A new study published in the International Journal of Optics introduces a pioneering spectral reconstruction method that significantly enhances terahertz (THz) spectroscopy for the non-invasive detection of hazardous substances hidden in mail packages.
Amid growing security concerns driven by global e-commerce volumes, researchers have developed a technique that increases the accuracy and reliability of THz-based screening systems used in customs and postal hubs.
Terahertz Technology Meets Real-World Challenges
THz waves—positioned between microwave and infrared frequencies—are uniquely suited for inspecting sealed items without physical intrusion. With their ability to penetrate common packaging materials such as paper, plastic, and fabric, THz waves offer a promising avenue for detecting explosives, narcotics, and contraband with minimal risk, as the radiation is non-ionizing and safe for package contents.
However, spectral distortion caused by air gaps, misalignments, and envelope materials has historically limited real-world performance. To address these hurdles, researchers proposed a dual-layered spectral reconstruction technique that pairs Voigt peak fitting with asymmetric least squares (AsLS) baseline correction.
This hybrid approach significantly sharpens spectral resolution while reducing background noise and interference—resulting in cleaner, more accurate data for threat detection.
Enhanced Accuracy Through Machine Learning and Signal Processing
The study leveraged a THz time-domain spectroscopy (THz-TDS) system incorporating a dual-port femtosecond laser and photoconductive antenna for high-resolution measurements. Sample tests were conducted using nalidixic acid and mitomycin—representing hazardous compounds—sealed in standard Express Mail Service envelopes.
Principal Component Analysis (PCA) revealed a marked improvement in signal quality after applying the reconstruction technique, with the first principal component (PC1) explaining over 99% of the variance, up from 75% in the raw data.
- Machine learning algorithms further validated the method’s impact:
- The PCA-SVM model achieved 90.74% accuracy, a 7% improvement.
- A 1D Convolutional Neural Network (CNN) reached 98.45% accuracy, nearly 10% higher than without spectral reconstruction.
Root mean square error (RMSE) dropped significantly to 0.31% for nalidixic acid and 0.28% for mitomycin, affirming the precision of the improved THz spectra.
Broader Implications for Global Security
This breakthrough has substantial implications for security screening at postal, customs, and border control facilities. The ability to detect illicit substances without opening packages enhances throughput while minimizing false alarms and operational delays.
Beyond mail inspection, the technology opens avenues for pharmaceutical verification, forensic science, counterfeit prevention, and biomedical diagnostics—domains where precise molecular identification is critical.
As threats become increasingly sophisticated, combining advanced spectroscopy with AI-driven analysis positions THz-based solutions as a frontline defense in non-destructive hazard detection.
Reference: https://www.azooptics.com/News.aspx?newsID=30329
