The Terahertz (THz) Research Group at the University of Hyderabad, led by Senior Professor Anil Kumar Chaudhary and Naveen Kumar Periketi (SRF, DIA-CoE, School of Physics), in collaboration with Ms. Iram Juveria and Prof. Aleem Basha (Maulana Azad National Urdu University), Dr. K. A. Jaleeli (Nizam College, Osmania University), and Dr. R. Purshotham (Department of Poultry Science, P.V.N.R. Telangana Veterinary University), has developed a novel, non-destructive terahertz spectroscopy-based technique for characterizing table and fertile eggshells.

For the first time, the team successfully measured the shell thickness and key optical and dielectric properties—including refractive index, absorption coefficients, and real and imaginary parts of the dielectric constants—using THz radiation in a completely non-invasive manner. The THz waves penetrate the eggshell without harming the embryo, providing valuable insights into egg fertility and shell quality, which are crucial parameters for poultry industry applications, particularly in quality assessment and transportation safety.

 

This pioneering work has been published in Nature Scientific Reports in open-access mode.

Scientific Reports volume 15, Article number: 35253 (2025) Cite this article

https://doi.org/10.1038/s41598-025-08985-1

In continuation, the research group led by Prof. A. K. Chaudhary along with Naveen Periketi (SRF), has made a significant advancement in the field of explosive material characterization by applying a one-dimensional convolutional neural network (1D-CNN) model to terahertz spectroscopic data. Their study focuses on the identification and classification of secondary explosive samples using terahertz time-domain spectroscopy (THz-TDS) performed in reflection geometry, a non-destructive technique that captures subtle spectral variations associated with molecular structure and composition.

The methodology holds strong potential for real-time detection and screening applications in defence and homeland security, where rapid and non-invasive material discrimination is critical. Moreover, the framework developed by the group can be extended to the analysis of other hazardous or sensitive materials, thus offering a powerful tool for future intelligent sensing platforms.

 

The work is published in the Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy,Volume 346, 5 February 2026, 126924 (online) .

Periketi, Naveen, and Anil Kumar Chaudhary. “Classification of secondary explosives with a 1D convolutional neural network technique using terahertz time-domain spectroscopy in reflection geometry.” Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy (2025): 126924.

https://doi.org/10.1016/j.saa.2025.126924