# Multi-mode Microscopic Hyperspectral Imager for the Sensing of Biological Samples
## 3 Results
### Zebrafish Detection on Transmission Mode  
To illustrate the performance of our MMHI system, we firstly performed the transmission detection of a translucent sample. Here, a zebrafish larva (supplied by core facilities, Zhejiang University school of medicine) was chosen as the tested sample (embedded in 1% agarose), and an optical microscopic image was captured through a traditional microscopy, as shown in Figure 3a.  
In the transmission mode, the broadband light is emitted from below the motion stage, and is captured by the imager after penetrating the sample. A push-broom scanning experiment was conducted to verify the feasibility of the hyperspectral imaging. The sample was placed on a glass slide and moved at an interval of 1 \(\upmu\)m on the motion stage to realize the scan process. A series of 1000 spectral images was obtained with a total scanning time of 100 s. The scanning speed was mainly limited by the efficiency of the CMOS camera to transfer the captured images to a computer hard disk. These images were subsequently processed by MATLAB and stitched into a 3D hyperspectral cube, in which the spatial image was obtained by summing the spectral images over all wavelength bands [18], as shown in Figure 3b. Compared the hyperspectral image with the optical image, we found that the MMHI system had excellent imaging performance.  
Figure 3: Experimental results the hyperspectral detection of a zebrafish under the transmission mode. (**a**) Optical microscopic image of a zebrafish under a traditional microscopy with 40\(\times\) objective. Scale bar: 100 \(\upmu\)m. (**b**) Hyperspectral image of a zebrafish reconstructed from our system. Scale bar: 100 \(\upmu\)m. (**c**) Partial enlarged image of the zebrafish and three points of interest, including the transparent fin (marked as red dot), speckle (marked as blue star) and yolk sac (marked as yellow triangle). (**d**) Corresponding transmittance spectrum at the three points of interest.  
We further analyzed three regions of interest (ROI, shown as Figure 3c) to investigate their transmission spectra. There are some methods for the ROI extraction of a biomedical image, such as semantic segmentation, multi-feature clustering and convolutional neural networks [19; 20; 21], which can greatly improve the accuracy of ROI selection. Here, fin (marked as red dot), speckle (marked as blue star) and yolk sac (marked as yellow triangle) were selected as ROI, and their corresponding spectra were shown in Figure 3d. Since the 3D hyperspectral cube of the zebrafish was obtained, every pixel in the 2D hyperspectral image corresponded to a specific spectrum. To avoid the spectral error of single pixel, we calculated the average spectrum of the corresponding area. Due to the transparency of the fin, its transmission spectrum was almost the same as the spectrum of the broadband light source. The speckle part contained a large amount of melanin, which caused the absorption in the whole visible light band. The existences of \(\beta\)-carotene and hemoglobin [22] in the yolk sac led to absorptions at 480 nm and 580 nm. Based on the above result, our MMHI system is shown to have the ability to detect translucent or transparent samples.