Evaluation of a multisensorial system for a rapid preliminary screening of the olive oil chemical compounds in an industrial process

In this study, a sensory system, named BIONOTE, based on gas and liquid analyses was used to analyse the headspace of olive oil samples obtained at the end of the extraction process for a preliminary screening of the volatile and phenolic compounds. Olive oil samples were obtained using different olive paste conditioning systems, including microwave and megasound machines at different processing time. The same olives batch was used for the entire test. BIONOTE showed the ability to discriminate between 64 virgin olive oils originated from different technologies or by using different process parameters, as demonstrated by the partial least square discriminant analysis (PLS-DA) models calculated. The percentage of correct classification in different conditions are in a range from 92.19% to 100%. In addition, the research shown that the multisensorial system can provide a preliminary estimation of some volatile and phenolic compounds concentrations detected by laboratory analysis. Data analysis has been performed using multivariate data analysis techniques: PLS-DA cross validation via leave one out criterion. Future perspectives are to further develop BIONOTE in order to increase the number of detected chemical compounds and finally to include the mathematical models obtained in the BIONOTE microcontroller for a rapid chemical characterization of olive oil in the mill

A Novel Approach for Prostate Cancer Diagnosis using a Gas Sensor Array

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Biosensors for Detection and Monitoring of Joint Infections

The aim of this review is to assess the use of biosensors in the diagnosis and monitoring of joint infection (JI). JI is worldwide considered a significant cause of morbidity and mortality in developed countries. Due to the progressive ageing of the global population, the request for joint replacement increases, with a significant rise in the risk of periprosthetic joint infection (PJI). Nowadays, the diagnosis of JI is based on clinical and radiological findings. Nuclear imaging studies are an option but are not cost-effective. Serum inflammatory markers and the analysis of the aspirated synovial fluid are required to confirm the diagnosis. However, a quick and accurate diagnosis of JI may remain elusive as no rapid and highly accurate diagnostic method was validated. A comprehensive search on Medline, EMBASE, Scopus, CINAH, CENTRAL, Google Scholar, and Web of Science was conducted from the inception to June 2021. The PRISMA guidelines were used to improve the reporting of the review. The MINORS was used for quality assessment. From a total of 155 studies identified, only four articles were eligible for this study. The main advantages of biosensors reported were accuracy and capability to detect bacteria also in negative culture cases. Otherwise, due to the few studies and the low level of evidence of the papers included, it was impossible to find significant results. Therefore, further high-quality studies are required

Neutron Detectors Based Upon Artificial Single Crystal Diamond

This paper reports about state-of-the-art artificial Single Crystal Diamond (SCD) neutron detectors based on a multilayered structure and grown by chemical vapour deposition (CVD) technique. Multilayered SCD detectors covered with a thin layer of 6LiF allow the simultaneous detection of both slow and fast neutrons and can operate in pulse and current mode. These detectors can also be produced with a thin layer of Boron. Application of SCD detectors to neutron detection around fusion tokamak is reported. Some problems related to the processing of the very fast electrical pulse produced by diamond are addressed and the achieved and foreseen development of the processing electronics is reported as well

Differential Detection of Potentially Hazardous Fusarium Species in Wheat Grains by an Electronic Nose

Fungal infestation on wheat is an increasingly grave nutritional problem in many countries worldwide. Fusarium species are especially harmful pathogens due to their toxic metabolites. In this work we studied volatile compounds released by F. cerealis, F. graminearum, F. culmorum and F. redolens using SPME-GC/MS. By using an electronic nose we were able to differentiate between infected and non-infected wheat grains in the post-harvest chain. Our electronic nose was capable of distinguishing between four wheat Fusaria species with an accuracy higher than 80%

Breathomics can discriminate between anti IgE-treated and non-treated severe asthma adults

Rationale: Omalizumab, an anti-IgE monoclonal antibody, is indicated in adults with severe persistent allergic asthma. Exhaled molecular markers can provide phenotypic information in asthma. Objectives: Determine whether adults with severe asthma on omalizumab (anti-IgE+) have a different breathprint compared with those who were not on anti-IgE therapy (anti-IgE-) as assessed by eNoses and gas chromatography/mass spectrometry (GC/MS) (breathomics). Methods: This was a cross-sectional analysis of the U- BIOPRED adult cohort. Severe asthma was defined by IMI-criteria [Bel, Thorax 2011]. Anti-IgE+ patients were on a regular treatment with s.c. omalizumab (150-375 mg) every 2-4 weeks. Exhaled volatile compounds trapped on adsorption tubes were analysed by a centralized eNose platform (Owlstone Lonestar, two Cyranose 320, Comon Invent, Tor Vergata TEN), including a total of 190 sensors, and GC/MS. Recursive feature elimination (http://topepo.github.io/caret/rfe.html) was used for feature selection and random forests, more robust to overfitting, for classification. Results: 9 anti- IgE+ (females/males 2/7, age 52.6±16.3 years, mean±SD, 1/2/6 current/ex/nonsmokers, pre-bronchodilator FEV1 70.6±21.1% predicted value) and 30 anti-IgE- patients (18/12 females/males, age 53.2±14.2 years, 0/16/14 current/ex/nonsmokers, pre-bronchodilator FEV1 59.6±30.7% predicted value) were studied. Conclusions: Preliminary results suggest that breathomics can distinguish between anti-IgE+ and anti-IgE- severe asthma patients

Methodological considerations for large-scale breath analysis studies : lessons from the U-BIOPRED severe asthma project

Methods for breath sampling and analysis require robust quality assessment to minimise the risk of false discoveries. Planning large-scale multi-site breath metabolite profiling studies also requires careful consideration of systematic and random variation as a result of sampling and analysis techniques. In this study we use breath sample data from the recent U-BIOPRED cohort to evaluate and discuss some important methodological considerations such as batch variation and correction, variation between sites, storage and transportation, as well as inter-instrument analytical differences. Based on this we provide a summary of recommended best practices for new large scale multi-site studies

Recent Progress and Next Steps for the MATHUSLA LLP Detector

We report on recent progress and next steps in the design of the proposed MATHUSLA Long Lived Particle (LLP) detector for the HL-LHC as part of the Snowmass 2021 process. Our understanding of backgrounds has greatly improved, aided by detailed simulation studies, and significant R&D has been performed on designing the scintillator detectors and understanding their performance. The collaboration is on track to complete a Technical Design Report, and there are many opportunities for interested new members to contribute towards the goal of designing and constructing MATHUSLA in time for HL-LHC collisions, which would increase the sensitivity to a large variety of highly motivated LLP signals by orders of magnitude.Comment: Contribution to Snowmass 2021 (EF09, EF10, IF6, IF9), 18 pages, 12 figures. v2: included additional endorser

First results on the angular resolution of the ARGO-YBJ detector

We present the first results on the angular resolution of the ARGO-YBJ detector in data taking at the Yangbajing Laboratory (Tibet, P.R. China, 4300 m a.s.l.