# Non-Destructive Detection of Golden Passion Fruit Quality Based on Dielectric Characteristics
## 1 Introduction  
Passion fruit is a perennial climbing vine plant belonging to the Passifloraceae family and Passiflora genus. There are approximately 50-60 edible species of passion fruit found worldwide. They are highly cherished for their unique aroma and nutritional value [1, 2]. Among them, yellow passion fruit accounts for about 95% of the world's commercial Passiflora output [3, 4]. As a distinctive fruit in southern China, the yellow passion fruit industry has rapidly developed in recent years and holds considerable growth potential. However, passion fruit is primarily harvested manually, leading to issues like inconsistent quality and mixing of ripe and unripe fruit [5]. With the growing demand for passion fruit in both domestic and international markets [6], manual quality assessment hinders the standardized and large-scale development of passion fruit products, making it challengingto gain a competitive edge in the market. Therefore, establishing a non-destructive quality testing method for passion fruit is essential to meet the needs of the industry's development. Many scholars, both domestically and internationally, have conducted research on the quality assessment of passion fruit. The primary detection technologies include spectroscopy [7], the electronic nose [8], GC-MS [9,10], the Mechanical Property Test [5,11] and others. Most of these methods require destructive testing. However, [PERSON] et al. utilized a non-destructive method using visible and short-wave near-infrared spectroscopy through interacting and transmission measurements to evaluate the physicochemical quality of ripe passion fruit. It is important to note that passion fruit of different maturities exhibit significant variations in their physicochemical indicators, and those with lower maturity tend to have noticeably thicker peels. The peel thickness and internal seeds can significantly impact the efficiency of spectroscopic non-destructive testing methods. Compared to these techniques, non-destructive testing based on dielectric properties is more efficient and reliable and involves lower equipment costs. Fruit undergoes a series of changes during the ripening process, both at the macro- and microscopic levels. Macroscopically, these changes are observed in terms of hardness, soluble solids content, acidity, pulp percentage and color. From a microscopic perspective, there is a formation of a bioelectric field generated by numerous charged particles within the fruit. As substances undergo transformation during the growth and ripening process, the amount of charge carried by various chemical substances within the fruit's internal tissues, as well as the spatial distribution of charges, may also change. These microscopic changes in the electric field have an impact on the fruit's dielectric properties at the macroscopic level [12,13]. The study of the relationship between dielectric properties and the quality of fruit, as well as the exploration of methods for the non-destructive evaluation of fruit quality based on dielectric properties, has attracted widespread attention. Both domestic and international research on fruit and vegetables like bananas [14], kiwifruit [15], carrots [16], Korla pears [17,18] and mangoes [19] involved measuring their electrical properties at low frequencies to determine the physicochemical qualities. The current research methods have shifted from early statistical analysis of dielectric properties to establishing multivariate models based on dielectric properties combined with various algorithms. [PERSON] et al. [15] developed a model using an Artificial Neural Network with dielectric property features as input to predict the hardness, soluble solids content and pH value of kiwifruit, achieving R\({}^{2}\) values of 0.92, 0.91 and 0.86, respectively. [PERSON] et al. [17] used equivalent parallel capacitance, quality factor, loss factor, equivalent parallel resistance, complex impedance and equivalent parallel inductance as model inputs.