Automating the amino acid identification in elliptical dichroism spectrometer with Machine Learning.

Amino acid identification is crucial across various scientific disciplines, including biochemistry, pharmaceutical research, and medical diagnostics. However, traditional methods such as mass spectrometry require extensive sample preparation and are time-consuming, complex and costly. Therefore, this study presents a pioneering Machine Learning (ML) approach for automatic amino acid identification by utilizing the unique absorption profiles from an Elliptical Dichroism (ED) spectrometer.

Characterizing biomolecular structure features through an innovative elliptical dichroism spectrometry for cancer detection.

This research introduces a novel method for evaluating the structural features of biomolecules, utilizing our innovative Elliptical Dichroism (ED) spectrometer specifically designed for stereochemical analysis. By integrating ED spectrometry with autocorrelation (AC) analysis, we investigate the conformational characteristics of biological molecules such as amino acids, proteins, and extracellular vesicles (EVs) induced by elliptically polarized UV absorption. Our streamlined approach offers a cost-effective and portable solution with minimal sample consumption and supports multiple working modes to efficiently characterize biomolecular structures.