of a TetraPhenylPorphyrin coordi-nated by a central metal of zinc (Zn-TPP) and manganese (Mn-TPP) for enhanced sensitivity and tailored specificity. Hazardous gases such as NO2, NH 3, H2S, SO2, and CO have been detected with various responsiveness in the range of concentration from 0.1 to 1000 ppm. The response of the chemiresistors in terms of p-type electrical conductance has been investigated as a function of the thickness of the functionalizing MPPs; and the effect of the temperature ranging from 20 to 150 °C on the sensor response has been addressed as well. A response of the CNT-sensor functionalized by 2 layers of Mn-TPP has been measured as 0.025% towards 0.5 ppm NO 2, at 150 °C.

Sensors are the less durable parts in any chemical sensor system, then the replacement of sensors maintaining hardware and data processing is a necessity in real applications. Though sensors replacement requires a perfect reproducibility of the sensors fabrication. Among the different sensor technologies, those based on optical transduction may offer a straightforward methodology to release the requirement of identical sensor manufacturing. In particular it will be shown here that the use of image sensor provides a surprising parallel with natural olfaction. An important consequence is that the data processing is independent from the geometric arrangement of the sensitive layer. This feature leads to a simple sensor layer replacement and to a prompt arrangement of different sensor systems, even remotely located, into a single data processing system.

In this work, gas sensing properties of monocarboxy tetraphenylporphyrin functionalized ZnO nanorods under different light illumination, were investigated. These studies showed that the sensitivity to triethylamine of porphyrin functionalized ZnO nanorod device are greatly enhanced under illumination with visible light. This photoactivated nanostructure composition has the potential for gas sensor device applications.