Lonestar VOC Analyzer
An easy to use analyzer for the detection of VOC biomarkers in clinical samples
- Rapid, sensitive and selective VOC detection and quantification
- Untargeted discovery and targeted analysis of disease biomarkers
- Cost-effective and easy to use for both clinicians and researchers
Volatile organic compounds (VOCs) are the end product of metabolic processes in the body that are related to diseases. These VOCs travel via the bloodstream, ending up in exhaled breath. Using FAIMS technology, the Lonestar VOC Analyzer can analyze breath samples for the detection of disease biomarkers related to infections, inflammatory diseases and cancers. Our customers include GSK, Samsung, and the UK National Health Service, among many others.
High sensitivity with part per billion detection levels combined with inlet control for high dynamic range
Ideal for rapid screening of samples
Compatible with various clinical samples - breath, urine, stool, blood, sweat, sputum
Cost-effective and easy to use for both clinicians and researchers
Selective screening for target compounds
Sensors guided stability
Integrated temperature, flow and humidity sensors for stable, closed-loop operation
Network and wireless connectivity for remote monitoring and operation
Easy integration of other sensor data and control of third party systems
Powerful custom software for data visualisation, real-time control and offline analysis
How it works
Breath samples are collected using Owlstone’s ReCIVA Breath Sampler, and are captured on Breath Biopsy Cartridges. The cartridges are then placed into a thermal desorption (TD) system, which carries out a controlled heating process to release the captured VOCs into the Lonestar VOC Analyzer for analysis.
The released VOCs go through an initial separation stage on a Gas Chromatography (GC) column. They then arrive at the FAIMS chip, where they are further separated and identified by means of their relative mobilities. The Lonestar VOC Analyzer can also be used to analyze solid and liquid samples, by replacing the TD and GC steps with the ATLAS sampling module.
Reproducibility is key for any analytical technique, and extensive testing has been carried out to ensure that the Lonestar VOC Analyzer, when used with the ReCIVA Breath Sampler, allows for consistent collection and analysis of breath VOCs. This is of particular importance when conducting studies that involve the collection of multiple samples, at multiple different sites and timepoints, such as Owlstone Medical's LuCID trial.
The figure to the left shows chromatograms of VOCs in breath samples collected on independent VOC sample tubes using the ReCIVA Breath Sampler and analyzed using the Lonestar VOC Analyzer. High reproducibility is observed within a single breath collect (i and ii). Small changes in the chromatogram are observed between different breath collects performed on the same individual (iii), which is to be expected because some VOCs in breath do vary over time.
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
ReCIVA® Breath Sampler
A reliable and reproducible way to capture VOC biomarkers in breath samples
ATLAS Headspace Sampler
Add simple and reproducible headspace sampling to Lonestar for analysis of VOCs from liquids and solids
Owlstone Medical work with new FAIMS users to develop sample and data analysis methodologies. The Lonestar VOC Analyzer uses an open data format, allowing users to also use off the shelf multi-variate analysis tools. Raw FAIMS spectra are produced from the analysis of clinical samples. 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 extract the distinguishing features of those patients suffering from specific illnesses vs. healthy individuals.
A model (such as the OPLS-DA model, left) or 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.
Field Asymmetric Ion Mobility Spectrometry
Positive and negative ions
Inlet / Outlet
1/8 Swagelok compression fittings
Volatile Organic Compounds
User adjustable inlet dilution for ppb - %
Required Gas Supply
Clean, dry air (there is an integrated, replaceable scrubber)
Max Heater Temperature
0% - 95%
Temperature, humidity, flow and pressure
Inbuilt tracker ball
Real-time chemical spectra and stored data
Inbuilt PC running Windows XP
Custom online control software
383 x 262 x 195 mm