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The volatilome profile of some biofluids (blood, urine, and human semen) identified by Solid-Phase Microextraction–Gas Chromatography/Mass Spectrometry (SPME-GC/MS) and collected from young men living in two high-pollution areas in Italy, i.e., Land of Fires and Valley of Sacco River, have been coupled to sperm parameters obtained by spermiogram analysis to build general multiple regression models. Panels of volatile organic compounds (VOCs) have been selected to optimize the models and used as predictive variables to estimate the different sperm quality parameters (sperm cell concentration, total and progressive motility/immotile cells, total/head/neck/tail morphology anomalies, semen round cell concentration). The results of the multiple linear regression models based on the different subgroups of data joining VOCs from one/two or three biofluids have been compared. Surprisingly, the models based on blood and urine VOCs have allowed an excellent estimate of spermiogram values, paving the way towards a new method of indirect evaluation of semen quality and preventive screening. The significance of VOCs in terms of toxicity and dangerousness was discussed with the support of chemical databases available online. [ABSTRACT FROM AUTHOR]

In this paper, a wireless potentiostat code-named ElectroSense, for interfacing of wearable electrochemical biosensors, will be presented. The system is devoted to non-invasive monitoring of glucose in wearable medical applications. Differently from other potentiostats in literature, which use digital-to-analog converters (DACs) as discrete components or integrated in high-end microcontrollers, in this work the pulse width modulation (PWM) technique is exploited through PWM-DAC approach to generate signals. The ubiquitous presence of integrated PWM peripherals in low-end microcontrollers, which generally also integrate analog-to-digital converters (ADCs), enables both the generation and acquisition of read-out signals on a single cheap electronic device without additional hardware. By this way, system's production costs, power consumption, and system's size are greatly reduced with respect to other solutions. All these features allow the system's adoption in wearable healthcare Internet-of-things (IoT) ecosystems. A description of both the sensing technology and the circuit will be discussed in detail, emphasizing advantages and drawbacks of the PWM-DAC approach. Experimental measurements will prove the efficacy of the proposed electronic system for non-invasive monitoring of glucose in wearable medical applications. [ABSTRACT FROM AUTHOR]

This paper presents an integrated circuit for time-based electrical impedance spectroscopy (EIS) of sensors. The circuit exploits maximum-length sequences (MLS) in order to perform a broadband excitation of the sensors under test. Therefore, the measured time-domain EIS is obtained by cross-correlating the input with the output of the analog front end (AFE). Unlike the conventional digital approach, the cross-correlation operation is performed in the analog domain. This leads to a lower RMS error in the measured time-domain EIS since the signal processing is not affected by the quantization noise of the analog-to-digital converter (ADC). It also relaxes the sampling frequency of the ADC leading, along with the lack of random access memory (RAM) usage, to a reduced circuit complexity. Theoretical concepts about the circuit's design and operation are presented, with an emphasis on the thermal noise phenomenon. The simulated performances are shown by testing a sensor's equivalent model composed of a 50 kΩ resistor in parallel with a 100 p F capacitor. A time-based EIS output of 255 points was obtained with a maximum tested frequency of 500 k Hz and a simulated RMS error of 0.0177% (or 177 ppm). [ABSTRACT FROM AUTHOR]

A novel and low-cost framework for food traceability, composed by commercial and proprietary sensing devices, for the remote monitoring of air, water, soil parameters and herbicide contamination during the farming process, has been developed and verified in real crop environments. It offers an integrated approach to food traceability with embedded systems supervision, approaching the problem to testify the quality of the food product. Moreover, it fills the gap of missing low-cost systems for monitoring cropping environments and pesticides contamination, satisfying the wide interest of regulatory agencies and final customers for a sustainable farming. The novelty of the proposed monitoring framework lies in the realization and the adoption of a fully automated prototype for in situ glyphosate detection. This device consists of a custom-made and automated fluidic system which, leveraging on the Molecularly Imprinted Polymer (MIP) sensing technology, permits to detect unwanted glyphosate contamination. The custom electronic mainboard, called ElectroSense, exhibits both the potentiostatic read-out of the sensor and the fluidic control to accomplish continuous unattended measurements. The complementary monitored parameters from commercial sensing devices are: temperature, relative humidity, atmospheric pressure, volumetric water content, electrical conductivity of the soil, pH of the irrigation water, total Volatile Organic Compounds (VOCs) and equivalent CO 2 . The framework has been validated during the olive farming activity in an Italian company, proving its efficacy for food traceability. Finally, the system has been adopted in a different crop field where pesticides treatments are practiced. This has been done in order to prove its capability to perform first level detection of pesticide treatments. Good correlation results between chemical sensors signals and pesticides treatments are highlighted. [ABSTRACT FROM AUTHOR]

This paper presents a fast high-linearity low-power versatile interface circuit for MOX gas sensor. The device is based on a resistance-to-frequency converter with digital output, featuring 128dB-Dynamic Range with 8-bit accuracy. This is achieved with a constant measurement time of 250ms (i.e. with 4Hz output data-rate). The ASIC covers the $100\Omega $ -1M $\Omega $ resistive range, with large programmability for the sensor resistance values. Large linearity (no glitches) is achieved with a single-scale measurement arrangement. The design effort reduces the power consumption to fit portable devices applications. The interface is realized in a standard CMOS 130nm technology with an area of $125000~\mu \text{m}^{2}$ and $450~\mu \text{A}$ -Total Current from a single 1.5V supply. Electrical and chemical measurements with a SnO2 sensor validate the results, showing high minimum CO detection capability in ambient air of 5ppm. [ABSTRACT FROM AUTHOR]

Abstract: Cigarette smoking harms nearly every organ of the body and causes many diseases. The analysis of exhaled breath for exogenous and endogenous volatile organic compounds (VOCs) can provide fundamental information on active smoking and insight into the health damage that smoke is creating. Various exhaled VOCs have been reported as typical of smoking habit and recent tobacco consumption, but to date, no eligible biomarkers have been identified. Aiming to identify such potential biomarkers, in this pilot study we analyzed the chemical patterns of exhaled breath from 26 volunteers divided into groups of nonsmokers and subgroups of smokers sampled at different periods of withdrawal from smoking. Solid‐phase microextraction technique and gas chromatography/mass spectrometry methods were applied. Many breath VOCs were identified and quantified in very low concentrations (ppbv range), but only a few (toluene, pyridine, pyrrole, benzene, 2‐butanone, 2‐pentanone and 1‐methyldecyclamine) were found to be statistically significant variables by Mann–Whitney test. In our analysis, we did not consider the predictive power of individual VOCs, as well as the criterion of uniqueness for biomarkers suggests, but we used the patterns of the only statistically significant compounds. Probit prediction model based on statistical relevant VOCs‐patterns showed that assessment of smoking status is heavily time dependent. In a two‐class classifier model, it is possible to predict with high specificity and sensitivity if a subject is a smoker who respected 1 hour of abstinence from smoking (short‐term exposure to tobacco) or a smoker (labelled "blank smoker") after a night out of smoking (long‐term exposure to tobacco). On the other side, in our study "blank smokers" are more like non‐smokers so that the two classes cannot be well distinguished and the corresponding prediction results showed a good sensitivity but low selectivity. [ABSTRACT FROM AUTHOR]

Smart Breath Analyzers were developed as sensing terminals of a telemedicine architecture devoted to remote monitoring of patients suffering from Chronic Obstructive Pulmonary Disease (COPD) and home-assisted by non-invasive mechanical ventilation via respiratory face mask. The devices based on different sensors (CO2/O2 and Volatile Organic Compounds (VOCs), relative humidity and temperature (R.H. & T) sensors) monitor the breath air exhaled into the expiratory line of the bi-tube patient breathing circuit during a noninvasive ventilo-therapy session; the sensor raw signals are transmitted pseudonymized to National Health Service units by TCP/IP communication through a cloud remote platform. The work is a proof-of-concept of a sensors-based IoT system with the perspective to check continuously the effectiveness of therapy and/or any state of exacerbation of the disease requiring healthcare. Lab tests in controlled experimental conditions by a gas-mixing bench towards CO2/O2 concentrations and exhaled breath collected in a sampling bag were carried out to test the realized prototypes. The Smart Breath Analyzers were also tested in real conditions both on a healthy volunteer subject and a COPD suffering patient. [ABSTRACT FROM AUTHOR]