Software and spectral databases

Researchers are using AI-enabled Raman spectroscopy to enhance the development, administration, and response prediction of cancer immunotherapies. This innovative, label-free method provides detailed insights into tumor-immune microenvironments, aiming to optimize personalized immunotherapy and other treatment strategies and improve patient outcomes.

A research team from Jiangsu University has developed a Raman spectroscopy-based method to detect aflatoxin B1 (AFB1) in peanuts with improved accuracy and efficiency. By employing a two-step hybrid strategy integrating backward interval partial least squares (BiPLS) and variable combination population analysis (VCPA), the new model significantly enhances the precision of AFB1 detection, providing a more reliable approach for food safety monitoring.

In this Icons of Spectroscopy column, Executive Editor Jerome Workman Jr. delves into Part II of the Raman Effect, discovered by C. V. Raman, a physicist whose major contributions to vibrational spectroscopy have helped shape the field and advance one of the most important quantum-based spectroscopy tools of recent generations.

In this "Icons of Spectroscopy" column, executive editor Jerome Workman Jr. delves into Part I of the life and work of C. V. Raman, a physicist who has made major contributions to the science of vibrational spectroscopy and created an understanding for one of the most important quantum-based spectroscopy tools in generations.

Scientists from China and Finland have developed an advanced method for detecting cardiovascular drugs in blood using surface-enhanced Raman spectroscopy (SERS) and artificial intelligence (AI). This innovative approach, which employs "molecular hooks" to selectively capture drug molecules, enables rapid and precise analysis, offering a potential advance for real-time clinical diagnostics.

Researchers from Tsinghua University have developed an innovative dual-wavelength Raman spectroscopy method that eliminates fluorescence interference in measuring esophageal tissue samples. This development enables precise identification of molecular changes in these tissues, with potential applications in early esophageal cancer diagnosis.