## 4 Discussion and Conclusions
In this wavelength range, spectra are primarily shaped by (de)oxyhemoglobin. The difference between the two datasets can be related to the difference in time after resection when the specimens are measured: Lumpectomy specimens are measured immediately after resection whereas the tissue slices are first inked and sliced. This time delay might have an effect on blood volume fraction and \(StO_{2}\) in the tissue as reported by [PERSON] et al. [27]. They stated that especially \(StO_{2}\) was an unreliable parameter due to excessive changes in oxygenated and deoxygenated hemoglobin post-exision. In their study, the blood volume fraction changed over time, but until 63 minutes after excision, these changes were less than the differences between positive malignant sites and connective or adipose tissue. Another explanation of the difference between the two datasets may be the difference in cutting the tissue by cauterization, used during surgery, or by using a regular knife, used while slicing the specimen. [PERSON] et al. showed that there was a difference in the total blood volume in lumpectomy (cut with cauterization) and mastectomy (cut with a knife) specimens and they assumed that by cauterization of the vasculature, blood is prevented from draining out of the vessels [27]. [PERSON] et al. reported on the effect of thermal coagulation on blood samples and found a change in shape in between 600 and 650 nm that was related to a decrease in oxyhemoglobin related features [28]. A similar effect was observed in this study, as shown in Figure 7a. However, since this wavelength range was excluded after wavelength reduction, this effect of cauterization was expected to be minimal. In a study by [PERSON] et al., the influence of cutting by coagulation on diffuse reflectance spectra in the NIR was small in muscle tissue and nonexistent in adipose tissue [29]. Therefore, we expect a minimum effect in the NIR wavelengthregion. With the remaining wavelengths, it is possible to discriminate adipose tissue from the other tissue types on the tissue slices dataset, but it was more challenging to distinguish connective tissue from malignant tissue. With the LDA classifier and the limited wavelengths range, the performance for detecting adipose tissue was high (>94%) while the performance for detection of either tumor (56% using VIS), connective tissue (35% using NIR) or both tumor and connective tissue (79% and 68%, respectively, using VIS + NIR) was low. These classification results on the slices dataset may not be good enough for accurate discrimination between tumor and connective tissue in clinical practice. This was confirmed with the lumpectomy dataset: only 1 of 4 malignant locations that required a re-operation were found with LDA. As explained in the introduction, obtaining a high correlation of hyperspectral measurements on lumpectomy specimens with histopathology is difficult. In addition, it is challenging to find tumor-positive margins on the specimen by eye. With the presented data acquisition method, we were able to obtain a high correlation with histopathology and mark the tumor positive margin in 4 out of 5 patients that required an extra operation. Nevertheless, in the lumpectomy dataset, there were only a limited number of malignant locations in comparison with the number of healthy locations, as the number of specimens with a tumor-positive margin was limited. In addition, none of the malignant locations contained more than 80% IC or DCIS in the corresponding H & E section. Therefore, in contrast to the slices dataset, the lumpectomy dataset contained no 'pure' tumor measurements. For example, in the H & E section of location 1 in Figure 4b, there is a rim of adipose tissue between the ILC border and the resection surface. This affects the measured spectrum and will increase the likelihood that this malignant location is classified as adipose tissue.