Drift compensation on electronic nose data relevant to the monitoring of odorous emissions from a landfill by opls

Now a days electronic noses (e-noses) are often prescribed in the permits of new or existing plants for a continuous monitoring of ambient air quality at receptors or plant fenceline. To do this, the e-nose has to guarantee a stable classification and quantification performance over time. However, the problem of drift (i.e., progressive deviation over time of sensor responses) becomes critical in the case of continuous odour monitoring in the field. Thus, specific strategies for drift compensation need to be defined. This paper focuses on the development a specific drift correction model based on OPLS to mitigate drift effects on e-nose data relevant to three olfactometric campaigns carried out at a landfill over three years. The paper aims to reduce costs associated to the recalibration of the decisional model to be performed before every seasonal e-nose monitoring campaign prescribed in the permit of the landfill. The OPLS model was built on the data collected in the first two campaigns, while data relevant to the most recent campaign were used to test its efficacy in compensating drift. The results achieved pointed out the potentialities of the OPLS model to mitigate drift effects, thereby allowing to extend the applicability of the model developed within an olfactometric campaign to the subsequent ones. The classification performance achieved involving the OPLS correction (i.e., 75%) was considerably higher than the one achieved on non-corrected data (i.e., about 55%)

A novel approach for the non-invasive diagnosis of prostate cancer based on urine odour analysis

The diagnosis of prostate cancer (PCa) still remains difficult mainly due to limits of current screening procedure. The analysis of urine odor represents an opportunity and valid alternative. Trained dogs proved extraordinary capability to recognize PCa-specific volatile-organic compounds. Unfortunately, dogs cannot be introduced in the clinical setting. Thus, the interest in instruments mimicking the canine olfactory system is gaining increasing attention. In this context, we here propose an electronic nose (EN) for the non-invasive diagnosis of PCa and report the results of a blind cohort study to compare EN performance with trained dogs' olfaction

Drift compensation on electronic nose data for non-invasive diagnosis of prostate cancer by urine analysis

Diagnostic protocol for prostate cancer (KP) is affected by poor accuracy and high false-positive rate. The most promising innovative approach is based on urine analysis by electronic noses (ENs), highlighting a specific correlation between urine alteration and KP presence. Although EN could be exploited to develop non-invasive KP diagnostic tools, no study has already introduced EN into clinical practice, most probably because of drift issues that hinder EN scaling up from research objects to large-scale diagnostic devices. This study, proposing an EN for non-invasive KP detection, describes the data processing protocol applied to a urine headspace dataset acquired over 9 months, comprising 81 patients with KP and 41 controls, for compensating the drift. It proved effective in mitigating drift on 1-year-old sensors by restoring accuracy from 55% up to 80%, achieved by new sensors not subjected to drift. The model achieved, on double-blind validation, a balanced accuracy of 76.2% (CI95% 51.9–92.3)

Accuracy of a new electronic nose for prostate cancer diagnosis in urine samples

Objective: To evaluate the accuracy of a new electronic nose to recognize prostate cancer in urine samples. Methods: A blind, prospective study on consecutive patients was designed. Overall, 174 subjects were included in the study: 88 (50.6%) in prostate cancer group, and 86 (49.4%) in control group. Electronic nose performance for prostate cancer was assessed using sensitivity and specificity. The diagnostic accuracy of electronic nose was reported as area under the receiver operating characteristic curve. Results: The electronic nose in the study population reached a sensitivity 85.2% (95% confidence interval 76.1–91.9; 13 false negatives out of 88), a specificity 79.1% (95% confidence interval 69.0–87.1; 18 false positives out of 86). The accuracy of the electronic nose represented as area under the receiver operating characteristic curve 0.821 (95% confidence interval 0.764–0.879). Conclusions: The diagnostic accuracy of electronic nose for recognizing prostate cancer in urine samples is high, promising and susceptible to supplemental improvement. Additionally, further studies will be necessary to design a clinical trial to validate electronic nose application in diagnostic prostate cancer nomograms