spektrummatching

Spectral matching, also sometimes written spektrummatching, is the process of comparing an observed spectrum to a library of reference spectra to identify substances, characterize materials, or infer properties from their spectral signature. It is widely used in analytical chemistry, spectroscopy, and remote sensing, appearing in mass spectrometry, infrared and Raman spectroscopy, as well as hyperspectral imaging and signal processing.

Typical workflow includes data acquisition, preprocessing (noise reduction, baseline correction, normalization), feature extraction (peak detection, fingerprint spectra), alignment to correct instrumental drift, and a similarity or scoring step that ranks candidate references. Common similarity measures include cosine similarity, correlation, dot product, spectral angle mapper, or probabilistic scoring. Some workflows incorporate machine learning models to predict identities from spectra or to rescore library matches.

Applications include compound identification in drug discovery and quality control, environmental monitoring, food authentication, mineral and material identification, forensic analysis, and planetary remote sensing. In mass spectrometry, spectral matching often relies on MS/MS libraries (for example, NIST or MassBank) and scoring schemes to identify compounds.

Challenges include variability between instruments and experimental conditions, incomplete or noisy spectra, peak overlap, and limited library coverage. Effective spectral matching requires careful preprocessing, robust libraries, and standardized scoring frameworks to enable reliable identification and comparison across studies.