## 5 Conclusions  
In conclusion, we propose a non-invasive and highly accurate multi-wavelength light NIR glucose sensor using an HMML-BP network based on the multi-wavelength light narrowband NIR sensing system. Our human glucose sensing method combines the advantages of both this system and an HMML-BP network. The latter is composed of multilevel metric learning modules and a BP network to remove complex sensingbackgrounds and achieve highly accurate blood glucose predictions. The highly accurate narrowband multi-wavelength light NIR sensing system consists of six-channel NIR filters with center wavelengths of 850 nm, 940 nm, 1300 nm, 1400 nm, 1550 nm, and 1650 nm, with a spectral resolution of less than 12 nm. The measured six-channel multi-wavelength light information is entered into multilevel metric learning modules to build 3D multi-wavelength light data; these are then optimized using SNE, diffusion maps, and factor analysis algorithms, before being further utilized as the BP network input to predict human glucose. The predicted results of the HMML-BP network optimized using the factor analysis algorithm show the best performance; all the predicted values fall within region A and the MARD is around 9.98%, meeting the requirements of daily human glucose monitoring. Our current system is still subject to many limitations in terms of hardware, data processing, and predicted results. To enhance portability for practical applications, the following issues should be addressed: (1) Hardware system: The system is limited by bulky halogen lamps and the experimental platform of the filter wheel. Therefore, it is crucial to reduce the size of these lamps in order to build a portable system. Future work could focus on designing wearable prototypes developed using low-cost and compact components such as NIR LEDs, photodiodes, and Bluetooth modules. (2) Data processing: To a certain extent, multilevel metric learning increases calculation complexity, so the real-time performance of blood glucose prediction still needs to be improved. (3) Predicted results: The system is in the theoretical verification stage, and the sample size is relatively small; in the future, human blood glucose measurements could be conducted in clinical settings in order to expand the dataset, enhance its diversity, and thus verify the system's long-term stability. Further work on non-uniform HMML-BP networks with different heterogeneous algorithms, including multilevel metric learning elements, could be pursued in order to improve predication accuracy and broaden the working range. Additional dynamic physiological variations, such as sweat-induced light-scattering changes and temperature-dependent blood flow dynamics, could also be investigated to explore more complex tissue properties and enhance prediction accuracy. In the future, more electric feedback modules could also be added to achieve automatic correction and further improve prediction accuracy. Moreover, miniature, portable, and highly accurate multi-wavelength light NIR glucose sensors could be applied to on-chip multi-wavelength light NIR systems using integrated, narrowband NIR LED-filter-photodiodes. The proposed non-invasive glucose sensing method allows for the convenient measuring and effective use of highly accurate multi-wavelength light NIR information to achieve non-invasive, precise blood glucose prediction, offering a new way of utilizing multispectral--or even hyperspectral--information in smart human glucose sensors. Our proposed method thus shows promise for application in non-invasive daily blood glucose monitoring. The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app15105652/s1](https://www.mdpi.com/article/10.3390/app15105652/s1)]([https://www.mdpi.com/article/10.3390/app15105652/s1](https://www.mdpi.com/article/10.3390/app15105652/s1)).