## 1 Introduction  
Traditional human blood glucose sensors are commonly based on the electrochemical method, where samples have to be obtained through the finger-prick method, which is painful, expensive, and susceptible to shortcomings [1, 2, 3]. The demand for non-invasive, cheap, and user-friendly glucose monitors, which are critical in diabetes, is rapidly growing. People with diabetes need frequent blood glucose management [4, 5]. Advanced optical glucose sensors could take advantage of non-contact optical measurements, which have attracted great interest both in research and on the commercial market [6, 7]. There have been many reports of such optical method-based sensors, such as near-infrared (NIR) absorption [8, 9], mid-infrared (MIR) absorption [10], Raman shift [11, 12, 13], and NIR photoacousticsensors [14; 15], and so on. NIR glucose sensors employ the intrinsic NIR spectral absorption properties of glucose molecules and human tissue's NIR light penetration ability to sense human glucose. Many researchers have applied on-chip waveguides [16], micro-fibers [17], and hydrogel-coated layers [18] to fabricate NIR glucose sensors, which commonly need complex, slower, and expensive experimental methods, limiting the application of low-cost blood glucose sensing in practical, commercial fields. Taking advantage of low-cost NIR LED/LD sources and Si/Ge/InGaAs photodiodes, NIR glucose sensors are convenient to use, providing a potential application method for emerging non-invasive human glucose monitors [19]. However, the intrinsic NIR absorption spectra of the glucose molecule overlap severely with other human biomaterials such as water, fat, protein, and so on, leading to complex, noisy backgrounds for non-invasive blood glucose monitors [20; 21]. As a result, removing these backgrounds and improving glucose sensing accuracy are crucial for developing non-invasive daily NIR human glucose monitors. Many groups have analyzed the NIR absorption properties of glucose in human tissue [22] and utilized advanced artificial intelligence (AI) algorithms to remove complex noising backgrounds [23; 24], achieving non-invasive glucose measurement through AI algorithms and electronic control systems [25; 26; 27]. [PERSON] et al. reported daily blood glucose detection based on the infrared pulsed sensing (IPS) method using NIR LED and a deep hybrid feature neural network to predict blood glucose concentrations [28]. [PERSON] et al. reported the use of multi-modal spectroscopy where three NIR channels of 850 nm, 950 nm, and 1300 nm were input into the artificial neural network (ANN) to sense the glucose concentration [29]. [PERSON] and coworkers proposed a dual (940 nm and 1300 nm) short-wavelength NIR-wave-based detection system that was validated on 200 individual subjects [30]. [PERSON] et al. reported on a blood glucose detection system using a 1550 nm NIR absorbance spectrum and particle swarm optimization (PSO) with two ANNs [31]. [PERSON] et al. proposed a wearable, non-invasive consumer glucose device that applied 940 nm and 1300 nm NIR lights, which combined with the deep neural network regression algorithm to sense glucose levels [32]. [PERSON] et al. designed a non-invasive blood glucose spectroscopy method that consisted of 1070 nm, 950 nm, and 935 nm NIR LEDs and combined this with an ANN [33]. [PERSON] et al. proposed a single 940 nm wavelength glucose sensor using different machine learning algorithms that were trained using extracted features and reference glucose values [34]. [PERSON] and coworkers proposed a non-invasive blood glucose sensor and applied dual-channel photoplethysmography (PPG) with values of 530 nm and 1550 nm LED and pulse arrival velocity (PAV) [35]. [PERSON] et al. reported on a nonlinear auto-regressive method with exogenous (NARX) input using seven parameters, including 1550 nm NIR absorbance information, ambient temperature, ambient humidity, systolic pressure, diastolic pressure, pulse rate, and body temperature, to predict human glucose levels [36].