Lonestar VOC Analyzer
A non-invasive, easy to use analyzer for the detection of disease biomarkers in clinical samples
- Rapid, sensitive and selective VOC analyzer
- Untargeted discovery and targeted analysis of disease biomarkers
- Cost-effective and easy to use for both clinicians and researchers
Overview
Lonestar Disease Biomarker Detection
Volatile organic compounds (VOCs) are the end product of metabolic processes in the body that are related to diseases such as colon cancer, coeliac disease and tuberculosis. Using FAIMS technology, Lonestar can analyse clinical samples including urine, stool, blood, sweat and breath for the detection of disease biomarkers related to infections, inflammatory diseases and cancers. Lonestar is a portable, self contained unit that can provide rapid and detailed sample analysis, making it ideal for use by researchers and clinicians. Our customers include GSK, Samsung, NHS among many others.
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Highly sensitive
High sensitivity with part per billion detection levels combined with inlet control for high dynamic range
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Rapid screening
Ideal for rapid screening of samples for diagnosis
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Highly versatile
Compatible with various clinical samples - breath, urine, stool, blood, sweat, sputum
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Cost-effective
Cost-effective and easy to use for both clinicians and researchers
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Targeted Analysis
Selective screening for target compounds
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Sensors guided stability
Integrated temperature, flow and humidity sensors for stable, closed-loop operation
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Connectivity
Network and wireless* connectivity for remote monitoring and operation
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Easy integration
Easy integration of other sensor data and control of third party systems
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Powerful software
Powerful custom software for data visualisation, real-time control and offline analysis
Have a question about how to use the Lonestar VOC Analyzer in your own research?
Contact our team or view our support portal
How it works
Detecting early signs of disease through unique chemical fingerprints
Different diseases have unique chemical fingerprints that can be detected by the analysis of gas-phase volatile organic compounds (VOCs) produced by clinical samples such as urine, stool, blood, saliva, sweat. Disease related VOCs are also present in and detectable in exhaled breath. The detection of these specific disease biomarkers can provide early diagnosis of cancers, infections and easy monitoring of chronic inflammatory conditions.
Learn about FAIMS technology-
100.00 sof VOCs are present on our breath every time we breathe
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4.00 yearsearlier diagnosis of cancer becomes a reality
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1000.00 xOwlstone's FAIMS is considerably smaller than existing technologies
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20000.00lives that the LuCID project aims to save by 2020
We have worked with a number of eNose platforms for medical breath research and we have found Owlstone’s FAIMS technology and Lonestar instrument to provide very good accuracy.
We have been using FAIMS for almost five years and have found it able to non-invasively detect a broad range of diseases, including cancer, with high sensitivity and specificity
Research case studies
Lonestar's real-world applications
Research teams around the world have already discovered the unique potential of FAIMS. Published studies cover the spectrum from cancer (bowel/colorectal) to inflammatory diseases (such as IBD and Celiac disease) to infectious diseases (C. difficile).
Breath Biopsy® Complete Guide - VOCs as Biomarkers for Disease
DOWNLOAD FREE EBOOK
Integrations
Lonestar integrates with other Owlstone products and services
Data analysis
Open data format and off the shelf multi-variate analysis tools
Owlstone Medical work with new FAIMS users to develop sample and data analysis methodologies. Lonestar uses an open data format, allowing users to also use off the shelf multi-variate analysis tools. Raw FAIMS spectra are produced by Lonestar from the analysis of clinical samples. The plots to the right are FAIMS spectra collected for human a) stool b) urine and c) breath. FAIMS spectra are multi-dimensional and extremely rich in information, so typically techniques such as discrete wavelet transforms or principal component analysis are required to detect the distinguishing features of those patients suffering from specific illnesses vs. spectra from healthy individuals.
A classification algorithm is then developed and applied to the reduced data set. This algorithm tests relevant features of the data, and assigns each sample to the disease or disease-free category. The form of the classifier depends upon the data-set, but random forest, sparse logistic regression and support vector machines have all been successfully deployed. The classifier is then ideally tested on a new set of samples, to externally validate the sensitivity and specificity.
Read about the applications of FAIMS in medical diagnostics
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Technology
Field Asymmetric Ion Mobility Spectrometry
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Detection Mode
Positive and negative ions
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Sample Input
Ambient, Headspace
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Inlet / Outlet
1/8 Swagelok compression fittings
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Analyte Range
Volatile Organic Compounds
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Dynamic Range
User adjustable inlet dilution for ppb - %
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Instrumental Air
Integrated and easy to replace
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Max Heater Temperature
70C
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Humidity Range
0% - 95%
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Instrument Sensors
Temperature, humidity, flow and pressure
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Inputs
Inbuilt tracker ball
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Output
Real-time chemical spectra and stored data
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Computer
Inbuilt PC running Windows XP
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Memory
4gb internal
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Comms
USB, RJ45
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Software
Custom online control software
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AC Inputs
120/240V AC
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Dimensions
383 x 262 x 195 mm
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Weight
7.8kg