## 1 Introduction  
Kiwifruit (Actinidia deliciosa) has garnered widespread consumer appeal due to its distinct flavor and exceptional nutritional value [1]. Zhouzhi County, located in Xi'an City, Shaanxi Province, China, has earned global recognition as the premier habitat for kiwifruit cultivation and serves as the largest kiwifruit production hub [2]. Among the distinctive varieties cultivated in Zhouzhi County, Cuixiang kiwifruit stands as a prominent representative, which boasts a higher nutritional value of vitamin C compared to other kiwifruits. Sweetness content plays a pivotal role as a quality indicator for this variety, directly influencing the flavor and consumer preference [3, 4]. Traditional methods for assessing fruit sweetness content by crushing and juicing kiwifruit and measuring the juice by dripping it onto a sweetness tester is often time-consuming and destructive, failing to provide timely insights into the sweetness during the harvest season both for farmers and consumers. Consequently, there is a pressing need to explore rapid and non-destructive approaches for detecting kiwifruit sweetness levels. Visible and near-infrared spectroscopy (Vis/NIR) records detailed information on fruits with wide and continuous electromagnetic radiation in the form of reflectance and absorptions. The distinct and narrow absorption properties are strong information to identify the internal quality of fruits in a non-destructive and effective way [5]. Vis/NIR has progressively been employed for assessing the internal quality of various fruits, including apricot [6], apple [7; 8; 9], strawberry [10], citrus [11], etc. For kiwifruit, Vis/NIR was widely used to detect the physicochemical characteristics, including soluble solids content (SSC) [12; 13; 14], dry matter [15], pH [16], and firmness [17]. Additionally, Vis/NIR has been instrumental in tasks such as freshness identification of kiwifruit [18], evaluation of kiwifruit quality at different maturity stages [19], and discrimination of varietal flesh color [20]. In summary, Vis/NIR data has exhibited the potential to identify the internal quality of fruits in a non-destructive and fast way. However, the non-destructive detecting method of kiwifruit sweetness using Vis/NIR data is still lacking, and the accuracy of the sweetness estimation model needs to be improved. For the kiwifruit, solid samples often feature non-uniform surfaces, resulting in noise and scattering effects during the collection of hyperspectral signals, which can weaken the spectral signal and reduce the performance of quality prediction models [21]. To address the issue of light scattering in spectral data, a series of pre-processing steps, such as multiplicative scatter correction (MSC) and standard normal variable (SNV) were suggested to compensate for additive and/or multiplicative effects in spectral data [22; 23; 24]. The results consistently demonstrated improved model accuracy when working with pre-processed spectra compared to original spectra, with the most effective combination being the convolutional smoothing method coupled with MSC and SNV [25]. In addition, different feature selection methods were used to reduce the data volume and dimensionality in the hyperspectral reflectance to simplify the estimation models. Methods such as successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) were often deployed in the realm of fruit quality assessment [26]. [PERSON] et al. (2012) applied the SPA algorithm to downscale the spectral data of navel oranges [27]. The result indicated that the accuracy of the models built with the features after the SPA reached 100% for both calibration and prediction datasets. [PERSON] et al. (2014) found that using CARS algorithm to select the features from hyperspectral data could obtain the best results for predicting the SSC of Korla fragrant per [28], with R\({}^{2}\) and RMSE being 0.85 and 0.29, respectively.