Beatrice Julia Lotesoriere

It is well known that gas sensor responses are affected by the presence of humidity in the analyzed gas. This is particularly true when dealing with biological fluid samples, whose high moisture content interferes with the adsorption of the trace volatile organic compounds (VOCs) on the sensors’ active layer. To address this challenge, this study focuses on designing and testing a novel sampling system for the dehumidification of biological fluid headspace to be characterized by an electronic nose (e-Nose). Such a system, based on the use of disposable polymeric sampling bags purged with dry air, exploits the polymers’ permeability to water vapor to reduce sample humidity. Tested materials included Nalophan^TM (20 μm), high-density polyethylene (HDPE, 8, 9, 10 and 11 μm), low-density polyethylene (LDPE, 12 and 50 μm), and biodegradable polyester (Bio-PS, 15 μm). First, dehumidification performance was characterized as a function of dry air flow rate and film type. A purge of 1 L/min accelerated the sample humidity removal compared to passive storage of bags from >2 h to TM dryer. Comparison was based on the e-Nose’s ability to discriminate between pooled vs. spiked samples and reveal real-life metabolomic changes. Nalophan^TM bags and Nafion^TM dryer provided the highest VOC fingerprint to support discrimination by the e-Nose, while Bio-PS provided the fastest sample dehumidification. The proposed bag-based system offers a cost-effective, disposable, and contamination-free solution to humidity interference in e-Noses.

Electronic noses (E-Noses) equipped with metal-oxide semiconductor (MOS) sensors are promising tools for non-invasive medical diagnostics. Their adoption in clinical practice, however, is limited—among others—by sensor variability across devices, which makes individual calibration necessary. This study presents an approach for the development of a calibration transfer (CT) methodology for urine headspace analysis, involving the design and realization of a dedicated experimental setup and protocol. Partial least squares-discriminant analysis (PLS-DA) models were trained on human urine samples enriched with selected biomarkers to simulate pathological states. Models from a reference (“master”) device were transferred to other (“slave”) units in multiple master–slave configurations using Direct Standardization (DS). To overcome the variability of human urine, synthetic urine recipes were formulated to mimic sensor responses and serve as reproducible transfer samples. Several strategies for selecting transfer samples were evaluated, including the Kennard–Stone algorithm, a DBSCAN-based approach, and random selection. Without CT, classification accuracy on slave devices decreased markedly (37–55%) compared to the master’s performance (79%), whereas applying DS with synthetic standards restored accuracy to 75–80%. These results demonstrate that combining reproducible synthetic standards with DS enables effective model transfer across E-Noses, reducing calibration requirements and supporting their broader applicability in medical diagnostics.

Waste treatment plants (WTPs) often generate odours that may cause nuisance to citizens living nearby. In general, people are becoming more sensitive to environmental issues, and particularly to odour pollution. Instrumental Odour Monitoring Systems (IOMSs) represent an emerging tool for continuous odour measurement and real-time identification of odour peaks, which can provide useful information about the process operation and indicate the occurrence of anomalous conditions likely to cause odour events in the surrounding territories. This paper describes the implementation of two IOMSs at the fenceline of a WTP, focusing on the definition of a specific experimental protocol and data processing procedure for dealing with the interferences of humidity and temperature affecting sensors’ responses. Different approaches for data processing were compared and the optimal one was selected based on field performance testing. The humidity compensation model developed proved to be effective, bringing the IOMS classification accuracy above 95%. Also, the adoption of a class-specific regression model compared to a global regression model resulted in an odour quantification capability comparable with those of the reference method (i.e., dynamic olfactometry). Lastly, the validated models were used to process the monitoring data over a period of about one year.

The most recent Best Available Techniques (BAT) for waste treatment plants indicate odour as environmental pollutants to be monitored and controlled. In Italy, the prescription of permanent installations of electronic noses (or Industrial Odour Monitoring Systems - IOMS) in the environmental permit of waste treatment plants (WTPs) is becoming more and more frequent. IOMS are intended to provide a real-time estimation of the odour concentration at the fenceline of the plant. Although this type of IOMS application is becoming very common, it is far from being state-of-the-art. In this context, this paper describes a research project aimed at the implementation at the fenceline of a WTP of an IOMS network, comprising two IOMS and a meteorological station. The paper focuses on presenting the experimental procedure involved for training IOMS and verifying their performance in the field. 5 olfactometric campaigns were carried out at the plant to build IOMS calibration models aimed at WTP odours detection, classification, and quantification. Results of field performance testing proved, with classification accuracies above 95% achieved by both IOMS, a very good capability of properly trained IOMS to recognize WTP odours. Moreover, they pointed out a great agreement between the estimations of the odour concentration provided by the IOMS and the odour concentration assessed by dynamic olfactometry, thereby boosting the research in this field. Finally, the paper reports the results of 1-year monitoring at the WTP with the purpose of evaluating the possibility to define an alarm threshold for the odour concentration.

The application of Instrumental Odor Monitoring Systems (IOMS) for odor concentration estimation in wastewater treatment plants remains a challenge. We present the optimization of a heterogeneous gas sensor array mounted on a small drone to be used in dynamic conditions. The proposed method is based on the use of feature selection during the estimation of the best calibration model. The results show that the selection of an optimal sensor array and the proper time window decreases the multiplicative error a 25%.

The realisation of an electronic nose network for the monitoring of the odour concentrations at the fenceline of a waste treatment plant has been investigated. One of the main challenges for instrumental environmental monitoring concerns the interference of the daily variability of humidity and temperature of the ambient air to which sensors are exposed. In this study, we propose a solution to overcome this problem, implementing a specific normalisation pretreatment into the data processing procedure. The preliminary results obtained show a better classification and quantification performance whenever humidity model compensation is applied.

Electronic noses (e-noses) are devices based on combining different gas sensors’ responses to a given sample for identifying specific odor fingerprints. In recent years, this technology has been considered a promising novel tool in several fields of application, but several issues still hamper its widespread use. This review paper describes how some physical confounding factors, such as temperature, humidity, and gas flow, in terms of flow direction and flow rate, can drastically influence gas sensors’ responses and, consequently, e-nose results. Among the software and hardware approaches adopted to address such issues, different hardware compensation strategies proposed in the literature were critically analyzed. Solutions related to e-nose sensors’ modification, design and readout, sampling system and/or chamber geometry design were investigated. A trade-off between the loss of volatile compounds of interest, the decrease of sensors’ sensitivity, and the lack of fast responses need to be pointed out. The existing body of knowledge suggests that the e-nose design needs to be highly tailored to the target application to exploit the technology potentialities fully and highlights the need for further studies comparing the several solutions proposed as a starting point for the application-driven design of e-nose-based systems.

Prostate cancer (PCa) represents the cause of the second highest number of cancer-related deaths worldwide, and its clinical presentation can range from slow-growing to rapidly spreading metastatic disease. As the characteristics of most cases of PCa remains incompletely understood, it is crucial to identify new biomarkers that can aid in early detection. Despite the prostate-specific antigen serum (PSA) levels, prostate biopsy, and imaging representing the actual gold-standard for diagnosing PCa, analyzing volatile organic compounds (VOCs) has emerged as a promising new frontier. We and other authors have reported that highly trained dogs can recognize specific VOCs associated with PCa with high accuracy. However, using dogs in clinical practice has several limitations. To exploit the potential of VOCs, an electronic nose (eNose) that mimics the dog olfactory system and can potentially be used in clinical practice was designed. To explore the eNose as an alternative to dogs in diagnosing PCa, we conducted a systematic literature review and meta-analysis of available studies. PRISMA guidelines were used for the identification, screening, eligibility, and selection process. We included six studies that employed trained dogs and found that the pooled diagnostic sensitivity was 0.87 (95% CI 0.86–0.89; I², 98.6%), the diagnostic specificity was 0.83 (95% CI 0.80–0.85; I², 98.1%), and the area under the summary receiver operating characteristic curve (sROC) was 0.64 (standard error, 0.25). We also analyzed five studies that used an eNose to diagnose PCa and found that the pooled diagnostic sensitivity was 0.84 (95% CI, 0.80–0.88; I², 57.1%), the diagnostic specificity was 0.88 (95% CI, 0.84–0.91; I², 66%), and the area under the sROC was 0.93 (standard error, 0.03). These pooled results suggest that while highly trained dogs have the potentiality to diagnose PCa, the ability is primarily related to olfactory physiology and training methodology. The adoption of advanced analytical techniques, such as eNose, poses a significant challenge in the field of clinical practice due to their growing effectiveness. Nevertheless, the presence of limitations and the requirement for meticulous study design continue to present challenges when employing eNoses for the diagnosis of PCa.

Odors are typically released into the atmosphere as diffuse emissions from area and volume sources, whose detailed quantification in terms of odor emission rate is often hardly achievable by direct source sampling. Indirect methods, involving the use of micrometeorological methods in order to correlate downwind concentrations to the emission rates, are already mentioned in literature, but rarely found in real applications for the quantification of odor emissions. The instrumentation needed for the development of micrometeorological methods has nowadays become accessible in terms of prices and reliability, thus making the implementation of such methods to industrial applications more and more interesting. For this reason, this work aims to provide an overview of micrometeorological methods and investigate their effective applicability to odors, thereby providing a short description of the physics related to such methods and analyzing the relevant scientific literature. The theoretical basis of these methods is presented, and their advantages and disadvantages are discussed. Moreover, their applicability to the estimation of odor emissions is discussed by providing some suggestions about the suitable ways to evaluate the most critical parameters needed for the calculation of the odor emission rate.