## 1 Introduction  
Near-infrared (NIR) spectroscopy is a powerful analytical technique that enables rapid and non-destructive measurement of the chemical and physical properties of samples based on their spectral data [1; 2; 3]. NIR spectroscopy works by shining light in the near-infrared range (typically 700-2500 nm) onto a sample and measuring the amount of light that is absorbed or reflected by the sample. The resulting spectrum provides information about the sample's molecular composition and physical properties, such as moisture content, protein content, fat content, and more. Its advantages over other analytical methods include easy sample preparation, environmental friendliness, and the ability to provide online or in situ measurements. These advantages make NIR spectroscopy a popular tool that has been widely applied in various fields, such as food [4; 5; 6], pharmaceuticals [7], and agriculture [8; 9; 10]. Fruit quality evaluation is one of the most important and challenging tasks for NIR spectroscopy, as fruits are complex biological systems that contain a diverse range of components, such as sugars, acids, vitamins, minerals, water content, and firmness [11; 12; 13]. The total amount of solid substances that can be dissolved in water, comprising sugars, acids, vitamins, minerals, and other components, is referred to as soluble solids content (SSC) [14; 15; 16]. Fruit quality is a crucially important factor that is indicated by the SSC through the process of photosynthesis, plants convert sunlight, carbon dioxide, and water into sugar substances such as glucose and fructose which are subsequently stored in the fruit. Consequently, the SSC of fruits is reflective of attributes such as ripeness, sweetness, texture, and overall quality. Monitoring and evaluating the quality attributes of fruits in a rapid and accurate way is essential for human health and nutrition. Chemometric methods, including multiple linear regression (MLR), principal component regression (PCR), partial least squares (PLS), artificial neural network (ANN), and support vector regression (SVR), as well as the techniques developed in recent years, are widely studied for multivariate calibration of NIR spectra [17; 18; 19]. However, the quality of a multivariate calibration model depends on a series of factors, e.g., the nature of the samples, the quality of the calibration data, the spectral variables used in the model, and even the technique of the modeling. Therefore, great efforts have been made for building a robust model, including studies on the methods for spectral preprocessing [20; 21], outlier detection [22], variable selection [23; 24] and the techniques of modeling [25]. With these methods, practical models with good predictability and reliability can be built in most applications. NIR spectroscopy provides a powerful tool for fruit non-destructive detection by measuring the spectra of fruit samples and applying multivariate calibration models to predict their quality parameters. Compared to conventional methods, NIR spectroscopy avoids sample destruction or contamination, reduces analysis time and cost, and covers a large sample area. However, one of the main problems with using NIR spectroscopy for fruit detection is the difficulty in generalizing and transferring models between different fruits [4; 26]. Each fruit type has to be modeled separately, as different fruits have different spectral characteristics due to their diverse compositions, structures, and shapes. Spectral variations caused by these factors may degrade the performance of calibration models or make them invalid for another kind of fruit. This requires a large amount of reference data collection and model establishment for each fruit type, which limits the practical application of NIR spectroscopy for fruit detection. To address this problem, researchers have commonly resorted to three strategies: spectral preprocessing [27], global modeling [28; 29], and calibration transfer without standard [30; 31].