There is a region between 1000-1400 nm that is well transparent to living organisms

The light that humans normally see is visible light (VIS) in the middle of the spectrum.
The wavelengths between 1000 nm and 1400 nm are known as the "window of transmittance" for living organisms.
Our equipment can measure hyperspectral imaging (spectral imaging) from the visible light range to the near-infrared light range.

This is the whispering meat seen in near-infrared light.
You can see the checkerboard through the center of the whispering meat.
This is because near-infrared light passes through the meat and hits the checkerboard below,
and the reflected light is measured.
In other words, near-infrared light of a certain wavelength has the characteristic of passing through objects where light visible to the naked eye is reflected or absorbed.
(*Note: Near-infrared light is invisible to the human eye, so the colors are pseudo-colored.)

Hyperspectral Imaging (HSI) Spectral Imaging
A method of acquiring three-dimensional data with continuous spectral information at each pixel of the image

This is an example of an image of a rat's stomach. Each region (e.g., heart, lungs, liver, stomach) has a different absorption wavelength spectrum.
The color of the line represents the average absorption wavelength spectrum of each region.
Each site (e.g., heart, lungs, liver, stomach) has multiple pixels.
Each pixel has its own spectral data. （This is hyperspectral imaging/spectral imaging.）
For example, in a normal color image, one pixel (pixel) has three RGB values, but in this case there are over 100 values.
The graph shows that each spectrum is different.

Automatic Discrimination by AI
NIR-HSI can perform bio-transmission + component analysis + mapping.

Since each pixel (pixels) has its own characteristic wavelength spectrum,
AI technology (machine learning) can be used to identify them.
Since data can be obtained for each pixel (pixel), it is a good match for AI technology.
The result of AI identification is superimposed on the original image.
The differences in color are the respective parts of the image. Since each pixel can be color-coded,
you can tell that it has been identified.

Measurement example: Visualization of deep blood vessels

This photograph shows that endoscopic images taken during surgery can visualize large blood vessels from the superficial to the deeper layers of the body.
With human visible light (normal camera), only the thin blood vessels in the superficial layers are visible,
The thicker blood vessels (blue dotted lines) in the deeper layers are not visible.
In the near-infrared light image, the thin blood vessels in the superficial layer are not visible, but the thick blood vessels in the deeper layers appear to be raised.

Measurement example: Identification of plastics

Examples of analysis of plastics measured with the NIR-HSI system for rigid endoscopes.
Left: The visible image shows six types of plastics that appear transparent.
Each plastic has a different wavelength spectrum to be identified.
Top photo on the right: Near-infrared images of plastics taken by the system. （The color is pseudo-transparent.)
Bottom right: Machine-learning-based identification of plastics captured by the device.
The color represents the identified plastic.