## 1 Introduction  
Apple is one of the most widely grown fruits around the world. Over the past decade, there has been a remarkable increase in apple production worldwide. Between 2010 and 2020, worldwide apple production grew from 71.19 million metric tons to 86.44 million metric tons [1]. This edible fruit is a good source of antioxidants such as ascorbic acid and polyphenols, which are known to improve human health. Apple is also high in vitamins, minerals, and sugars [2; 3; 4]. The fruit grade/quality is determined by various factors, including mass, pH, firmness, soluble solids content (SSC), color, moisture, and internal browning [5]. Most of these parameters are also critical in determining apple ripeness and/or optimal harvest times. Among them, moisture content (MC) is the most important quality attribute that directly affects the fruit's shelf life [6].  
Unfortunately, the conventional methods for evaluating the aforementioned quality traits are destructive, subjective, time-consuming, and/or prone to operational errors. Therefore, identifying intelligent and non-destructive alternative technologies for assessingapple quality is of great interest to the industry [7]. Over the past decade, hyperspectral imaging (HSI) has shown promising performance as a reliable tool for the safety and quality assessment of agricultural products [8; 9]. This non-destructive method combines the features of traditional imaging and spectroscopy to simultaneously analyze spatial and spectral information of a sample, making it an invaluable tool for assessing quality indicators of fruits, vegetables, legumes, oilseeds, and grains [7]. For example, scholars previously explored the capability of HSI in assessing the MC of various fruits such as strawberries [10], mango [11], tomatoes [12], potatoes [13], mushrooms [14], and persimmons [15]. Moreover, scientists implemented HSI for pH prediction of various fruits such as grapes [16], apples [17], peaches [18], kiwifruit [19], cherries [20] and strawberries [21].  
Despite several research studies on the use of HSI to monitor fruit quality, one of the main challenges in implementing this technology in the industry has been the enormity of the datasets and the cumbersome analysis that is required to glean useful information from them. One potential solution to overcome this challenge may be the implementation of state-of-the-art machine learning algorithms [22; 23; 24]. Over the past few years, machine learning algorithms have become increasingly popular in classification and prediction research, with techniques such as k-nearest neighbor (kNN), artificial neural networks (ANN), decision trees (DT), and convolutional neural networks (CNN) being widely employed [25; 26; 27; 28; 29; 30; 31; 32].  
The present work aims to assess apples' pH and MC variations at different maturity stages. Unlike the majority of hyperspectral data analysis models that only utilize partial least squares regression (PLSR) and/or multiple linear regression (MLR), herein, we explore the performance of three additional machine learning algorithms (i.e., kNN, DT, and ANN) in predicting the industry-accepted quality parameters to contribute to the ongoing effort in resolving data analysis challenge of hyperspectral data. Considering we previously demonstrated the capability of visible-near-infrared (VIS-NIR) HSI technology for apple firmness, SSC [33], ripening levels [34], phenolic content, antioxidant activity, and ascorbic acid prediction [4; 35], the present work contributes to revealing the full potential of VIS-NIR HSI in apple grading, maturity assessment, and shelf life estimation. Such a tool has the potential to enhance sustainable apple production through improved quality assessment, waste reduction, and increased energy efficiency in data processing.