Particle analysis module

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.

A new study highlights the use of Raman spectroscopy to detect lung cancer and evaluate the effects of the PCV13 vaccine. Researchers found distinct biochemical changes in lung cancer patients and healthy individuals, revealing the vaccine's significant impact on immune response.

A combination of surface-enhanced Raman spectroscopy (SERS) and machine learning on microfluidic chips has achieved an impressive 98.6% accuracy in classifying leukemia cell subtypes, offering a fast, highly sensitive tool for clinical diagnosis.

Researchers at the Chinese Academy of Sciences have developed an optical detection strategy for circulating tumor cells (CTCs), combining machine learning (ML) and dual-modal surface-enhanced Raman spectroscopy (SERS). This approach offers high sensitivity, specificity, and efficiency, potentially advancing early cancer diagnosis.

Researchers from the University of Liege have demonstrated the potential of surface-enhanced transmission Raman spectroscopy (SETRS) for detecting impurities in pharmaceuticals. The study highlights SETRS’s superior sensitivity, precision, and efficiency in quantifying toxic impurities like 4-aminophenol (4-AP), offering a promising alternative to traditional methods.

Researchers have developed a rapid, reagent-free method to estimate the saponification value (SV) of edible oils using handheld Raman spectroscopy. This innovative approach simplifies oil quality testing, cutting time and costs while enhancing accuracy and portability.