Chemical Sensors, Neural Network for Human Breath Analysis

We’ve been following the science of breath analysis for quite sometime. Here are interesting new developments from University at Buffalo researchers, led by Dr. Frank V. Bright, Distinguished Professor in the Department of Chemistry in the College of Arts and Sciences:

Current methods of detecting these chemicals in human breath and other odors require cumbersome and elaborate laboratory instruments, such as gas chromatographs, which would be prohibitively expensive and inappropriate for clinical, home or remote field settings.
That’s why the UB team is taking a multidisciplinary approach, integrating research in neural networks, pattern analysis, novel sensor technologies, low-power optical detectors and light sources with clinical expertise…
The UB team, members of which have developed some of the world’s most stable and robust sensors, including some that do not need any calibration for more than two years, may be the first to integrate chemists, clinicians, computer scientists and engineers to exploit the full potential of expired gases or odors from human breath or other parts of the body to diagnose diseases.
While there are other electronic “noses” already on the market, they cannot correlate reliably their read-outs to a particular disease state, Bright said.
“The UB device will be unique because it will be designed to exploit, and in some ways mimic, the concepts of olfaction,” he continued. “Despite the fact that we might encounter numerous really smelly things in our lifetime, it is not as if there are billions of discrete sensors within our noses that nature designed a priori to respond selectively to every possible smelly odor.
“Rather, there are suites of receptors in our nasal passages and the collective response from all of these receptors to an odor or set of odors can be discriminated,” he said.
In the same way, the UB device will contain individual chemical sensors, perhaps as many as a million, which collectively will produce a pattern revealing the chemical signature of a patient’s breath, which may be related to a particular disease state.
That pattern will then be used to “train” neural networks, groups of connected artificial neurons capable of learning new information, to discriminate potentially between patients with specific diseases.
“The power of neural networks in this research is that they will pull out the important features and save them so that when they are exposed to a chemical pattern they have ‘seen’ before, the device will elicit the right response,” said Albert H. Titus, Ph.D., assistant professor of electrical engineering in the UB School of Engineering and Applied Sciences and a co-investigator on the project.
He added that with neural network processing, the size of the sensor elements can stay very small, each measuring about 10 micrometers in size, a critical element for the inexpensive, low-power device the UB team is designing.
Titus is building novel, complementary metal oxide semiconductor (CMOS) arrays that simultaneously will read the signals produced by each of the sensor elements.
“The issue with this application is can you come up with a unique ensemble of sensor elements that exhibit enough diversity to respond to a large variety of small, chemically similar species to give you a chance of realizing the chemical fidelity that you need?” asked Bright.
To achieve that fidelity, he said the chemical sensors will be made out of xerogels, porous glass-like materials that consist of easily tailored nanoscopic pores, which can be tuned to recognize specific chemicals or classes of chemicals.
Bright’s lab is well-known for its work developing chemical sensors out of xerogels that detect chemicals in blood, urine and other samples.
So far, he and his associates have developed xerogels that can respond to about 100 different chemicals, ranging from small molecules like oxygen and carbon dioxide to mid-sized molecules like steroids and prostaglandins up to big proteins like interleukins.
Bright explained that the envisioned device will work as follows: As the breath sample flows through the breath-testing device, the individual sensing elements will change their color or intensity; those changes will be detected by the CMOS array, producing electrical signals that then can be processed by the neural network.

More from the university press office…
Article at LiveScience.com…