\begin{abstract}

Many Bayesian solutions for the evaluation of forensic evidence exists. Most however, fail to quantifiy the real problem of \emph{brittleness} which plagued these model solutions and only one method has been presented to reduce this problem. Brittleness here refers to what Ken Smalldon labelled the ``fall-off-the-cliff-effect". In this work two(2) causes of brittleness have been identified:

\begin{enumerate}
\item A tiny error in the collection of data can lead to a spurious outcome from a forensic model
\item and/or if the model is dependent in statistical assumptions, such as assuming that the distributions of refractive indices of glass collected at a crime scene or a suspect obeys the properties of a normal distribution. 
\end{enumerate}

The aim of this work is to present solutions to these causes of brittleness and also a method for reducing brittleness. To accomplish this, first two chemometric models which are not easily influenced by tiny changes in the data and also do not rely on any statistical assumptions are presented. Then a measure for brittleness for one of these models is introduced. Finally, prototype learning is used to reduce the brittleness in these models.

With a data set composed of the infrared spectra of the clear coat layer of a range of cars, the performance analysis showed that both models were strong with near 100\% of the validation set finding the right target. Finally, prototype learning is applied successfully with a reduction in brittleness while mainintaining statistically indistinguisable results with validation sets.


\end{abstract}
