Window function for weighting out irrelevant interferents 64

For simulation, we do not desire to recreate the absorption peaks associated with CO2 or the water combination band in region 1780 - 2600 cm⁻¹. This absorption is a source of variation in the data set, but we do not expect it to carry any discriminative information for healthy and diseased carti-lage. Moreover, because these absorbance peaks are located in an otherwise absorption free region, we have the possibility to apply a window function to filter it out, without disturbing any informative spectral variations. To achieve this, a function for smoothly filtering out the peaks in region 1780 - 2600 cm⁻¹ was constructed based on the the Tukey window function [43].

The Tukey window function, also called the tapered cosine function, is for

Figure 4.17: This figure shows the window function (orange) used to weight out water combination band and CO2 band in the simulation, together with the mean spectrum (blue) for the Human12 data set.

this purpose augmented to the resulting function shown in Fig. 4.17 (red).

It should be noted that the function is applied after the MSC-L correction, to avoid disturbance of the parameter estimation, since realistic values of the parameters are important for the validation of the suggested preprocessing strategies for seven wavenumber channels data.

4.4.2 Selection of principal components

In the simulation approach used in this thesis, the principal components (loadings) for the PCA model are calculated and recombined into new spec-tra for the construction of a healthy cartilage tissue group and a diseased cartilage tissue group. The recombination of loadings was done separately for healthy and diseased groups. In this section, we evaluate how many prin-cipal components to include in the simulation model. To this end, PCA is run on the MSC-L corrected broad-band Human12 data set, and the calculated loadings are investigated. We use influence plots as an extra quality check for the data on which the simulation model is built, and motivate the further re-moval of some spectra. The goal is to include the components which contain information about the between-class spectral variation as well as variability that is common for the two classes, without introducing too many irrelevant artefacts.

In the PCA simulation approach, we run PCA on healthy and diseased groups separately, identifying the spectral variability in the data set within each of these groups. We thus obtain two separate sets of loadings, shown

components appear smooth and free from physical and other interference effects for both healthy and diseased groups. By including the three first principal components, we explain 94,2% of the spectral variability for group healthy and 95,2 % for group diseased, which we in general consider an ac-ceptable amount for the purpose of simulation. However, whether to include more than the first three components should be considered in more detail.

As we see for both the healthy and diseased group loadings, the 4th and 5th components show signs of interference effects in the regions 3700-4000 cm⁻¹ and 1700-2000 cm⁻¹, which are attributed to water vapor rotational transi-tions (see section 2.2, Fig. 2.4). Indeed component 5 accounts mainly for water vapor interferences. Therefore we must consider whether water vapor is something we want to include in the simulation. This is discussed in the following paragraph. Although the 6th principal component also contains some water vapor features, it is discarded because it explains only 0.8 % of the variance in both groups.

We evaluate now if water vapor should be included in the simulation or not. Firstly, the presence of water vapor in spectra is often an indicator that there has been water vapor in the air inside the instrumentation during measurements. To investigate if water vapor is associated with a limited number of spectra, or if it is a common occurrence in most spectra, the 5th principal component from PCA is used, since this component contains almost only signals from water vapor. In order to remove other possible contributions, we set the regions not associated with water vapor to zero.

We recombine the the scores of the 5th components, t₅, with the augmented water vapor component ˜p₅ by,

Xwv,centered=t₅p˜₅⁰ (4.1)

, whereXwv,centeredis water vapor contributions (with respect to the mean spectrum) for each spectrum. Xwv,centered is shown in Fig. 4.20, where we have zoomed in at the water vapor absorbance peaks in region 1300 - 1900 cm⁻¹. It can clearly be seen that many spectra contain more water vapor than the mean spectrum. This motivates us to include the principal component identifying water vapor variations in the simulation, since it is clearly an interference which is always present, at least for the instrumentation used for data set Human12. This may also be the case for the final Miracle probe instrumentation, unless instrumental precautions are made. For classification tasks such as in the following validation section, it should thus be desirable that classifiers are able to handle these variations. We concluded that water vapor components 4 and 5 should be included in the PCA simulation for

PC1( 61.3%)

PC2( 29.8%)

PC3( 3.1%)

PC4( 2.4%)

PC5( 1.3%)

PC6( 0.8%)

Figure 4.18: The figure includes the six first PCA loadings (PCs) for the healthy group in the broad-band data set Human12. The explained variance by each component is marked in the legend.

healthy and diseased groups even though they represent interference effects.

Further, in section 4.6, we will see how classification results are impacted by including versus not including water vapor in the simulation, for further discussion on this topic.

PC1( 54.6%)

PC2( 36.2%)

PC3( 4.4%)

PC4( 1.7%)

PC5( 1.2%)

PC6( 0.8%)

Figure 4.19: The figure includes the six first PCA loadings (PCs) for the diseased group in the broad-band data set Human12.The explained variance by each component is marked in the legend.

4.4.3 Thorough quality check for the data used for the