## 1 Introduction  
Wine quality is intrinsically linked to the quality and geographical origin of the grapes utilized as the raw material, along with the success of post-harvest winemaking techniques. These factors need to be properly controlled to achieve the desired wine properties and quality standards. In particular, the search for the optimal grape berries' maturity stage is a permanent concern of producers who need to make better decisions regarding the best moment for harvesting, as well as to select grapes according to their quality features to accomplish the desired wine consistency and quality. This can be attained through monitoring the enological parameters, such as the sugar content, pH, and anthocyanin concentration. Usually, these enological parameters are assessed along with the grape maturation stage by conventional physical and chemical techniques, which have the disadvantage of being limited to a certain number of samples, as well as being destructive, time-consuming, and expensive. In order to overcome these disadvantages, there have been extensive research efforts for faster, non-destructive, and less expensive ways to assess the enological parameters, with hyperspectral imaging (HSI) emerging as a very promising alternative [1; 2; 3; 4]. This technology has the benefit of merging the features of both imaging and spectroscopy that, in the reflectance mode, allows for the collection of information about the intensity of the light reflected by grapes as a function of their wavelengths [5; 6]. However, the large amount of data generated by hyperspectral imaging poses significant challenges for data-driven modelling, requiring the use of suitable data analytic tools to properly deal with the complex spatial-wavelength structure and to extract the relevant information and the underlying patterns. In this context, supervised learning methods have been used to predict the value of a variety of output variables from the available predictors. However, the number of regression methods currently available is large, and selecting a suitable method is a cumbersome task with practitioners often relying on their preferred method and ignoring others that may present predictive advantages. In fact, most of the studies focusing on grape ripeness assessment (see Table 1) are still based on partial least squares regression (PLS) [7; 8; 9; 10; 11]. Nevertheless, some authors have also implemented support vector machines (SVM) [12] and artificial neural networks (ANN) [13; 14]. Thus, and given the plethora of methods available, the selection of the most suitable methods requires conducting extensive comparison studies, which represent an unbiased and effective approach to assess the performance of different regression methods in predicting the response variable of interest. As previously published works tend to focus on one, or a very limited set of predictive methods, overlooking entire classes of approaches, there is currently a gap of studies in the literature that compare, in a fair and unbiased way, a wide variety of methods over the same dataset in order to find the most adequate ones, also extracting insights from their combined analysis. Therefore, the present work reports the development and comparison of distinct linear and non-linear regression methods using hyperspectral imaging data collected in reflectance mode. The major novelty relies on putting together a rich variety of carefully chosen regression methods, arising from different classes of machine learning, statistical, and artificial intelligence domains, to predict sugar, pH, and anthocyanin contents in wine grape berries for the target dataset. To drive the comparison, an integrated linear and non-linear predictive analytics comparison framework (L&NL-PAC) was developed to assess the prediction performance of different classes of regression methods, covering the main classes of machine learning methods that are fully integrated with the most common spectral preprocessing approaches.