Bloodstain age estimation aims to infer the time of blood deposition from measurable changes in

dried bloodstains, yet its practical use is currently limited by fragmented methodology, strong context

dependence and a lack of quantitatively validated frameworks. This cumulative thesis develops

a stepwise pathway from biological and methodological foundations to a digitally supported and

mechanistically informed framework for bloodstain age estimation.

First, the biological and forensic basis of blood as evidence is outlined, clarifying how hemo-

globin chemistry and stain morphology relate to bloodstain pattern analysis and where temporal

information can strengthen crime scene reconstruction. A systematic review of existing analytical

approaches and aging signatures then delineates the current state of bloodstain age estimation and

identifies a central research gap: although numerous biochemical and optical aging markers have

been described, there is no generally applicable framework that accounts for key influencing factors

such as temperature, humidity, light exposure and substrate. New experimental series on ex vivo

bloodstains under controlled conditions are used to quantify these dependencies and define the

context in which any realistic bloodstain age estimation approach must operate.

On this empirical basis, the thesis introduces digital strategies that reduce observer dependence

and prepare bloodstain information for modeling. A guide for documenting crime scenes digitally,

algorithmic support for stain interpretation, and a mobile-compatible, image-based age estimation

are combined into a coherent and data-driven digital workflow that links bloodstain pattern analysis

more directly to subsequent temporal analysis.

Finally, the thesis turns to modeling as a bridge between observed aging signatures and underly-

ing mechanisms. Spectroscopic investigations of hemoglobin degradation and related compounds

provide the basis for a systems-based, generative model of bloodstain aging. This model repre-

sents UV–Vis spectra as mixtures of hemoglobin derivatives governed by time-dependent kinetics,

enabling the reconstruction and simulation of aging trajectories under defined conditions and the

generation of realistic synthetic spectra to help close existing experimental gaps that currently limit

the comprehensive and reliable use of bloodstain age estimation.

Taken together, the body of scientific work presented in this cumulative thesis shows how the

combination of targeted experimentation, digitization and generative modeling enables a transition

in bloodstain age estimation from largely descriptive, case-by-case analyses toward a more realistic,

mechanistically informed and practice-oriented framework.