## 2 Methods  
In human biomedical tissue, the NIR-I window of 700-900 nm shows low tissue absorption, while that of 900-1700 nm presents a higher penetration depth due to reduced scattering [41]. Considering the absorption properties of glucose (C\({}_{6}\)H\({}_{12}\)O\({}_{6}\)), with bands of 1100-1300 nm and 1500-1800 nm, 6-channel multi-wavelength NIR lights of 850 nm, 950 nm, 1300 nm, 1400 nm, 1550 nm, and 1650 nm are used as \(I_{sig}\) to characterize the NIR optical performance of human tissue. In order to account for potential interference from analytes such as lactate and urea, Table S1 in the Supplementary Materials lists the NIR absorption peaks of key blood components. The primary absorption bands of urea and protein are primarily associated with N-H bonds, whereas glucose molecules exhibit absorption at C-H and O-H bonds. The selected wavelengths (850 nm, 940 nm, 1300 nm, 1400 nm, 1550 nm, and 1650 nm) are distinct from the absorption peaks of most interfering analytes. Considering distinct optical absorption properties of human tissue and glucose, the low tissue absorption regions (850 nm and 940 nm channels) serve as reference signals to mitigate background noise; glucose absorption bands (1300 nm, 1400 nm, 1550 nm, and 1650 nm) containing target-specific information serve as the signal. This measured multi-channel optical information is systematically integrated by multilevel metric learning with a BP network. The designed multilevel metric learning module is used to extract effective optical information, while the BP network is applied to predict human blood glucose concentrations. These 6-channel multi-wavelength NIR lights are divided into two groups: 850 nm and 950 nm for low tissue absorption, and 1300 nm, 1400 nm, 1550 nm, and 1650 nm for obvious absorption. According to the multi-wavelength absorption model, based on the Lambert-Beer law [42], the intensity of 6-channel multi-wavelength NIR light \(I_{\text{sig}}(\lambda_{i})\) can be calculated by  
\[I_{\text{sig}}(\lambda_{i})=I_{0}exp\big{[}\varepsilon_{sig}(\lambda_{i})c_{ i}L(\lambda_{i})+G\big{]}, \tag{1}\]  
where \(I_{0}\) represents the intensity of incident light, \(c_{i}\) is the concentration of the measured sample, \(G\) donates the absorption of the sensing background, and \(\varepsilon_{\text{sig}}(\lambda_{i})\) and \(L(\lambda_{i})\) describe the extinction factor and optical path for the wavelength \(\lambda_{i}\). To remove complex sensing background \(G\), the scattered light of the ground glass diffuser serves as the reference light \(I_{ref}\) and can be described as follows:  
\[I_{ref}=I_{0}exp[G] \tag{2}\]  
Then, the normalized multi-wavelength light \(NI_{SR}(\lambda_{i})\) can be calculated as  
\[NI_{SR}(\lambda_{i})=\frac{I_{sig}}{I_{ref}}=exp\big{[}\varepsilon_{sig}( \lambda_{i})c_{i}L(\lambda_{i})\big{]} \tag{3}\]  
Furthermore, normalized multi-wavelength lights \(NI_{SR}(\lambda_{i})\) are introduced into the proposed HMML-BP network to remove complex human tissue noise and predict glucose concentration. As shown in Figure 1, our HMML-BP network consists of four critical parts:Removing sensing background: Measured 6-channel human intensity \(I_{\text{sig}}(\lambda_{i})\) is normalized according to the measured reference scattering intensity \(I_{\text{sig}}(\lambda_{i})\), using Equations (1)-(3), in order to remove the influence of the sensing background and obtain normalized 6-channel multi-wavelength light information, \(NI_{SR}\);
2. First level of metric learning elements: Normalized 850 nm \(NI_{SR}\)(850) and 940 nm \(NI_{SR}\)(940) channels are trained by the metric learning elements to form the 1D human tissue low absorption data (TData 1). SNE, diffusion maps, and factor analysis algorithms are utilized to optimize TData 1 to effectively extract NIR optical information.