## 1 Introduction  
Peach is a popular fruit appreciated by consumers due to its eating quality. It is rich in a variety of vitamins and minerals, including carbohydrates, organic acids, pigments, phenolics, vitamins, volatiles, antioxidants, and small amounts of proteins and lipids [1, 2]. Quality evaluation of peaches is important for processing, inventory control, and marketing. Physical and chemical quality detection methods (detection of firmness, soluble solids concentration, titatable acidity, etc.) accurately determine the quality of the fruit [3; 4]. However, these detection methods are time-consuming and require special experimental equipment and conditions, making them impractical during actual production [3]. In many cases, practical peach quality evaluation consists of appearance screening by operators, which is influenced by subjective factors and has a low efficiency and large errors [5]. As labor costs increase, labor-intensive evaluation and grading constitutes a major expense for fresh and processed peach postharvest management [6]. Therefore, they are gradually being replaced by automated evaluation systems based on machine vision, image processing technology, and other emerging detection technologies. These approaches, which include measurement of dielectric properties and hyperspectral imaging, have the potential to improve processing efficiency, reduce costs, and minimize waste [7; 8]. For example, [PERSON] et al. [9] designed a 13-layer convolutional neural network (CNN) for fruit category identification with three types of data augmentation. [PERSON] et al. [10] studied banana quality and maturity using hyperspectral imaging in the visible and near-infrared (400-1000 nm) regions, and [PERSON] et al. [11] developed a real-time pixel-based early apple bruise detection system based on hyperspectral imaging (HIS) in the shortwave infrared (SWIR) range. However, the appearance of fruit is often affected by ripening agents and experimental conditions (e.g., simple glossiness, image background), and quality differences cannot be fully captured by imaging [11; 12]. [PERSON] et al. [13] proposed a rapid and non-destructive method for investigating the correlation between the dielectric constant and quality parameters of banana fruit. [PERSON] et al. [14] investigated changes in the dielectric properties of Fuji apples with red-dot disease that were stored at constant temperature, and [PERSON] et al. [15] reported that 13 dielectric properties of peaches showed regular changes with increasing frequency. However, further research and more cases are required to apply emerging detection technologies to quality evaluation of fresh fruit. It is difficult to accurately assess the quality of an entire batch of fruit based on single evaluation indicators and limited samples; such assessments are affected by the experimental environment and the characteristics of the individual fruit sampled [16; 17]. Fruit quality evaluation methods combined with multiple detection technologies, therefore, receive significant attention [18; 19]. For example, [PERSON] et al. [20] described a platform for evaluation of honey quality based on electrical impedance spectroscopy (EIS) and Fourier-transform mid-infrared spectroscopy (FT-MIR), which was used to detect the presence of sucrose as an adulterant in honey varieties from different floral origins. [PERSON] et al. [21] used an electronic nose based on ultrafast gas chromatography and gas chromatography with mass spectrometry to analyze the quality of three citrus fruits. Currently, fruit quality assessment still requires more convenient and effective evaluation methods [22]. In this work, a rapid and simple measurement of electrical properties was used to predict related differences in the quality of fresh peaches. In addition, principal component analysis (PCA) was used to develop a comprehensive method and model for effectively evaluating and grading peach quality. This model can be used to guide consumers' choices when buying fresh peaches.