Statistical analysis

The statistical analysis is done with the leave one out strategy. All data from one head is excluded and the rest is used for training (training data). Afterwards, the trained classifiers are tested at the left out data (test data). This is done for all 15 possible combinations of left out animals. Before the training process, the data is normalized and a principle component analysis (PCA) is done for the training data. The first 24 components are used for training. In average, more than 99 % of the information can be represented by the first 24 components. Thus, this is valid and it decreases the time to train the classifier significantly. The coefficient matrix of the training data is also applied to the test data before the classifiers are tested.

For the classification two classifications are done. First, all four tissue types are tested. This multi class classification is done with Support Vector Machine (SVM), Random Forest (RF) and Linear Discriminant Analysis (LDA). Second, the differentiation between fat and nerve is tested separately due to the fact that this classification is normally the most difficult one and nerve is often surrounded by fat tissue. Moreover, it is tested how well nerve can be found in any of the other four tissue types. In the last two cases, the same classifiers and RobustBoost (RB) are used. RB is used because it also shows good results for tissue differentiation [26]. For all cases, only single spectra are used for training and testing. No averaging is done.