Linear decompressors are the dominant methodology used in commercial test data compression tools. However, they are generally not able to exploit correlations in the test data, and thus the amount of compression that can be achieved with a linear decompressor is directly limited by the number of specified bits in the test data. The paper describes a scheme in which a non-linear decoder is placed between the linear decompressor and the scan chains. The nonlinear decoder uses statistical transformations that exploit correlations in the test data to reduce the number of specified bits that need to be produced by the linear decompressor. Given a test set, a procedure is presented for selecting a statistical code that effectively "compresses" the number of specified bits (note that this is a novel and different application of statistical codes from what has been studied before and requires new algorithms). Results indicate that the overall compression can be increased significantly using a small non-linear decoder produced with the procedure described in this paper.

Liquid chromatography linked with mass spectrometry (LC-MS) was used to analyse gelatin from four different species after a trypsin digest. Using chemometric software to analyse the data it was possible to find peptide fragments that were specific to each species of gelatin: porcine, bovine, chicken or fish. Identification of these peptides was challenging due to the destructive nature of gelatin manufacture. The untargeted workflow method developed allowed identification of 21 unknown gelatin samples with 100% accuracy. Fish gelatin is made from a large range of different species that do not share a common differentiating protein but it was shown that the protein from a parasitic bacteria could be used to identify fish gelatin.

The project aim was to identify differences in the metabolomic profiles in the serum of patients with multiple sclerosis (MS), those with neuropathic pain (NP) and those with both MS and NP compared with controls and to identify potential biomarkers of each disease state.