## 1 Introduction  
The kiwifruit is a perennial fruit belonging to the _Actinidiaceae_ family, specifically the genus _Actinidia_. Usually, kiwifruit are harvested unripe and stored in controlled environment storage rooms for long periods of time before being marketed. As a climacteric fruit, it produces ethylene (\"ripening hormone\") during the postharvest ripening process [1]. Combined with pre-harvest factors, this contributes to the variation in fruit quality both at harvest and during chilled storage, resulting in the difficulty of sorting fruits according to their storage potential. To reduce losses along the agrifood supply chain, the optimal assessment of fruit ripeness is a crucial aspect in order to optimize the quality of the fruit for fresh consumption [2]. Traditionally, ripeness classification has relied on destructive methods, which require sampling and chemical and physical analysis that are limiting in terms of cost, time, and large-scale applicability [3]. However, it is important to emphasize that maturity requirements can vary significantly depending on the commercial destination of the product: for fresh consumption, fruits at the optimal stage of ripeness are preferred, with desirable organoleptic qualities such as aroma, sweetness, and texture, whereas for storage or long-distance transportation, it is often preferable to harvest slightly unripe fruits, which are more resistant to mechanical damage and spoilage, in order to preserve quality at the point of sale. Near-infrared visual spectroscopy (Vis-NIR) is one of the most commonly used non-destructive techniques to estimate the internal quality of fruits [4]. Spectroscopy is based on the bonding that governs the interaction between near-infrared (NIR) light and the vibrations of chemical bonds in organic molecules inside the products. Each chemical compound has distinctive absorption and reflection bands in the NIR spectrum [5]. Due to these basic principles, the measurement of the ripeness of orchards non-destructively has a solid basis from the past [6]. The use of portable NIR devices facilitates the quick collection of data on the chemical composition and structural characteristics of fruits, making this technology especially advantageous for field applications as well as along the supply chain [7]. Nowadays, several applications of NIR spectroscopy have been carried out on the evaluation of insight quality of fruits like melons [8], apricot [9], apple [10], citrus [11], strawberries [12], and also kiwifruit [1,4]. Soluble solids content (SSC) and flesh firmness (FF) are key parameters strongly correlated with final consumption quality, including sweetness and texture [13]. According to the literature, flesh firmness (FF) in kiwifruits must be equal to or greater than approximately 62 N, while the soluble solids content (SSC) should range from 6.2 to 6.5\({}^{\circ}\)Brix for fresh consumption and from 7 to 9\({}^{\circ}\)Brix for storage [14,15]. The integration of NIR spectroscopy (NIRS) sensors with machine learning (ML) models offers promising opportunities for classifying fruit ripening stages and predicting quality traits [4]. Traditional ML models, primarily based on linear relationships between instances and predictors in chemometric applications, have reached a significant milestone, consistently proving to be robust tools for predicting fruit quality parameters, as confirmed by several studies [10,11,12,16]. Partial Least Squares Regression (PLSR) remains the most widely used linear method for multivariate modeling due to its robustness and being easy to interpret [17]. On kiwifruit applications, [PERSON] et al. (2019) [13] achieved high predictive performance using PLS models for SSC (R\({}^{2}\) = 0.993, RMSEP = 0.40) and dry matter (DM) (R\({}^{2}\) = 0.983, RMSEP = 0.33), confirming the reliability of the prediction task. Similar results were reported by [PERSON] et al. (2022) [15], who applied PLS models to the 'Hayward' variety and achieved predictive R\({}^{2}\) values of up to 0.94 for SSC and 0.92 for FF.