VIT-AP University Researchers Demonstrate a Promising Low-Cost Photonic Technology for Non-Invasive Disease Screening Through Breath Analysis

Amaravati, November 13, 2026: In a significant research achievement, scientists from CATS Global (CASTLE Advanced Technologies and Systems), Nashik, Maharashtra, and VIT-AP University, Amaravati, Andhra Pradesh, have developed and experimentally demonstrated an innovative, low-cost optical sensing approach that could pave the way for affordable, non-invasive disease screening through breath analysis.
The research, authored by Prof. Dr. Rao Tatavarti, Prof. Dr. Sridevi Nadimpalli and Prof. Dr. P. Arulmozhivarman Pachiyappan, was published on November 6, 2026, in the prestigious international journal Scientific Reports, published by Springer Nature.
The research article, titled "Photonic elastic-backscatter fingerprinting of exhaled-breath metabolites for non-invasive disease screening," explores an innovative photonic sensing technique that uses a simple, low-power laser and a position-sensitive optical detector to identify distinctive optical signatures associated with disease-related chemical compounds.
A New Direction in Affordable Breath-Based Screening
Human breath contains hundreds of volatile organic compounds (VOCs) and other gases that can reflect changes in the body's metabolic processes. Variations in the concentration of these compounds can be associated with different diseases, making breath analysis a promising approach for non-invasive health screening.
Conventional breath-analysis technologies often rely on sophisticated laboratory instruments, including mass spectrometers, gas chromatographs and advanced optical spectroscopy systems, which can be expensive and require specialized infrastructure.
Addressing this challenge, the research team explored a fundamental yet innovative question:
Can a simple, fixed-wavelength laser and a photonic detector extract meaningful chemical information from a gas mixture without requiring expensive spectroscopic equipment?
The study demonstrates the potential of photonic elastic-backscatter fingerprinting to detect, distinguish and classify different disease-associated gas mixtures through subtle changes in the intensity and spatial position of scattered laser light.
This approach offers an exciting direction towards developing compact, cost-effective and potentially portable optical sensing systems.
Five Disease-Associated Conditions Investigated
The researchers experimentally evaluated the system using synthetic gas mixtures representing five disease-associated conditions:
1.      Chronic Kidney Disease (CKD): Ammonia, associated with metabolic changes resulting from impaired kidney function.
2.      Cardiovascular Disease (CVD): Acetone and n-pentane, associated with metabolic changes and oxidative stress.
3.      Small Intestinal Bacterial Overgrowth (SIBO): Hydrogen and methane, gases associated with bacterial fermentation.
4.      Cystic Fibrosis (CF): Carbon monoxide and nitric oxide, associated with airway inflammation and oxidative stress.
5.      Chronic Obstructive Pulmonary Disease (COPD): Carbon monoxide and nitric oxide, investigated at different concentrations to distinguish its gas signature from that of cystic fibrosis.
Two additional reference conditions—healthy-air composition and ambient chamber air—were included, resulting in seven experimental gas classes.
The Innovative Dr. T Photonic Sensing System
The research introduces a custom-developed optical hardware platform known as the Dr. T System.
The system employs:
·  A low-power 635 nm diode laser operating below 1 mW.
·  A specially designed quartz gas chamber of 0.1088 litres.
·  A dual-axis position-sensing photodiode (PSD) to capture backscattered light.
·  Three optical data channels comprising light intensity and two spatial beam-position coordinates.
·  Computational data processing and SoftMax-based classification to distinguish the different gas mixtures.
Unlike conventional spectroscopy, which analyses the wavelength spectrum of light, this approach examines the subtle changes in the intensity and spatial distribution of elastically backscattered light.
These changes create distinctive optical fingerprints corresponding to the composition of the gas mixtures.
The experimental evaluation involved seven synthetic gas classes across 19 experimental runs. The system achieved an overall classification accuracy of 78.9%, demonstrating the feasibility of extracting disease-associated chemical information using a relatively simple optical configuration.
Importantly, four of the five simulated disease classes achieved perfect validation recall in the reported leave-one-run-out testing.
Scientific Significance and Future Potential
One of the important contributions of this research is its demonstration that useful chemical discrimination may be achieved through relatively simple optical measurements, without relying on bulky and expensive spectroscopic instruments.
The study also provides insights into the underlying optical physics, particularly how molecular composition and variations in refractive index influence the spatial behaviour of backscattered laser light.
The research establishes an experimental foundation for further development of affordable photonic breath-analysis technologies.
The authors have also identified important milestones for future clinical translation, including humidity compensation, constant-pressure operation, improved radiometric calibration and advanced signal-processing algorithms.
It is important to note that the present study was conducted using controlled, dry synthetic gas mixtures rather than actual human breath samples. Further investigations involving real human breath samples, clinical validation and larger patient populations will be necessary before the technology can be considered for medical diagnostic applications.
The Researchers Behind the Innovation
Prof. Dr. Rao Tatavarti – Lead Designer and Principal Investigator
Prof. Dr. Rao Tatavarti brings over 45 years of experience in advanced technology research and development. His distinguished career includes senior scientific and programme leadership roles at the Naval Physical and Oceanographic Laboratory (NPOL), Defence Research and Development Organization (DRDO). His expertise spans photonics, lasers, fibre-optic sensors, optoelectronic systems and advanced sensing technologies. He is the Founder Director and Chairman of the CATS Group of Companies.
Prof. Dr. Sridevi Nadimpalli – Research Collaborator
Prof. Dr. Sridevi Nadimpalli brings substantial academic and research experience in chemistry, chemical engineering and technology development. Her contribution to the study includes experimental design, calibration and the development of controlled gas-mixture testing protocols.
Prof. Dr. P. Arulmozhivarman Pachiyappan – Research Collaborator
Prof. Dr. P. Arulmozhivarman Pachiyappan is the Vice-Chancellor of VIT-AP University, Amaravati. His research expertise encompasses electrical and electronics engineering, photonics, signal processing, machine learning and computational data analytics. His contribution to the research focuses on computational modelling, signal processing and the development of the algorithmic classification framework.
Strengthening Interdisciplinary Research and Innovation
This collaborative research demonstrates the importance of bringing together expertise in photonics, engineering, chemistry, signal processing and computational intelligence to address important challenges in healthcare technology.
The work also reflects the growing potential of interdisciplinary research collaborations between academic institutions and technology-driven organisations to translate scientific ideas into practical innovations.
For VIT-AP University, this publication represents an important contribution to research in advanced photonic technologies, intelligent sensing systems and interdisciplinary innovation, with potential applications in healthcare, environmental monitoring and industrial sensing.
The research team envisages further development of this technology through real-world testing, clinical collaborations and continued hardware and algorithmic refinement.
The long-term vision is to contribute to the development of affordable, accessible and non-invasive screening technologies that could eventually support healthcare systems, particularly in resource-constrained settings.