## 4 Results and Discussion  
### Multi-Wavelength Light Metric Learning  
Six multi-wavelength signal light \(NI_{SR}(\lambda_{i})\) channels of \(NI_{SR}(850)\), \(NI_{SR}(940)\), \(NI_{SR}(1300)\), \(NI_{SR}(1400)\), \(NI_{SR}(1550)\), and \(NI_{SR}(1650)\) are input into the multilevel metric  
Figure 2: (**a**) Schematic diagrams of the highly accurate narrowband multi-wavelength light NIR sensing system and (**b**) the monolithic multi-wavelength light electric filter wheel, consisting of 6 infrared filters of 850 nm, 940 nm, 1300 nm, 1400 nm, 1550 nm, and 1650 nm. learning module to build multi-wavelength light data. Figure 3a-c show the TData 1 made by SNE, diffusion maps, and factor analysis algorithms, respectively. As can be seen in Figure 3a-c, all these TData 1 exhibit low linear properties that can be attributed to the intrinsic random absorption properties of various biomaterials constituting the artificial human tissue background. Furthermore, Figure 3d-f exhibit the GData 3 optimized by SNE, diffusion maps, and factor analysis algorithms, respectively, displaying a certain degree of linearity compared to TData 1 (Figure 3a-c). The constructed GData 3 Fact made by the factor analysis algorithm shows improved linearity compared to that obtained by SNE and diffusion map algorithms. In addition, these 3D multi-wavelength light metric data, MData 3, in Figure 3g-i show improved linear properties compared to the GData 3 shown in Figure 3d-f, indicating that the artificial human background TData 1 play a critical role in removing random biomaterial interference. Furthermore, MData 3 Fact obtained by the factor analysis algorithm (Figure 3i) exhibit the best linearity in contrast to all these results, showing that the multilevel metric learning module achieved using this algorithm shows the best performance in reducing biomaterial interference and effectively extracting glucose NIR absorption information. The construction of multi-wavelength light data using other algorithms is shown in Figure S1. Figure 3: (**a–c**) Tissue absorption data (1D) (TData 1) obtained by SNE, diffusion maps, and factor analysis algorithms using measured \(N_{\text{ISR}}(850)\) and \(N_{\text{ISR}}(940)\); (**d–f**) 3D glucose absorption data (GData 3) made by SNE, diffusion maps, and factor analysis algorithms using measured \(N_{ISR}(1300)\), \(N_{\text{ISR}}(1400)\), \(N_{\text{ISR}}(1550)\), and \(N_{\text{ISR}}(1650)\); and (**g–i**) 3D multi-wavelength light data (MData 3) created by SNE, diffusion maps, and factor analysis algorithms using TData 1 and GData 3, where salivary spots denote the measured glucose concentration space. In view of the multidimensional distribution characteristic of 3D multi-wavelength light data, the standardized Euclidean distance is calculated and compared to quantitatively characterize GData 3 and MData 3. As shown by the silver spots in Figure 3d-i, measured 3D glucose concentration data are made by expanding the measured glucose concentration \(c_{n}\) into the 3D space; the three axes describe the measured glucose concentration \(c_{n}\). Utilizing the pdist2 (pairwise distance between two sets of samples) algorithm in MATLAB (MATLAB R2023b, The MathWorks, Inc., Natick, MA, USA), the similarity \(\alpha\) and the difference \(\beta\) between 3D multi-wavelength light data and glucose concentration data can be calculated as follows:  
\[\alpha=\frac{\sum_{i=1}^{n}d_{ii}}{n}, \tag{4}\]  
\[\beta=\frac{\sum_{i,j=1}^{n}d_{ij}}{n(n-1)}, \tag{5}\]  
where \(d_{i,j}\) and \(d_{i,j}\) donate the inner and outer term of standardized Euclidean distance, and \(n\) is the measured human glucose. In general, the smaller similarity \(\alpha\) and larger difference \(\beta\) typically indicate the better performance of 3D data.